From 4edfa2f4bde7626bc42cbb138024d8ec07af5d6d Mon Sep 17 00:00:00 2001
From: DavisVaughan <davis@posit.co>
Date: Thu, 15 Aug 2024 21:53:48 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20tidyvers?=
 =?UTF-8?q?e/tidyr@9966c049541a708605b9c2d109251f4b14355c00=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dev/news/index.html | 3 ++-
 dev/pkgdown.yml     | 2 +-
 dev/search.json     | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/dev/news/index.html b/dev/news/index.html
index f30a3cc8..43178910 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -59,7 +59,8 @@
 
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="development version" id="tidyr-development-version">tidyr (development version)<a class="anchor" aria-label="anchor" href="#tidyr-development-version"></a></h2>
-<ul><li>tidyr now requires dplyr &gt;=1.1.0 (<a href="https://github.com/tidyverse/tidyr/issues/1568" class="external-link">#1568</a>, <a href="https://github.com/catalamarti" class="external-link">@catalamarti</a>).</li>
+<ul><li><p><code><a href="../reference/pivot_wider_spec.html">pivot_wider_spec()</a></code> now throws a more informative error on non-data frame inputs (<a href="https://github.com/catalamarti" class="external-link">@catalamarti</a>, <a href="https://github.com/tidyverse/tidyr/issues/1510" class="external-link">#1510</a>).</p></li>
+<li><p>tidyr now requires dplyr &gt;=1.1.0 (<a href="https://github.com/tidyverse/tidyr/issues/1568" class="external-link">#1568</a>, <a href="https://github.com/catalamarti" class="external-link">@catalamarti</a>).</p></li>
 </ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="1.3.1" id="tidyr-131">tidyr 1.3.1<a class="anchor" aria-label="anchor" href="#tidyr-131"></a></h2><p class="text-muted">CRAN release: 2024-01-24</p>
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index 80e95842..5560fc69 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   programming: programming.html
   rectangle: rectangle.html
   tidy-data: tidy-data.html
-last_built: 2024-08-15T21:19Z
+last_built: 2024-08-15T21:52Z
 urls:
   reference: https://tidyr.tidyverse.org/reference
   article: https://tidyr.tidyverse.org/articles
diff --git a/dev/search.json b/dev/search.json
index 5850b1f3..7ffa206b 100644
--- a/dev/search.json
+++ b/dev/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement codeofconduct@posit.co. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tidyr","title":"Contributing to tidyr","text":"outlines propose change tidyr. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to tidyr","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. YES: edit roxygen comment .R file R/. : edit .Rd file man/.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"prerequisites","dir":"","previous_headings":"","what":"Prerequisites","title":"Contributing to tidyr","text":"make substantial pull request, always file issue make sure someone team agrees ’s problem. ’ve found bug, create associated issue illustrate bug minimal reprex.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"","what":"Pull request process","title":"Contributing to tidyr","text":"recommend create Git branch pull request (PR). Look Travis AppVeyor build status making changes. README contain badges continuous integration services used package. New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat. Contributions test cases included easier accept. user-facing changes, add bullet top NEWS.md current development version header describing changes made followed GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to tidyr","text":"Please note tidyr project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"see-tidyverse-development-contributing-guide","dir":"","previous_headings":"","what":"See tidyverse development contributing guide","title":"Contributing to tidyr","text":"details.","code":""},{"path":"https://tidyr.tidyverse.org/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 tidyr authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tidyr.tidyverse.org/dev/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with tidyr","title":"Getting help with tidyr","text":"Thanks using tidyr. filing issue, places explore pieces put together make process smooth possible. Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! section tidyverse site. Armed reprex, next step figure ask. ’s question: start forum.posit.co, /StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed. right place, need file issue, please review “File issues” paragraph tidyverse contributing guidelines. Thanks help!","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"In packages","text":"vignette serves two distinct, related, purposes: documents general best practices using tidyr package, inspired using ggplot2 packages. describes migration patterns transition tidyr v0.8.3 v1.0.0. release includes breaking changes nest() unnest() order increase consistency within tidyr rest tidyverse. go , ’ll attach packages use, expose version tidyr, make small dataset use examples.","code":"library(tidyr) library(dplyr, warn.conflicts = FALSE) library(purrr)  packageVersion(\"tidyr\") #> [1] '1.3.1.9000'  mini_iris <- as_tibble(iris)[c(1, 2, 51, 52, 101, 102), ] mini_iris #> # A tibble: 6 × 5 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <fct>      #> 1          5.1         3.5          1.4         0.2 setosa     #> 2          4.9         3            1.4         0.2 setosa     #> 3          7           3.2          4.7         1.4 versicolor #> 4          6.4         3.2          4.5         1.5 versicolor #> 5          6.3         3.3          6           2.5 virginica  #> 6          5.8         2.7          5.1         1.9 virginica"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"using-tidyr-in-packages","dir":"Articles","previous_headings":"","what":"Using tidyr in packages","title":"In packages","text":"assume ’re already familiar using tidyr functions, described vignette(\"programming.Rmd\"). two important considerations using tidyr package: avoid R CMD CHECK notes using fixed variable names. alert upcoming changes development version tidyr.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"fixed-column-names","dir":"Articles","previous_headings":"Using tidyr in packages","what":"Fixed column names","title":"In packages","text":"know column names, code works way regardless whether inside outside package: R CMD check warn undefined global variables (Petal.Length, Petal.Width, Sepal.Length, Sepal.Width), doesn’t know nest() looking variables inside mini_iris (.e. Petal.Length friends data-variables, env-variables). easiest way silence note use all_of(). all_of() tidyselect helper (like starts_with(), ends_with(), etc.) takes column names stored strings: Alternatively, may want use any_of() OK specified variables found input data. tidyselect package offers entire family select helpers. probably already familiar using dplyr::select().","code":"mini_iris %>% nest(   petal = c(Petal.Length, Petal.Width),   sepal = c(Sepal.Length, Sepal.Width) ) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]> mini_iris %>% nest(   petal = all_of(c(\"Petal.Length\", \"Petal.Width\")),   sepal = all_of(c(\"Sepal.Length\", \"Sepal.Width\")) ) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"continuous-integration","dir":"Articles","previous_headings":"Using tidyr in packages","what":"Continuous integration","title":"In packages","text":"Hopefully ’ve already adopted continuous integration package, R CMD check (includes tests) run regular basis, e.g. every time push changes package’s source GitHub similar. tidyverse team currently relies heavily GitHub Actions, example. usethis::use_github_action() can help get started. recommend adding workflow targets devel version tidyr. ? Always? package tightly coupled tidyr, consider leaving place time, know changes tidyr affect package. Right tidyr release? everyone else, add (re-activate existing) tidyr-devel workflow period preceding major tidyr release potential breaking changes, especially ’ve contacted reverse dependency checks. Example GitHub Actions workflow tests package development version tidyr: GitHub Actions evolving landscape, can always mine workflows tidyr (tidyverse/tidyr/.github/workflows) main r-lib/actions repo ideas.","code":"on:   push:     branches:       - main   pull_request:     branches:       - main  name: R-CMD-check-tidyr-devel  jobs:   R-CMD-check:     runs-on: macOS-latest     steps:       - uses: actions/checkout@v4       - uses: r-lib/actions/setup-r@v2       - name: Install dependencies         run: |           install.packages(c(\"remotes\", \"rcmdcheck\"))           remotes::install_deps(dependencies = TRUE)           remotes::install_github(\"tidyverse/tidyr\")         shell: Rscript {0}       - name: Check         run: rcmdcheck::rcmdcheck(args = \"--no-manual\", error_on = \"error\")         shell: Rscript {0}"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"tidyr-v0-8-3---v1-0-0","dir":"Articles","previous_headings":"","what":"tidyr v0.8.3 -> v1.0.0","title":"In packages","text":"v1.0.0 makes considerable changes interface nest() unnest() order bring line newer tidyverse conventions. tried make functions backward compatible possible give informative warning messages, cover 100% use cases, may need change package code. guide help minimum pain. Ideally, ’ll tweak package works tidyr 0.8.3 tidyr 1.0.0. makes life considerably easier means ’s need coordinate CRAN submissions - can submit package works tidyr versions, submit tidyr CRAN. section describes recommend practices , drawing general principles described https://design.tidyverse.org/changes-multivers.html. use continuous integration already, strongly recommend adding build tests development version tidyr; see details. section briefly describes run different code different versions tidyr, goes major changes might require workarounds: nest() unnest() get new interfaces. nest() preserves groups. nest_() unnest_() defunct. ’re struggling problem ’s described , please reach via github email can help .","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"conditional-code","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"Conditional code","title":"In packages","text":"Sometimes ’ll able write code works v0.8.3 v1.0.0. often requires code ’s particularly natural either version ’d better (temporarily) separate code paths, containing non-contrived code. get re-use existing code “old” branch, eventually phased , write clean, forward-looking code “new” branch. basic approach looks like . First define function returns TRUE new versions tidyr: highly recommend keeping function provides obvious place jot transition notes package, makes easier remove transitional code later . Another benefit tidyr version determined run time, build time, therefore detect user’s current tidyr version. functions, use statement call different code different versions: new code uses function exists tidyr 1.0.0, get NOTE R CMD check: one notes can explain CRAN submission comments. Just mention ’s forward compatibility tidyr 1.0.0, CRAN let package .","code":"tidyr_new_interface <- function() {   packageVersion(\"tidyr\") > \"0.8.99\" } my_function_inside_a_package <- function(...)   # my code here    if (tidyr_new_interface()) {     # Freshly written code for v1.0.0     out <- tidyr::nest(df, data = any_of(c(\"x\", \"y\", \"z\")))   } else {     # Existing code for v0.8.3     out <- tidyr::nest(df, x, y, z)   }    # more code here }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"new-syntax-for-nest","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"New syntax for nest()","title":"In packages","text":"changed: --nested columns longer accepted “loose parts”. new list-column’s name longer provided via .key argument. Now use construct like : new_col = <something existing cols>. changed: use ... metadata problematic pattern ’re moving away . https://design.tidyverse.org/dots-data.html new_col = <something existing cols> construct lets us create multiple nested list-columns (“multi-nest”). examples: need quick dirty fix without think, just call nest_legacy() instead nest(). ’s nest() v0.8.3:","code":"mini_iris %>%   nest(petal = matches(\"Petal\"), sepal = matches(\"Sepal\")) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]> # v0.8.3 mini_iris %>%   nest(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, .key = \"my_data\")  # v1.0.0 mini_iris %>%   nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))  # v1.0.0 avoiding R CMD check NOTE mini_iris %>%   nest(my_data = any_of(c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")))  # or equivalently: mini_iris %>%   nest(my_data = !any_of(\"Species\")) if (tidyr_new_interface()) {   out <- tidyr::nest_legacy(df, x, y, z) } else {   out <- tidyr::nest(df, x, y, z) }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"new-syntax-for-unnest","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"New syntax for unnest()","title":"In packages","text":"changed: --unnested columns must now specified explicitly, instead defaulting list-columns. also deprecates .drop .preserve. .sep deprecated replaced names_sep. unnest() uses emerging tidyverse standard disambiguate duplicated names. Use names_repair = tidyr_legacy request previous approach. .id deprecated can easily replaced creating column names prior unnest(), e.g. upstream call mutate(). changed: use ... metadata problematic pattern ’re moving away . https://design.tidyverse.org/dots-data.html changes details arguments relate features rolling across multiple packages tidyverse. example, ptype exposes prototype support new vctrs package. names_repair specifies duplicated non-syntactic names, consistent tibble readxl. : need quick dirty fix without think, just call unnest_legacy() instead unnest(). ’s unnest() v0.8.3:","code":"# v0.8.3 df %>% unnest(x, .id = \"id\")  # v1.0.0 df %>% mutate(id = names(x)) %>% unnest(x)) nested <- mini_iris %>%   nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))  # v0.8.3 automatically unnests list-cols nested %>% unnest()  # v1.0.0 must be told which columns to unnest nested %>% unnest(any_of(\"my_data\")) if (tidyr_new_interface()) {   out <- tidyr::unnest_legacy(df) } else {   out <- tidyr::unnest(df) }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"nest-preserves-groups","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"nest() preserves groups","title":"In packages","text":"changed: nest() now preserves groups present input. changed: reflect growing support grouped data frames, especially recent releases dplyr. See, example, dplyr::group_modify(), group_map(), friends. fact nest() now preserves groups problematic downstream, choices: Apply ungroup() result. level pragmatism suggests, however, least consider next two options. never grouped first place. Eliminate group_by() call specify columns nested versus nested directly nest(). Adjust downstream code accommodate grouping. Imagine used group_by() nest() mini_iris, computed list-column outside data frame. now try add back data post hoc: fails df grouped mutate() group-aware, ’s hard add completely external variable. pragmatically ungroup()ing, can ? One option work inside data frame, .e. bring map() inside mutate(), design problem away: , somehow, grouping seems appropriate working inside data frame option, tibble::add_column() group-unaware. lets add external data grouped data frame.","code":"(df <- mini_iris %>%    group_by(Species) %>%    nest()) #> # A tibble: 3 × 2 #> # Groups:   Species [3] #>   Species    data             #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> (external_variable <- map_int(df$data, nrow)) #> [1] 2 2 2 df %>%   mutate(n_rows = external_variable) #> Error in `mutate()`: #> ℹ In argument: `n_rows = external_variable`. #> ℹ In group 1: `Species = setosa`. #> Caused by error: #> ! `n_rows` must be size 1, not 3. df %>%   mutate(n_rows = map_int(data, nrow)) #> # A tibble: 3 × 3 #> # Groups:   Species [3] #>   Species    data             n_rows #>   <fct>      <list>            <int> #> 1 setosa     <tibble [2 × 4]>      2 #> 2 versicolor <tibble [2 × 4]>      2 #> 3 virginica  <tibble [2 × 4]>      2 df %>%   tibble::add_column(n_rows = external_variable) #> # A tibble: 3 × 3 #> # Groups:   Species [3] #>   Species    data             n_rows #>   <fct>      <list>            <int> #> 1 setosa     <tibble [2 × 4]>      2 #> 2 versicolor <tibble [2 × 4]>      2 #> 3 virginica  <tibble [2 × 4]>      2"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"nest_-and-unnest_-are-defunct","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"nest_() and unnest_() are defunct","title":"In packages","text":"changed: nest_() unnest_() longer work changed: Specialized standard evaluation versions functions, e.g., foo_() complement foo(). older lazyeval framework. :","code":"# v0.8.3 mini_iris %>%   nest_(     key_col = \"my_data\",     nest_cols = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")   )  nested %>% unnest_(~ my_data)  # v1.0.0 mini_iris %>%   nest(my_data = any_of(c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")))  nested %>% unnest(any_of(\"my_data\"))"},{"path":"https://tidyr.tidyverse.org/dev/articles/nest.html","id":"basics","dir":"Articles","previous_headings":"","what":"Basics","title":"Nested data","text":"nested data frame data frame one () columns list data frames. can create simple nested data frames hand: (possible create list-columns regular data frames, just tibbles, ’s considerably work default behaviour data.frame() treat lists lists columns.) commonly ’ll create tidyr::nest(): nest() specifies variables nested inside; alternative use dplyr::group_by() describe variables kept outside. think nesting easiest understand connection grouped data: row output corresponds one group input. ’ll see shortly particularly convenient per-group objects. opposite nest() unnest(). give name list-column containing data frames, row-binds data frames together, repeating outer columns right number times line .","code":"df1 <- tibble(   g = c(1, 2, 3),   data = list(     tibble(x = 1, y = 2),     tibble(x = 4:5, y = 6:7),     tibble(x = 10)   ) )  df1 #> # A tibble: 3 × 2 #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 1]> df2 <- tribble(   ~g, ~x, ~y,    1,  1,  2,    2,  4,  6,    2,  5,  7,    3, 10,  NA ) df2 %>% nest(data = c(x, y)) #> # A tibble: 3 × 2 #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]> df2 %>% group_by(g) %>% nest() #> # A tibble: 3 × 2 #> # Groups:   g [3] #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]> df1 %>% unnest(data) #> # A tibble: 4 × 3 #>       g     x     y #>   <dbl> <dbl> <dbl> #> 1     1     1     2 #> 2     2     4     6 #> 3     2     5     7 #> 4     3    10    NA"},{"path":"https://tidyr.tidyverse.org/dev/articles/nest.html","id":"nested-data-and-models","dir":"Articles","previous_headings":"","what":"Nested data and models","title":"Nested data","text":"Nested data great fit problems one something group. common place arises ’re fitting multiple models. list data frames, ’s natural produce list models: even produce list predictions: workflow works particularly well conjunction broom, makes easy turn models tidy data frames can unnest()ed get back flat data frames. can see bigger example broom dplyr vignette.","code":"mtcars_nested <- mtcars %>%   group_by(cyl) %>%   nest()  mtcars_nested #> # A tibble: 3 × 2 #> # Groups:   cyl [3] #>     cyl data               #>   <dbl> <list>             #> 1     6 <tibble [7 × 10]>  #> 2     4 <tibble [11 × 10]> #> 3     8 <tibble [14 × 10]> mtcars_nested <- mtcars_nested %>%   mutate(model = map(data, function(df) lm(mpg ~ wt, data = df))) mtcars_nested #> # A tibble: 3 × 3 #> # Groups:   cyl [3] #>     cyl data               model  #>   <dbl> <list>             <list> #> 1     6 <tibble [7 × 10]>  <lm>   #> 2     4 <tibble [11 × 10]> <lm>   #> 3     8 <tibble [14 × 10]> <lm> mtcars_nested <- mtcars_nested %>%   mutate(model = map(model, predict)) mtcars_nested #> # A tibble: 3 × 3 #> # Groups:   cyl [3] #>     cyl data               model      #>   <dbl> <list>             <list>     #> 1     6 <tibble [7 × 10]>  <dbl [7]>  #> 2     4 <tibble [11 × 10]> <dbl [11]> #> 3     8 <tibble [14 × 10]> <dbl [14]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Pivoting","text":"vignette describes use new pivot_longer() pivot_wider() functions. goal improve usability gather() spread(), incorporate state---art features found packages. time, ’s obvious something fundamentally wrong design spread() gather(). Many people don’t find names intuitive find hard remember direction corresponds spreading gathering. also seems surprisingly hard remember arguments functions, meaning many people (including !) consult documentation every time. two important new features inspired R packages advancing reshaping R: pivot_longer() can work multiple value variables may different types, inspired enhanced melt() dcast() functions provided data.table package Matt Dowle Arun Srinivasan. pivot_longer() pivot_wider() can take data frame specifies precisely metadata stored column names becomes data variables (vice versa), inspired cdata package John Mount Nina Zumel. vignette, ’ll learn key ideas behind pivot_longer() pivot_wider() see used solve variety data reshaping challenges ranging simple complex. begin ’ll load needed packages. real analysis code, ’d imagine ’d library(tidyverse), can’t since vignette embedded package.","code":"library(tidyr) library(dplyr) library(readr)"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer","dir":"Articles","previous_headings":"","what":"Longer","title":"Pivoting","text":"pivot_longer() makes datasets longer increasing number rows decreasing number columns. don’t believe makes sense describe dataset “long form”. Length relative term, can say (e.g.) dataset longer dataset B. pivot_longer() commonly needed tidy wild-caught datasets often optimise ease data entry ease comparison rather ease analysis. following sections show use pivot_longer() wide range realistic datasets.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"pew","dir":"Articles","previous_headings":"Longer","what":"String data in column names","title":"Pivoting","text":"relig_income dataset stores counts based survey (among things) asked people religion annual income: dataset contains three variables: religion, stored rows, income spread across column names, count stored cell values. tidy use pivot_longer(): first argument dataset reshape, relig_income. cols describes columns need reshaped. case, ’s every column apart religion. names_to gives name variable created data stored column names, .e. income. values_to gives name variable created data stored cell value, .e. count. Neither names_to values_to column exists relig_income, provide strings surrounded quotes.","code":"relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> # ℹ 8 more rows #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(     cols = !religion,     names_to = \"income\",     values_to = \"count\"   ) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"billboard","dir":"Articles","previous_headings":"Longer","what":"Numeric data in column names","title":"Pivoting","text":"billboard dataset records billboard rank songs year 2000. form similar relig_income data, data encoded column names really number, string. can start basic specification relig_income dataset. want names become variable called week, values become variable called rank. also use values_drop_na drop rows correspond missing values. every song stays charts 76 weeks, structure input data force creation unnecessary explicit NAs. nice easily determine long song stayed charts, , ’ll need convert week variable integer. can using two additional arguments: names_prefix strips wk prefix, names_transform converts week integer: Alternatively, single argument using readr::parse_number() automatically strips non-numeric components:","code":"billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 #> # ℹ 5,297 more rows billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_prefix = \"wk\",     names_transform = as.integer,     values_to = \"rank\",     values_drop_na = TRUE,   ) billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_transform = readr::parse_number,     values_to = \"rank\",     values_drop_na = TRUE,   )"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"many-variables-in-column-names","dir":"Articles","previous_headings":"Longer","what":"Many variables in column names","title":"Pivoting","text":"challenging situation occurs multiple variables crammed column names. example, take dataset: country, iso2, iso3, year already variables, can left . columns new_sp_m014 newrel_f65 encode four variables names: new_/new prefix indicates counts new cases. dataset contains new cases, ’ll ignore ’s constant. sp/rel/ep describe case diagnosed. m/f gives gender. 014/1524/2535/3544/4554/65 supplies age range. can break variables specifying multiple column names names_to, either providing names_sep names_pattern. names_pattern natural fit. similar interface extract: give regular expression containing groups (defined ()) puts group column. go one step use readr functions convert gender age factors. think good practice categorical variables known set values. way little efficient mutate fact, pivot_longer() transform one occurence name mutate() need transform many repetitions.","code":"who #> # A tibble: 7,240 × 60 #>    country     iso2  iso3   year new_sp_m014 new_sp_m1524 new_sp_m2534 #>    <chr>       <chr> <chr> <dbl>       <dbl>        <dbl>        <dbl> #>  1 Afghanistan AF    AFG    1980          NA           NA           NA #>  2 Afghanistan AF    AFG    1981          NA           NA           NA #>  3 Afghanistan AF    AFG    1982          NA           NA           NA #>  4 Afghanistan AF    AFG    1983          NA           NA           NA #>  5 Afghanistan AF    AFG    1984          NA           NA           NA #>  6 Afghanistan AF    AFG    1985          NA           NA           NA #>  7 Afghanistan AF    AFG    1986          NA           NA           NA #>  8 Afghanistan AF    AFG    1987          NA           NA           NA #>  9 Afghanistan AF    AFG    1988          NA           NA           NA #> 10 Afghanistan AF    AFG    1989          NA           NA           NA #> # ℹ 7,230 more rows #> # ℹ 53 more variables: new_sp_m3544 <dbl>, new_sp_m4554 <dbl>, #> #   new_sp_m5564 <dbl>, new_sp_m65 <dbl>, new_sp_f014 <dbl>, #> #   new_sp_f1524 <dbl>, new_sp_f2534 <dbl>, new_sp_f3544 <dbl>, #> #   new_sp_f4554 <dbl>, new_sp_f5564 <dbl>, new_sp_f65 <dbl>, #> #   new_sn_m014 <dbl>, new_sn_m1524 <dbl>, new_sn_m2534 <dbl>, #> #   new_sn_m3544 <dbl>, new_sn_m4554 <dbl>, new_sn_m5564 <dbl>, … who %>%   pivot_longer(     cols = new_sp_m014:newrel_f65,     names_to = c(\"diagnosis\", \"gender\", \"age\"),     names_pattern = \"new_?(.*)_(.)(.*)\",     values_to = \"count\"   ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows who %>%   pivot_longer(     cols = new_sp_m014:newrel_f65,     names_to = c(\"diagnosis\", \"gender\", \"age\"),     names_pattern = \"new_?(.*)_(.)(.*)\",     names_transform = list(       gender = ~ readr::parse_factor(.x, levels = c(\"f\", \"m\")),       age = ~ readr::parse_factor(         .x,         levels = c(\"014\", \"1524\", \"2534\", \"3544\", \"4554\", \"5564\", \"65\"),         ordered = TRUE       )     ),     values_to = \"count\", )"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"multiple-observations-per-row","dir":"Articles","previous_headings":"Longer","what":"Multiple observations per row","title":"Pivoting","text":"far, working data frames one observation per row, many important pivoting problems involve multiple observations per row. can usually recognise case name column want appear output part column name input. section, ’ll learn pivot sort data. following example adapted data.table vignette, inspiration tidyr’s solution problem. Note two pieces information (values) child: name dob (date birth). need go separate columns result. supply multiple variables names_to, using names_sep split variable name. Note special name .value: tells pivot_longer() part column name specifies “value” measured (become variable output). Note use values_drop_na = TRUE: input shape forces creation explicit missing variables observations don’t exist. similar problem problem also exists anscombe dataset built base R: dataset contains four pairs variables (x1 y1, x2 y2, etc) underlie Anscombe’s quartet, collection four datasets summary statistics (mean, sd, correlation etc), quite different data. want produce dataset columns set, x y. Setting cols_vary \"slowest\" groups values columns x1 y1 together rows output moving x2 y2. argument often produces intuitively ordered output pivoting every column dataset. similar situation can arise panel data. example, take example dataset provided Thomas Leeper. can tidy using approach anscombe:","code":"household #> # A tibble: 5 × 5 #>   family dob_child1 dob_child2 name_child1 name_child2 #>    <int> <date>     <date>     <chr>       <chr>       #> 1      1 1998-11-26 2000-01-29 Susan       Jose        #> 2      2 1996-06-22 NA         Mark        NA          #> 3      3 2002-07-11 2004-04-05 Sam         Seth        #> 4      4 2004-10-10 2009-08-27 Craig       Khai        #> 5      5 2000-12-05 2005-02-28 Parker      Gracie household %>%   pivot_longer(     cols = !family,     names_to = c(\".value\", \"child\"),     names_sep = \"_\",     values_drop_na = TRUE   ) #> # A tibble: 9 × 4 #>   family child  dob        name   #>    <int> <chr>  <date>     <chr>  #> 1      1 child1 1998-11-26 Susan  #> 2      1 child2 2000-01-29 Jose   #> 3      2 child1 1996-06-22 Mark   #> 4      3 child1 2002-07-11 Sam    #> 5      3 child2 2004-04-05 Seth   #> 6      4 child1 2004-10-10 Craig  #> 7      4 child2 2009-08-27 Khai   #> 8      5 child1 2000-12-05 Parker #> 9      5 child2 2005-02-28 Gracie anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89 anscombe %>%   pivot_longer(     cols = everything(),     cols_vary = \"slowest\",     names_to = c(\".value\", \"set\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 44 × 3 #>    set       x     y #>    <chr> <dbl> <dbl> #>  1 1        10  8.04 #>  2 1         8  6.95 #>  3 1        13  7.58 #>  4 1         9  8.81 #>  5 1        11  8.33 #>  6 1        14  9.96 #>  7 1         6  7.24 #>  8 1         4  4.26 #>  9 1        12 10.8  #> 10 1         7  4.82 #> # ℹ 34 more rows pnl <- tibble(   x = 1:4,   a = c(1, 1,0, 0),   b = c(0, 1, 1, 1),   y1 = rnorm(4),   y2 = rnorm(4),   z1 = rep(3, 4),   z2 = rep(-2, 4), )  pnl %>%   pivot_longer(     cols = !c(x, a, b),     names_to = c(\".value\", \"time\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 8 × 6 #>       x     a     b time         y     z #>   <int> <dbl> <dbl> <chr>    <dbl> <dbl> #> 1     1     1     0 1     -1.40        3 #> 2     1     1     0 2      0.622      -2 #> 3     2     1     1 1      0.255       3 #> 4     2     1     1 2      1.15       -2 #> 5     3     0     1 1     -2.44        3 #> 6     3     0     1 2     -1.82       -2 #> 7     4     0     1 1     -0.00557     3 #> 8     4     0     1 2     -0.247      -2"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"wider","dir":"Articles","previous_headings":"","what":"Wider","title":"Pivoting","text":"pivot_wider() opposite pivot_longer(): makes dataset wider increasing number columns decreasing number rows. ’s relatively rare need pivot_wider() make tidy data, ’s often useful creating summary tables presentation, data format needed tools.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"capture-recapture-data","dir":"Articles","previous_headings":"Wider","what":"Capture-recapture data","title":"Pivoting","text":"fish_encounters dataset, contributed Myfanwy Johnston, describes fish swimming river detected automatic monitoring stations: Many tools used analyse data need form station column: dataset records fish detected station - doesn’t record wasn’t detected (common type data). means output data filled NAs. However, case know absence record means fish seen, can ask pivot_wider() fill missing values zeros:","code":"fish_encounters #> # A tibble: 114 × 3 #>    fish  station  seen #>    <fct> <fct>   <int> #>  1 4842  Release     1 #>  2 4842  I80_1       1 #>  3 4842  Lisbon      1 #>  4 4842  Rstr        1 #>  5 4842  Base_TD     1 #>  6 4842  BCE         1 #>  7 4842  BCW         1 #>  8 4842  BCE2        1 #>  9 4842  BCW2        1 #> 10 4842  MAE         1 #> # ℹ 104 more rows fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen   ) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA #>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA #>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA #>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA #>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA #>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> # ℹ 9 more rows #> # ℹ 1 more variable: MAW <int> fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen,     values_fill = 0   ) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1     0     0     0     0     0 #>  5 4847        1     1      1     0       0     0     0     0     0     0 #>  6 4848        1     1      1     1       0     0     0     0     0     0 #>  7 4849        1     1      0     0       0     0     0     0     0     0 #>  8 4850        1     1      0     1       1     1     1     0     0     0 #>  9 4851        1     1      0     0       0     0     0     0     0     0 #> 10 4854        1     1      0     0       0     0     0     0     0     0 #> # ℹ 9 more rows #> # ℹ 1 more variable: MAW <int>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"aggregation","dir":"Articles","previous_headings":"Wider","what":"Aggregation","title":"Pivoting","text":"can also use pivot_wider() perform simple aggregation. example, take warpbreaks dataset built base R (converted tibble better print method): designed experiment nine replicates every combination wool (B) tension (L, M, H): happens attempt pivot levels wool columns? get warning cell output corresponds multiple cells input. default behaviour produces list-columns, contain individual values. useful output summary statistics, e.g. mean breaks combination wool tension: complex summary operations, recommend summarising reshaping, simple cases ’s often convenient summarise within pivot_wider().","code":"warpbreaks <- warpbreaks %>%   as_tibble() %>%   select(wool, tension, breaks) warpbreaks #> # A tibble: 54 × 3 #>    wool  tension breaks #>    <fct> <fct>    <dbl> #>  1 A     L           26 #>  2 A     L           30 #>  3 A     L           54 #>  4 A     L           25 #>  5 A     L           70 #>  6 A     L           52 #>  7 A     L           51 #>  8 A     L           26 #>  9 A     L           67 #> 10 A     M           18 #> # ℹ 44 more rows warpbreaks %>%   count(wool, tension) #> # A tibble: 6 × 3 #>   wool  tension     n #>   <fct> <fct>   <int> #> 1 A     L           9 #> 2 A     M           9 #> 3 A     H           9 #> 4 B     L           9 #> 5 B     M           9 #> 6 B     H           9 warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks   ) #> Warning: Values from `breaks` are not uniquely identified; output will contain #> list-cols. #> • Use `values_fn = list` to suppress this warning. #> • Use `values_fn = {summary_fun}` to summarise duplicates. #> • Use the following dplyr code to identify duplicates. #>   {data} |> #>   dplyr::summarise(n = dplyr::n(), .by = c(tension, wool)) |> #>   dplyr::filter(n > 1L) #> # A tibble: 3 × 3 #>   tension A         B         #>   <fct>   <list>    <list>    #> 1 L       <dbl [9]> <dbl [9]> #> 2 M       <dbl [9]> <dbl [9]> #> 3 H       <dbl [9]> <dbl [9]> warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = mean   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        44.6  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"generate-column-name-from-multiple-variables","dir":"Articles","previous_headings":"Wider","what":"Generate column name from multiple variables","title":"Pivoting","text":"Imagine, https://stackoverflow.com/questions/24929954, information containing combination product, country, year. tidy form might look like : want widen data one column combination product country. key specify multiple variables names_from: either names_from values_from select multiple variables, can control column names output constructed names_sep names_prefix, workhorse names_glue:","code":"production <-   expand_grid(     product = c(\"A\", \"B\"),     country = c(\"AI\", \"EI\"),     year = 2000:2014   ) %>%   filter((product == \"A\" & country == \"AI\") | product == \"B\") %>%   mutate(production = rnorm(nrow(.))) production #> # A tibble: 45 × 4 #>    product country  year production #>    <chr>   <chr>   <int>      <dbl> #>  1 A       AI       2000    -0.244  #>  2 A       AI       2001    -0.283  #>  3 A       AI       2002    -0.554  #>  4 A       AI       2003     0.629  #>  5 A       AI       2004     2.07   #>  6 A       AI       2005    -1.63   #>  7 A       AI       2006     0.512  #>  8 A       AI       2007    -1.86   #>  9 A       AI       2008    -0.522  #> 10 A       AI       2009    -0.0526 #> # ℹ 35 more rows production %>%   pivot_wider(     names_from = c(product, country),     values_from = production   ) #> # A tibble: 15 × 4 #>     year    A_AI    B_AI    B_EI #>    <int>   <dbl>   <dbl>   <dbl> #>  1  2000 -0.244   0.738  -0.313  #>  2  2001 -0.283   1.89    1.07   #>  3  2002 -0.554  -0.0974  0.0700 #>  4  2003  0.629  -0.936  -0.639  #>  5  2004  2.07   -0.0160 -0.0500 #>  6  2005 -1.63   -0.827  -0.251  #>  7  2006  0.512  -1.51    0.445  #>  8  2007 -1.86    0.935   2.76   #>  9  2008 -0.522   0.176   0.0465 #> 10  2009 -0.0526  0.244   0.578  #> # ℹ 5 more rows production %>%   pivot_wider(     names_from = c(product, country),     values_from = production,     names_sep = \".\",     names_prefix = \"prod.\"   ) #> # A tibble: 15 × 4 #>     year prod.A.AI prod.B.AI prod.B.EI #>    <int>     <dbl>     <dbl>     <dbl> #>  1  2000   -0.244     0.738    -0.313  #>  2  2001   -0.283     1.89      1.07   #>  3  2002   -0.554    -0.0974    0.0700 #>  4  2003    0.629    -0.936    -0.639  #>  5  2004    2.07     -0.0160   -0.0500 #>  6  2005   -1.63     -0.827    -0.251  #>  7  2006    0.512    -1.51      0.445  #>  8  2007   -1.86      0.935     2.76   #>  9  2008   -0.522     0.176     0.0465 #> 10  2009   -0.0526    0.244     0.578  #> # ℹ 5 more rows  production %>%   pivot_wider(     names_from = c(product, country),     values_from = production,     names_glue = \"prod_{product}_{country}\"   ) #> # A tibble: 15 × 4 #>     year prod_A_AI prod_B_AI prod_B_EI #>    <int>     <dbl>     <dbl>     <dbl> #>  1  2000   -0.244     0.738    -0.313  #>  2  2001   -0.283     1.89      1.07   #>  3  2002   -0.554    -0.0974    0.0700 #>  4  2003    0.629    -0.936    -0.639  #>  5  2004    2.07     -0.0160   -0.0500 #>  6  2005   -1.63     -0.827    -0.251  #>  7  2006    0.512    -1.51      0.445  #>  8  2007   -1.86      0.935     2.76   #>  9  2008   -0.522     0.176     0.0465 #> 10  2009   -0.0526    0.244     0.578  #> # ℹ 5 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"tidy-census","dir":"Articles","previous_headings":"Wider","what":"Tidy census","title":"Pivoting","text":"us_rent_income dataset contains information median income rent state US 2017 (American Community Survey, retrieved tidycensus package). estimate moe values columns, can supply values_from: Note name variable automatically appended output columns.","code":"us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"implicit-missing-values","dir":"Articles","previous_headings":"Wider","what":"Implicit missing values","title":"Pivoting","text":"Occasionally, ’ll come across data names variable encoded factor, data represented. pivot_wider() defaults generating columns values actually represented data, might want include column possible level case data changes future. names_expand argument turn implicit factor levels explicit ones, forcing represented result. also sorts column names using level order, produces intuitive results case. multiple names_from columns provided, names_expand generate Cartesian product possible combinations names_from values. Notice following data omitted rows percentage value 0. names_expand allows us make explicit pivot. related problem can occur implicit missing factor levels combinations id_cols. case, missing rows (rather columns) ’d like explicitly represent. example, ’ll modify daily data type column, pivot instead, keeping day id column. type levels represented columns, missing rows related unrepresented day factor levels. can use id_expand way used names_expand, expand (sort) implicit missing rows id_cols.","code":"weekdays <- c(\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\")  daily <- tibble(   day = factor(c(\"Tue\", \"Thu\", \"Fri\", \"Mon\"), levels = weekdays),   value = c(2, 3, 1, 5) )  daily #> # A tibble: 4 × 2 #>   day   value #>   <fct> <dbl> #> 1 Tue       2 #> 2 Thu       3 #> 3 Fri       1 #> 4 Mon       5 daily %>%   pivot_wider(     names_from = day,     values_from = value   ) #> # A tibble: 1 × 4 #>     Tue   Thu   Fri   Mon #>   <dbl> <dbl> <dbl> <dbl> #> 1     2     3     1     5 daily %>%   pivot_wider(     names_from = day,     values_from = value,     names_expand = TRUE   ) #> # A tibble: 1 × 7 #>     Mon   Tue   Wed   Thu   Fri   Sat   Sun #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1     5     2    NA     3     1    NA    NA percentages <- tibble(   year = c(2018, 2019, 2020, 2020),   type = factor(c(\"A\", \"B\", \"A\", \"B\"), levels = c(\"A\", \"B\")),   percentage = c(100, 100, 40, 60) )  percentages #> # A tibble: 4 × 3 #>    year type  percentage #>   <dbl> <fct>      <dbl> #> 1  2018 A            100 #> 2  2019 B            100 #> 3  2020 A             40 #> 4  2020 B             60  percentages %>%   pivot_wider(     names_from = c(year, type),     values_from = percentage,     names_expand = TRUE,     values_fill = 0   ) #> # A tibble: 1 × 6 #>   `2018_A` `2018_B` `2019_A` `2019_B` `2020_A` `2020_B` #>      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> #> 1      100        0        0      100       40       60 daily <- mutate(daily, type = factor(c(\"A\", \"B\", \"B\", \"A\"))) daily #> # A tibble: 4 × 3 #>   day   value type  #>   <fct> <dbl> <fct> #> 1 Tue       2 A     #> 2 Thu       3 B     #> 3 Fri       1 B     #> 4 Mon       5 A daily %>%   pivot_wider(     names_from = type,     values_from = value,     values_fill = 0   ) #> # A tibble: 4 × 3 #>   day       A     B #>   <fct> <dbl> <dbl> #> 1 Tue       2     0 #> 2 Thu       0     3 #> 3 Fri       0     1 #> 4 Mon       5     0 daily %>%   pivot_wider(     names_from = type,     values_from = value,     values_fill = 0,     id_expand = TRUE   ) #> # A tibble: 7 × 3 #>   day       A     B #>   <fct> <dbl> <dbl> #> 1 Mon       5     0 #> 2 Tue       2     0 #> 3 Wed       0     0 #> 4 Thu       0     3 #> 5 Fri       0     1 #> 6 Sat       0     0 #> 7 Sun       0     0"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"unused-columns","dir":"Articles","previous_headings":"Wider","what":"Unused columns","title":"Pivoting","text":"Imagine ’ve found situation columns data completely unrelated pivoting process, ’d still like retain information somehow. example, updates ’d like pivot system column create one row summaries county’s system updates. typical pivot_wider() call, completely lose information date column. example, ’d like retain recent update date across systems particular county. accomplish can use unused_fn argument, allows us summarize values columns utilized pivoting process. can also retain data delay aggregation entirely using list() summary function.","code":"updates <- tibble(   county = c(\"Wake\", \"Wake\", \"Wake\", \"Guilford\", \"Guilford\"),   date = c(as.Date(\"2020-01-01\") + 0:2, as.Date(\"2020-01-03\") + 0:1),   system = c(\"A\", \"B\", \"C\", \"A\", \"C\"),   value = c(3.2, 4, 5.5, 2, 1.2) )  updates #> # A tibble: 5 × 4 #>   county   date       system value #>   <chr>    <date>     <chr>  <dbl> #> 1 Wake     2020-01-01 A        3.2 #> 2 Wake     2020-01-02 B        4   #> 3 Wake     2020-01-03 C        5.5 #> 4 Guilford 2020-01-03 A        2   #> 5 Guilford 2020-01-04 C        1.2 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value   ) #> # A tibble: 2 × 4 #>   county       A     B     C #>   <chr>    <dbl> <dbl> <dbl> #> 1 Wake       3.2     4   5.5 #> 2 Guilford   2      NA   1.2 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value,     unused_fn = list(date = max)   ) #> # A tibble: 2 × 5 #>   county       A     B     C date       #>   <chr>    <dbl> <dbl> <dbl> <date>     #> 1 Wake       3.2     4   5.5 2020-01-03 #> 2 Guilford   2      NA   1.2 2020-01-04 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value,     unused_fn = list(date = list)   ) #> # A tibble: 2 × 5 #>   county       A     B     C date       #>   <chr>    <dbl> <dbl> <dbl> <list>     #> 1 Wake       3.2     4   5.5 <date [3]> #> 2 Guilford   2      NA   1.2 <date [2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"contact-list","dir":"Articles","previous_headings":"Wider","what":"Contact list","title":"Pivoting","text":"final challenge inspired Jiena Gu. Imagine contact list ’ve copied pasted website: challenging ’s variable identifies observations belong together. can fix noting every contact starts name, can create unique id counting every time see “name” field: Now unique identifier person, can pivot field value columns:","code":"contacts <- tribble(   ~field, ~value,   \"name\", \"Jiena McLellan\",   \"company\", \"Toyota\",   \"name\", \"John Smith\",   \"company\", \"google\",   \"email\", \"john@google.com\",   \"name\", \"Huxley Ratcliffe\" ) contacts <- contacts %>%   mutate(     person_id = cumsum(field == \"name\")   ) contacts #> # A tibble: 6 × 3 #>   field   value            person_id #>   <chr>   <chr>                <int> #> 1 name    Jiena McLellan           1 #> 2 company Toyota                   1 #> 3 name    John Smith               2 #> 4 company google                   2 #> 5 email   john@google.com          2 #> 6 name    Huxley Ratcliffe         3 contacts %>%   pivot_wider(     names_from = field,     values_from = value   ) #> # A tibble: 3 × 4 #>   person_id name             company email           #>       <int> <chr>            <chr>   <chr>           #> 1         1 Jiena McLellan   Toyota  NA              #> 2         2 John Smith       google  john@google.com #> 3         3 Huxley Ratcliffe NA      NA"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer-then-wider","dir":"Articles","previous_headings":"","what":"Longer, then wider","title":"Pivoting","text":"problems can’t solved pivoting single direction. examples section show might combine pivot_longer() pivot_wider() solve complex problems.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"world-bank","dir":"Articles","previous_headings":"Longer, then wider","what":"World bank","title":"Pivoting","text":"world_bank_pop contains data World Bank population per country 2000 2018. goal produce tidy dataset variable column. ’s obvious exactly steps needed yet, ’ll start obvious problem: year spread across multiple columns. Next need consider indicator variable: SP.POP.GROW population growth, SP.POP.TOTL total population, SP.URB.* urban areas. Let’s split two variables: area (total urban) actual variable (population growth): Now can complete tidying pivoting variable value make TOTL GROW columns:","code":"world_bank_pop #> # A tibble: 1,064 × 20 #>    country indicator        `2000`  `2001`  `2002`  `2003`  `2004`  `2005` #>    <chr>   <chr>             <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #>  1 ABW     SP.URB.TOTL      4.16e4 4.20e+4 4.22e+4 4.23e+4 4.23e+4 4.24e+4 #>  2 ABW     SP.URB.GROW      1.66e0 9.56e-1 4.01e-1 1.97e-1 9.46e-2 1.94e-1 #>  3 ABW     SP.POP.TOTL      8.91e4 9.07e+4 9.18e+4 9.27e+4 9.35e+4 9.45e+4 #>  4 ABW     SP.POP.GROW      2.54e0 1.77e+0 1.19e+0 9.97e-1 9.01e-1 1.00e+0 #>  5 AFE     SP.URB.TOTL      1.16e8 1.20e+8 1.24e+8 1.29e+8 1.34e+8 1.39e+8 #>  6 AFE     SP.URB.GROW      3.60e0 3.66e+0 3.72e+0 3.71e+0 3.74e+0 3.81e+0 #>  7 AFE     SP.POP.TOTL      4.02e8 4.12e+8 4.23e+8 4.34e+8 4.45e+8 4.57e+8 #>  8 AFE     SP.POP.GROW      2.58e0 2.59e+0 2.61e+0 2.62e+0 2.64e+0 2.67e+0 #>  9 AFG     SP.URB.TOTL      4.31e6 4.36e+6 4.67e+6 5.06e+6 5.30e+6 5.54e+6 #> 10 AFG     SP.URB.GROW      1.86e0 1.15e+0 6.86e+0 7.95e+0 4.59e+0 4.47e+0 #> # ℹ 1,054 more rows #> # ℹ 12 more variables: `2006` <dbl>, `2007` <dbl>, `2008` <dbl>, #> #   `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, #> #   `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl> pop2 <- world_bank_pop %>%   pivot_longer(     cols = `2000`:`2017`,     names_to = \"year\",     values_to = \"value\"   ) pop2 #> # A tibble: 19,152 × 4 #>    country indicator   year  value #>    <chr>   <chr>       <chr> <dbl> #>  1 ABW     SP.URB.TOTL 2000  41625 #>  2 ABW     SP.URB.TOTL 2001  42025 #>  3 ABW     SP.URB.TOTL 2002  42194 #>  4 ABW     SP.URB.TOTL 2003  42277 #>  5 ABW     SP.URB.TOTL 2004  42317 #>  6 ABW     SP.URB.TOTL 2005  42399 #>  7 ABW     SP.URB.TOTL 2006  42555 #>  8 ABW     SP.URB.TOTL 2007  42729 #>  9 ABW     SP.URB.TOTL 2008  42906 #> 10 ABW     SP.URB.TOTL 2009  43079 #> # ℹ 19,142 more rows pop2 %>%   count(indicator) #> # A tibble: 4 × 2 #>   indicator       n #>   <chr>       <int> #> 1 SP.POP.GROW  4788 #> 2 SP.POP.TOTL  4788 #> 3 SP.URB.GROW  4788 #> 4 SP.URB.TOTL  4788 pop3 <- pop2 %>%   separate(indicator, c(NA, \"area\", \"variable\")) pop3 #> # A tibble: 19,152 × 5 #>    country area  variable year  value #>    <chr>   <chr> <chr>    <chr> <dbl> #>  1 ABW     URB   TOTL     2000  41625 #>  2 ABW     URB   TOTL     2001  42025 #>  3 ABW     URB   TOTL     2002  42194 #>  4 ABW     URB   TOTL     2003  42277 #>  5 ABW     URB   TOTL     2004  42317 #>  6 ABW     URB   TOTL     2005  42399 #>  7 ABW     URB   TOTL     2006  42555 #>  8 ABW     URB   TOTL     2007  42729 #>  9 ABW     URB   TOTL     2008  42906 #> 10 ABW     URB   TOTL     2009  43079 #> # ℹ 19,142 more rows pop3 %>%   pivot_wider(     names_from = variable,     values_from = value   ) #> # A tibble: 9,576 × 5 #>    country area  year   TOTL   GROW #>    <chr>   <chr> <chr> <dbl>  <dbl> #>  1 ABW     URB   2000  41625 1.66   #>  2 ABW     URB   2001  42025 0.956  #>  3 ABW     URB   2002  42194 0.401  #>  4 ABW     URB   2003  42277 0.197  #>  5 ABW     URB   2004  42317 0.0946 #>  6 ABW     URB   2005  42399 0.194  #>  7 ABW     URB   2006  42555 0.367  #>  8 ABW     URB   2007  42729 0.408  #>  9 ABW     URB   2008  42906 0.413  #> 10 ABW     URB   2009  43079 0.402  #> # ℹ 9,566 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"multi-choice","dir":"Articles","previous_headings":"Longer, then wider","what":"Multi-choice","title":"Pivoting","text":"Based suggestion Maxime Wack, https://github.com/tidyverse/tidyr/issues/384), final example shows deal common way recording multiple choice data. Often get data follows: actual order isn’t important, ’d prefer individual questions columns. can achieve desired transformation two steps. First, make data longer, eliminating explicit NAs, adding column indicate choice chosen: make data wider, filling missing observations FALSE:","code":"multi <- tribble(   ~id, ~choice1, ~choice2, ~choice3,   1, \"A\", \"B\", \"C\",   2, \"C\", \"B\",  NA,   3, \"D\",  NA,  NA,   4, \"B\", \"D\",  NA ) multi2 <- multi %>%   pivot_longer(     cols = !id,     values_drop_na = TRUE   ) %>%   mutate(checked = TRUE) multi2 #> # A tibble: 8 × 4 #>      id name    value checked #>   <dbl> <chr>   <chr> <lgl>   #> 1     1 choice1 A     TRUE    #> 2     1 choice2 B     TRUE    #> 3     1 choice3 C     TRUE    #> 4     2 choice1 C     TRUE    #> 5     2 choice2 B     TRUE    #> 6     3 choice1 D     TRUE    #> 7     4 choice1 B     TRUE    #> 8     4 choice2 D     TRUE multi2 %>%   pivot_wider(     id_cols = id,     names_from = value,     values_from = checked,     values_fill = FALSE   ) #> # A tibble: 4 × 5 #>      id A     B     C     D     #>   <dbl> <lgl> <lgl> <lgl> <lgl> #> 1     1 TRUE  TRUE  TRUE  FALSE #> 2     2 FALSE TRUE  TRUE  FALSE #> 3     3 FALSE FALSE FALSE TRUE  #> 4     4 FALSE TRUE  FALSE TRUE"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"manual-specs","dir":"Articles","previous_headings":"","what":"Manual specs","title":"Pivoting","text":"arguments pivot_longer() pivot_wider() allow pivot wide range datasets. creativity people apply data structures seemingly endless, ’s quite possible encounter dataset can’t immediately see reshape pivot_longer() pivot_wider(). gain control pivoting, can instead create “spec” data frame describes exactly data stored column names becomes variables (vice versa). section introduces spec data structure, show use pivot_longer() pivot_wider() insufficient.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer-1","dir":"Articles","previous_headings":"Manual specs","what":"Longer","title":"Pivoting","text":"see works, lets return simplest case pivoting applied relig_income dataset. Now pivoting happens two steps: first create spec object (using build_longer_spec()) use describe pivoting operation: (gives result , just code. ’s need use , presented simple example using spec.) spec look like? ’s data frame one row column wide format version data present long format, two special columns start .: .name gives name column. .value gives name column values cells go . also one column spec column present long format data present wide format data. corresponds names_to argument pivot_longer() build_longer_spec() names_from argument pivot_wider() build_wider_spec(). example, income column character vector names columns pivoted.","code":"spec <- relig_income %>%   build_longer_spec(     cols = !religion,     names_to = \"income\",     values_to = \"count\"   ) pivot_longer_spec(relig_income, spec) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows spec #> # A tibble: 10 × 3 #>    .name              .value income             #>    <chr>              <chr>  <chr>              #>  1 <$10k              count  <$10k              #>  2 $10-20k            count  $10-20k            #>  3 $20-30k            count  $20-30k            #>  4 $30-40k            count  $30-40k            #>  5 $40-50k            count  $40-50k            #>  6 $50-75k            count  $50-75k            #>  7 $75-100k           count  $75-100k           #>  8 $100-150k          count  $100-150k          #>  9 >150k              count  >150k              #> 10 Don't know/refused count  Don't know/refused"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"wider-1","dir":"Articles","previous_headings":"Manual specs","what":"Wider","title":"Pivoting","text":"widen us_rent_income pivot_wider(). result ok, think improved: think better columns income, rent, income_moe, rent_moe, can achieve manual spec. current spec looks like : case, mutate spec carefully construct column names: Supplying spec pivot_wider() gives us result ’re looking :","code":"us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows spec1 <- us_rent_income %>%   build_wider_spec(     names_from = variable,     values_from = c(estimate, moe)   ) spec1 #> # A tibble: 4 × 3 #>   .name           .value   variable #>   <chr>           <chr>    <chr>    #> 1 estimate_income estimate income   #> 2 estimate_rent   estimate rent     #> 3 moe_income      moe      income   #> 4 moe_rent        moe      rent spec2 <- spec1 %>%   mutate(     .name = paste0(variable, ifelse(.value == \"moe\", \"_moe\", \"\"))   ) spec2 #> # A tibble: 4 × 3 #>   .name      .value   variable #>   <chr>      <chr>    <chr>    #> 1 income     estimate income   #> 2 rent       estimate rent     #> 3 income_moe moe      income   #> 4 rent_moe   moe      rent us_rent_income %>%   pivot_wider_spec(spec2) #> # A tibble: 52 × 6 #>    GEOID NAME                 income  rent income_moe rent_moe #>    <chr> <chr>                 <dbl> <dbl>      <dbl>    <dbl> #>  1 01    Alabama               24476   747        136        3 #>  2 02    Alaska                32940  1200        508       13 #>  3 04    Arizona               27517   972        148        4 #>  4 05    Arkansas              23789   709        165        5 #>  5 06    California            29454  1358        109        3 #>  6 08    Colorado              32401  1125        109        5 #>  7 09    Connecticut           35326  1123        195        5 #>  8 10    Delaware              31560  1076        247       10 #>  9 11    District of Columbia  43198  1424        681       17 #> 10 12    Florida               25952  1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"by-hand","dir":"Articles","previous_headings":"Manual specs","what":"By hand","title":"Pivoting","text":"Sometimes ’s possible (convenient) compute spec, instead ’s convenient construct spec “hand”. example, take construction data, lightly modified Table 5 “completions” found https://www.census.gov/construction/nrc/index.html: sort data uncommon government agencies: column names actually belong different variables, summaries number units (1, 2-4, 5+) regions country (NE, NW, midwest, S, W). can easily describe tibble: yields following longer form: Note overlap units region variables; data really naturally described two independent tables.","code":"construction #> # A tibble: 9 × 9 #>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest #>   <dbl> <chr>     <dbl> <lgl>                      <dbl>     <dbl>   <dbl> #> 1  2018 Janua…      859 NA                           348       114     169 #> 2  2018 Febru…      882 NA                           400       138     160 #> 3  2018 March       862 NA                           356       150     154 #> 4  2018 April       797 NA                           447       144     196 #> 5  2018 May         875 NA                           364        90     169 #> 6  2018 June        867 NA                           342        76     170 #> 7  2018 July        829 NA                           360       108     183 #> 8  2018 August      939 NA                           286        90     205 #> 9  2018 Septe…      835 NA                           304       117     175 #> # ℹ 2 more variables: South <dbl>, West <dbl> spec <- tribble(   ~.name,            ~.value, ~units,  ~region,   \"1 unit\",          \"n\",     \"1\",     NA,   \"2 to 4 units\",    \"n\",     \"2-4\",   NA,   \"5 units or more\", \"n\",     \"5+\",    NA,   \"Northeast\",       \"n\",     NA,      \"Northeast\",   \"Midwest\",         \"n\",     NA,      \"Midwest\",   \"South\",           \"n\",     NA,      \"South\",   \"West\",            \"n\",     NA,      \"West\", ) construction %>% pivot_longer_spec(spec) #> # A tibble: 63 × 5 #>     Year Month    units region        n #>    <dbl> <chr>    <chr> <chr>     <dbl> #>  1  2018 January  1     NA          859 #>  2  2018 January  2-4   NA           NA #>  3  2018 January  5+    NA          348 #>  4  2018 January  NA    Northeast   114 #>  5  2018 January  NA    Midwest     169 #>  6  2018 January  NA    South       596 #>  7  2018 January  NA    West        339 #>  8  2018 February 1     NA          882 #>  9  2018 February 2-4   NA           NA #> 10  2018 February 5+    NA          400 #> # ℹ 53 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"theory","dir":"Articles","previous_headings":"Manual specs","what":"Theory","title":"Pivoting","text":"One neat property spec need spec pivot_longer() pivot_wider(). makes clear two operations symmetric: pivoting spec allows us precise exactly pivot_longer(df, spec = spec) changes shape df: nrow(df) * nrow(spec) rows, ncol(df) - nrow(spec) + ncol(spec) - 2 columns.","code":"construction %>%   pivot_longer_spec(spec) %>%   pivot_wider_spec(spec) #> # A tibble: 9 × 9 #>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest #>   <dbl> <chr>     <dbl>          <dbl>             <dbl>     <dbl>   <dbl> #> 1  2018 Janua…      859             NA               348       114     169 #> 2  2018 Febru…      882             NA               400       138     160 #> 3  2018 March       862             NA               356       150     154 #> 4  2018 April       797             NA               447       144     196 #> 5  2018 May         875             NA               364        90     169 #> 6  2018 June        867             NA               342        76     170 #> 7  2018 July        829             NA               360       108     183 #> 8  2018 August      939             NA               286        90     205 #> 9  2018 Septe…      835             NA               304       117     175 #> # ℹ 2 more variables: South <dbl>, West <dbl>"},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Programming with tidyr","text":"tidyr verbs use tidy evaluation make interactive data exploration fast fluid. Tidy evaluation special type non-standard evaluation used throughout tidyverse. ’s typical tidyr code: Tidy evaluation can use !Species say “columns except Species”, without quote column name (\"Species\") refer enclosing data frame (iris$Species). Two basic forms tidy evaluation used tidyr: Tidy selection: drop_na(), fill(), pivot_longer()/pivot_wider(), nest()/unnest(), separate()/extract(), unite() let select variables based position, name, type (e.g. 1:3, starts_with(\"x\"), .numeric). Literally, can use techniques dplyr::select(). Data masking: expand(), crossing() nesting() let refer use data variables variables environment (.e. write my_variable df$my_variable). focus tidy selection , since ’s common. can learn data masking equivalent vignette dplyr: https://dplyr.tidyverse.org/dev/articles/programming.html. considerations writing tidyr code packages, please see vignette(\"-packages\"). ’ve pointed tidyr’s tidy evaluation interface optimized interactive exploration. flip side adds challenges indirect use, .e. ’re working inside loop function. vignette shows overcome challenges. ’ll first go basics tidy selection data masking, talk use indirectly, show number recipes solve common problems. go , reveal version tidyr ’re using make small dataset use examples.","code":"library(tidyr)  iris %>%   nest(data = !Species) #> # A tibble: 3 × 2 #>   Species    data              #>   <fct>      <list>            #> 1 setosa     <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica  <tibble [50 × 4]> packageVersion(\"tidyr\") #> [1] '1.3.1.9000'  mini_iris <- as_tibble(iris)[c(1, 2, 51, 52, 101, 102), ] mini_iris #> # A tibble: 6 × 5 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <fct>      #> 1          5.1         3.5          1.4         0.2 setosa     #> 2          4.9         3            1.4         0.2 setosa     #> 3          7           3.2          4.7         1.4 versicolor #> 4          6.4         3.2          4.5         1.5 versicolor #> 5          6.3         3.3          6           2.5 virginica  #> 6          5.8         2.7          5.1         1.9 virginica"},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"tidy-selection","dir":"Articles","previous_headings":"","what":"Tidy selection","title":"Programming with tidyr","text":"Underneath functions use tidy selection tidyselect package. provides miniature domain specific language makes easy select columns name, position, type. example: select(df, 1) selects first column; select(df, last_col()) selects last column. select(df, c(, b, c)) selects columns , b, c. select(df, starts_with(\"\")) selects columns whose name starts “”; select(df, ends_with(\"z\")) selects columns whose name ends “z”. select(df, (.numeric)) selects numeric columns. can see details ?tidyr_tidy_select.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"indirection","dir":"Articles","previous_headings":"Tidy selection","what":"Indirection","title":"Programming with tidyr","text":"Tidy selection makes common task easier cost making less common task harder. want use tidy select indirectly column specification stored intermediate variable, ’ll need learn new tools. three main cases comes : tidy-select specification function argument, must embrace argument surrounding doubled braces. character vector variable names, must use all_of() any_of() depending whether want function error variable found. functions allow write loops function takes variable names character vector. complicated cases, might want use tidyselect directly: Learn vignette(\"tidyselect\"). Note many tidyr functions use ... can easily select many variables, e.g. fill(df, x, y, z). now believe disadvantages approach outweigh benefits, interface better fill(df, c(x, y, z)). new functions select columns, please just use single argument ....","code":"nest_egg <- function(df, cols) {   nest(df, egg = {{ cols }}) }  nest_egg(mini_iris, !Species) #> # A tibble: 3 × 2 #>   Species    egg              #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> nest_egg <- function(df, cols) {   nest(df, egg = all_of(cols)) }  vars <- c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\") nest_egg(mini_iris, vars) #> # A tibble: 3 × 2 #>   Species    egg              #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> sel_vars <- function(df, cols) {   tidyselect::eval_select(rlang::enquo(cols), df) } sel_vars(mini_iris, !Species) #> Sepal.Length  Sepal.Width Petal.Length  Petal.Width  #>            1            2            3            4"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Rectangling","text":"Rectangling art craft taking deeply nested list (often sourced wild caught JSON XML) taming tidy data set rows columns. three functions tidyr particularly useful rectangling: unnest_longer() takes element list-column makes new row. unnest_wider() takes element list-column makes new column. hoist() similar unnest_wider() plucks selected components, can reach multiple levels. (Alternative, complex inputs need rectangle nested list according specification, see tibblify package.) large number data rectangling problems can solved combining jsonlite::read_json() functions splash dplyr (largely eliminating prior approaches combined mutate() multiple purrr::map()s). Note jsonlite another important function called fromJSON(). don’t recommend performs automatic simplification (simplifyVector = TRUE). often works well, particularly simple cases, think ’re better rectangling know exactly ’s happening can easily handle complicated nested structures. illustrate techniques, ’ll use repurrrsive package, provides number deeply nested lists originally mostly captured web APIs.","code":"library(tidyr) library(dplyr) library(repurrrsive)"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"github-users","dir":"Articles","previous_headings":"","what":"GitHub users","title":"Rectangling","text":"’ll start gh_users, list contains information six GitHub users. begin, put gh_users list data frame: seems bit counter-intuitive: first step making list simpler make complicated? data frame big advantage: bundles together multiple vectors everything tracked together single object. user named list, element represents column. two ways turn list components columns. unnest_wider() takes every component makes new column: case, many components don’t need can instead use hoist(). hoist() allows us pull selected components using syntax purrr::pluck(): hoist() removes named components user list-column, can think moving components inner list top-level data frame.","code":"users <- tibble(user = gh_users) names(users$user[[1]]) #>  [1] \"login\"               \"id\"                  \"avatar_url\"          #>  [4] \"gravatar_id\"         \"url\"                 \"html_url\"            #>  [7] \"followers_url\"       \"following_url\"       \"gists_url\"           #> [10] \"starred_url\"         \"subscriptions_url\"   \"organizations_url\"   #> [13] \"repos_url\"           \"events_url\"          \"received_events_url\" #> [16] \"type\"                \"site_admin\"          \"name\"                #> [19] \"company\"             \"blog\"                \"location\"            #> [22] \"email\"               \"hireable\"            \"bio\"                 #> [25] \"public_repos\"        \"public_gists\"        \"followers\"           #> [28] \"following\"           \"created_at\"          \"updated_at\" users %>% unnest_wider(user) #> # A tibble: 6 × 30 #>   login             id avatar_url gravatar_id url   html_url followers_url #>   <chr>          <int> <chr>      <chr>       <chr> <chr>    <chr>         #> 1 gaborcsardi   660288 https://a… \"\"          http… https:/… https://api.… #> 2 jennybc       599454 https://a… \"\"          http… https:/… https://api.… #> 3 jtleek       1571674 https://a… \"\"          http… https:/… https://api.… #> 4 juliasilge  12505835 https://a… \"\"          http… https:/… https://api.… #> 5 leeper       3505428 https://a… \"\"          http… https:/… https://api.… #> 6 masalmon     8360597 https://a… \"\"          http… https:/… https://api.… #> # ℹ 23 more variables: following_url <chr>, gists_url <chr>, #> #   starred_url <chr>, subscriptions_url <chr>, organizations_url <chr>, #> #   repos_url <chr>, events_url <chr>, received_events_url <chr>, #> #   type <chr>, site_admin <lgl>, name <chr>, company <chr>, blog <chr>, #> #   location <chr>, email <chr>, hireable <lgl>, bio <chr>, #> #   public_repos <int>, public_gists <int>, followers <int>, #> #   following <int>, created_at <chr>, updated_at <chr> users %>% hoist(user,   followers = \"followers\",   login = \"login\",   url = \"html_url\" ) #> # A tibble: 6 × 4 #>   followers login       url                            user              #>       <int> <chr>       <chr>                          <list>            #> 1       303 gaborcsardi https://github.com/gaborcsardi <named list [27]> #> 2       780 jennybc     https://github.com/jennybc     <named list [27]> #> 3      3958 jtleek      https://github.com/jtleek      <named list [27]> #> 4       115 juliasilge  https://github.com/juliasilge  <named list [27]> #> 5       213 leeper      https://github.com/leeper      <named list [27]> #> 6        34 masalmon    https://github.com/masalmon    <named list [27]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"github-repos","dir":"Articles","previous_headings":"","what":"GitHub repos","title":"Rectangling","text":"start gh_repos similarly, putting tibble: time elements repos list repositories belong user. observations, become new rows, use unnest_longer() rather unnest_wider(): can use unnest_wider() hoist(): Note use c(\"owner\", \"login\"): allows us reach two levels deep inside list. alternative approach pull just owner put element column:","code":"repos <- tibble(repo = gh_repos) repos #> # A tibble: 6 × 1 #>   repo        #>   <list>      #> 1 <list [30]> #> 2 <list [30]> #> 3 <list [30]> #> 4 <list [26]> #> 5 <list [30]> #> 6 <list [30]> repos <- repos %>% unnest_longer(repo) repos #> # A tibble: 176 × 1 #>    repo              #>    <list>            #>  1 <named list [68]> #>  2 <named list [68]> #>  3 <named list [68]> #>  4 <named list [68]> #>  5 <named list [68]> #>  6 <named list [68]> #>  7 <named list [68]> #>  8 <named list [68]> #>  9 <named list [68]> #> 10 <named list [68]> #> # ℹ 166 more rows repos %>% hoist(repo,   login = c(\"owner\", \"login\"),   name = \"name\",   homepage = \"homepage\",   watchers = \"watchers_count\" ) #> # A tibble: 176 × 5 #>    login       name        homepage watchers repo              #>    <chr>       <chr>       <chr>       <int> <list>            #>  1 gaborcsardi after       NA              5 <named list [65]> #>  2 gaborcsardi argufy      NA             19 <named list [65]> #>  3 gaborcsardi ask         NA              5 <named list [65]> #>  4 gaborcsardi baseimports NA              0 <named list [65]> #>  5 gaborcsardi citest      NA              0 <named list [65]> #>  6 gaborcsardi clisymbols  \"\"             18 <named list [65]> #>  7 gaborcsardi cmaker      NA              0 <named list [65]> #>  8 gaborcsardi cmark       NA              0 <named list [65]> #>  9 gaborcsardi conditions  NA              0 <named list [65]> #> 10 gaborcsardi crayon      NA             52 <named list [65]> #> # ℹ 166 more rows repos %>%   hoist(repo, owner = \"owner\") %>%   unnest_wider(owner) #> # A tibble: 176 × 18 #>    login           id avatar_url  gravatar_id url   html_url followers_url #>    <chr>        <int> <chr>       <chr>       <chr> <chr>    <chr>         #>  1 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  2 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  3 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  4 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  5 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  6 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  7 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  8 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  9 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #> 10 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #> # ℹ 166 more rows #> # ℹ 11 more variables: following_url <chr>, gists_url <chr>, #> #   starred_url <chr>, subscriptions_url <chr>, organizations_url <chr>, #> #   repos_url <chr>, events_url <chr>, received_events_url <chr>, #> #   type <chr>, site_admin <lgl>, repo <list>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"game-of-thrones-characters","dir":"Articles","previous_headings":"","what":"Game of Thrones characters","title":"Rectangling","text":"got_chars similar structure gh_users: ’s list named lists, element inner list describes attribute GoT character. start way, first creating data frame unnesting component column: complex gh_users component char list, giving us collection list-columns: next depend purposes analysis. Maybe want row every book TV series character appears : maybe want build table lets match title name: (Note empty titles (\"\") due infelicity input got_chars: ideally people without titles title vector length 0, title vector length 1 containing empty string.)","code":"chars <- tibble(char = got_chars) chars #> # A tibble: 30 × 1 #>    char              #>    <list>            #>  1 <named list [18]> #>  2 <named list [18]> #>  3 <named list [18]> #>  4 <named list [18]> #>  5 <named list [18]> #>  6 <named list [18]> #>  7 <named list [18]> #>  8 <named list [18]> #>  9 <named list [18]> #> 10 <named list [18]> #> # ℹ 20 more rows  chars2 <- chars %>% unnest_wider(char) chars2 #> # A tibble: 30 × 18 #>    url            id name  gender culture born  died  alive titles aliases #>    <chr>       <int> <chr> <chr>  <chr>   <chr> <chr> <lgl> <list> <list>  #>  1 https://ww…  1022 Theo… Male   \"Ironb… \"In … \"\"    TRUE  <chr>  <chr>   #>  2 https://ww…  1052 Tyri… Male   \"\"      \"In … \"\"    TRUE  <chr>  <chr>   #>  3 https://ww…  1074 Vict… Male   \"Ironb… \"In … \"\"    TRUE  <chr>  <chr>   #>  4 https://ww…  1109 Will  Male   \"\"      \"\"    \"In … FALSE <chr>  <chr>   #>  5 https://ww…  1166 Areo… Male   \"Norvo… \"In … \"\"    TRUE  <chr>  <chr>   #>  6 https://ww…  1267 Chett Male   \"\"      \"At … \"In … FALSE <chr>  <chr>   #>  7 https://ww…  1295 Cres… Male   \"\"      \"In … \"In … FALSE <chr>  <chr>   #>  8 https://ww…   130 Aria… Female \"Dorni… \"In … \"\"    TRUE  <chr>  <chr>   #>  9 https://ww…  1303 Daen… Female \"Valyr… \"In … \"\"    TRUE  <chr>  <chr>   #> 10 https://ww…  1319 Davo… Male   \"Weste… \"In … \"\"    TRUE  <chr>  <chr>   #> # ℹ 20 more rows #> # ℹ 8 more variables: father <chr>, mother <chr>, spouse <chr>, #> #   allegiances <list>, books <list>, povBooks <list>, tvSeries <list>, #> #   playedBy <list> chars2 %>% select_if(is.list) #> # A tibble: 30 × 7 #>    titles    aliases    allegiances books     povBooks  tvSeries  playedBy #>    <list>    <list>     <list>      <list>    <list>    <list>    <list>   #>  1 <chr [2]> <chr [4]>  <chr [1]>   <chr [3]> <chr [2]> <chr [6]> <chr>    #>  2 <chr [2]> <chr [11]> <chr [1]>   <chr [2]> <chr [4]> <chr [6]> <chr>    #>  3 <chr [2]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [1]> <chr>    #>  4 <chr [1]> <chr [1]>  <NULL>      <chr [1]> <chr [1]> <chr [1]> <chr>    #>  5 <chr [1]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [2]> <chr>    #>  6 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr>    #>  7 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr>    #>  8 <chr [1]> <chr [1]>  <chr [1]>   <chr [4]> <chr [1]> <chr [1]> <chr>    #>  9 <chr [5]> <chr [11]> <chr [1]>   <chr [1]> <chr [4]> <chr [6]> <chr>    #> 10 <chr [4]> <chr [5]>  <chr [2]>   <chr [1]> <chr [3]> <chr [5]> <chr>    #> # ℹ 20 more rows chars2 %>%   select(name, books, tvSeries) %>%   pivot_longer(c(books, tvSeries), names_to = \"media\", values_to = \"value\") %>%   unnest_longer(value) #> # A tibble: 179 × 3 #>    name             media    value             #>    <chr>            <chr>    <chr>             #>  1 Theon Greyjoy    books    A Game of Thrones #>  2 Theon Greyjoy    books    A Storm of Swords #>  3 Theon Greyjoy    books    A Feast for Crows #>  4 Theon Greyjoy    tvSeries Season 1          #>  5 Theon Greyjoy    tvSeries Season 2          #>  6 Theon Greyjoy    tvSeries Season 3          #>  7 Theon Greyjoy    tvSeries Season 4          #>  8 Theon Greyjoy    tvSeries Season 5          #>  9 Theon Greyjoy    tvSeries Season 6          #> 10 Tyrion Lannister books    A Feast for Crows #> # ℹ 169 more rows chars2 %>%   select(name, title = titles) %>%   unnest_longer(title) #> # A tibble: 59 × 2 #>    name              title                                                 #>    <chr>             <chr>                                                 #>  1 Theon Greyjoy     \"Prince of Winterfell\"                                #>  2 Theon Greyjoy     \"Lord of the Iron Islands (by law of the green lands… #>  3 Tyrion Lannister  \"Acting Hand of the King (former)\"                    #>  4 Tyrion Lannister  \"Master of Coin (former)\"                             #>  5 Victarion Greyjoy \"Lord Captain of the Iron Fleet\"                      #>  6 Victarion Greyjoy \"Master of the Iron Victory\"                          #>  7 Will              \"\"                                                    #>  8 Areo Hotah        \"Captain of the Guard at Sunspear\"                    #>  9 Chett             \"\"                                                    #> 10 Cressen           \"Maester\"                                             #> # ℹ 49 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"geocoding-with-google","dir":"Articles","previous_headings":"","what":"Geocoding with google","title":"Rectangling","text":"Next ’ll tackle complex form data comes Google’s geocoding service, stored repurssive package json list-column named lists, makes sense start unnest_wider(): Notice results list lists. cities 1 element (representing unique match geocoding API), Washington Arlington two. can pull separate rows unnest_longer(): Now components, revealed unnest_wider(): can find latitude longitude unnesting geometry: location: also just look first address city: use hoist() dive deeply get directly lat lng:","code":"repurrrsive::gmaps_cities #> # A tibble: 5 × 2 #>   city       json             #>   <chr>      <list>           #> 1 Houston    <named list [2]> #> 2 Washington <named list [2]> #> 3 New York   <named list [2]> #> 4 Chicago    <named list [2]> #> 5 Arlington  <named list [2]> repurrrsive::gmaps_cities %>%   unnest_wider(json) #> # A tibble: 5 × 3 #>   city       results    status #>   <chr>      <list>     <chr>  #> 1 Houston    <list [1]> OK     #> 2 Washington <list [2]> OK     #> 3 New York   <list [1]> OK     #> 4 Chicago    <list [1]> OK     #> 5 Arlington  <list [2]> OK repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) #> # A tibble: 7 × 3 #>   city       results          status #>   <chr>      <list>           <chr>  #> 1 Houston    <named list [5]> OK     #> 2 Washington <named list [5]> OK     #> 3 Washington <named list [5]> OK     #> 4 New York   <named list [5]> OK     #> 5 Chicago    <named list [5]> OK     #> 6 Arlington  <named list [5]> OK     #> 7 Arlington  <named list [5]> OK repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) #> # A tibble: 7 × 7 #>   city   address_components formatted_address geometry     place_id types  #>   <chr>  <list>             <chr>             <list>       <chr>    <list> #> 1 Houst… <list [4]>         Houston, TX, USA  <named list> ChIJAYW… <list> #> 2 Washi… <list [2]>         Washington, USA   <named list> ChIJ-bD… <list> #> 3 Washi… <list [4]>         Washington, DC, … <named list> ChIJW-T… <list> #> 4 New Y… <list [3]>         New York, NY, USA <named list> ChIJOwg… <list> #> 5 Chica… <list [4]>         Chicago, IL, USA  <named list> ChIJ7cv… <list> #> 6 Arlin… <list [4]>         Arlington, TX, U… <named list> ChIJ05g… <list> #> 7 Arlin… <list [4]>         Arlington, VA, U… <named list> ChIJD6e… <list> #> # ℹ 1 more variable: status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) %>%   unnest_wider(geometry) #> # A tibble: 7 × 10 #>   city      address_components formatted_address bounds       location     #>   <chr>     <list>             <chr>             <list>       <list>       #> 1 Houston   <list [4]>         Houston, TX, USA  <named list> <named list> #> 2 Washingt… <list [2]>         Washington, USA   <named list> <named list> #> 3 Washingt… <list [4]>         Washington, DC, … <named list> <named list> #> 4 New York  <list [3]>         New York, NY, USA <named list> <named list> #> 5 Chicago   <list [4]>         Chicago, IL, USA  <named list> <named list> #> 6 Arlington <list [4]>         Arlington, TX, U… <named list> <named list> #> 7 Arlington <list [4]>         Arlington, VA, U… <named list> <named list> #> # ℹ 5 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) %>%   unnest_wider(geometry) %>%   unnest_wider(location) #> # A tibble: 7 × 11 #>   city      address_components formatted_address bounds         lat    lng #>   <chr>     <list>             <chr>             <list>       <dbl>  <dbl> #> 1 Houston   <list [4]>         Houston, TX, USA  <named list>  29.8  -95.4 #> 2 Washingt… <list [2]>         Washington, USA   <named list>  47.8 -121.  #> 3 Washingt… <list [4]>         Washington, DC, … <named list>  38.9  -77.0 #> 4 New York  <list [3]>         New York, NY, USA <named list>  40.7  -74.0 #> 5 Chicago   <list [4]>         Chicago, IL, USA  <named list>  41.9  -87.6 #> 6 Arlington <list [4]>         Arlington, TX, U… <named list>  32.7  -97.1 #> 7 Arlington <list [4]>         Arlington, VA, U… <named list>  38.9  -77.1 #> # ℹ 5 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   hoist(results, first_result = 1) %>%   unnest_wider(first_result) %>%   unnest_wider(geometry) %>%   unnest_wider(location) #> # A tibble: 5 × 12 #>   city      address_components formatted_address bounds         lat    lng #>   <chr>     <list>             <chr>             <list>       <dbl>  <dbl> #> 1 Houston   <list [4]>         Houston, TX, USA  <named list>  29.8  -95.4 #> 2 Washingt… <list [2]>         Washington, USA   <named list>  47.8 -121.  #> 3 New York  <list [3]>         New York, NY, USA <named list>  40.7  -74.0 #> 4 Chicago   <list [4]>         Chicago, IL, USA  <named list>  41.9  -87.6 #> 5 Arlington <list [4]>         Arlington, TX, U… <named list>  32.7  -97.1 #> # ℹ 6 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, results <list>, status <chr> repurrrsive::gmaps_cities %>%   hoist(json,     lat = list(\"results\", 1, \"geometry\", \"location\", \"lat\"),     lng = list(\"results\", 1, \"geometry\", \"location\", \"lng\")   ) #> # A tibble: 5 × 4 #>   city         lat    lng json             #>   <chr>      <dbl>  <dbl> <list>           #> 1 Houston     29.8  -95.4 <named list [2]> #> 2 Washington  47.8 -121.  <named list [2]> #> 3 New York    40.7  -74.0 <named list [2]> #> 4 Chicago     41.9  -87.6 <named list [2]> #> 5 Arlington   32.7  -97.1 <named list [2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"sharla-gelfands-discography","dir":"Articles","previous_headings":"","what":"Sharla Gelfand’s discography","title":"Rectangling","text":"’ll finish complex list, Sharla Gelfand’s discography. ’ll start usual way: putting list single column data frame, widening component column. also parse date_added column real date-time1. level, see information disc added Sharla’s discography, information disc . need widen basic_information column: Unfortunately fails ’s id column inside basic_information. can quickly see ’s going setting names_repair = \"unique\": problem basic_information repeats id column ’s also stored top-level, can just drop : Alternatively, use hoist(): quickly extract name first label artist indexing deeply nested list. systematic approach create separate tables artist label: join back original dataset needed.","code":"discs <- tibble(disc = discog) %>%   unnest_wider(disc) %>%   mutate(date_added = as.POSIXct(strptime(date_added, \"%Y-%m-%dT%H:%M:%S\"))) discs #> # A tibble: 155 × 5 #>    instance_id date_added          basic_information       id rating #>          <int> <dttm>              <list>               <int>  <int> #>  1   354823933 2019-02-16 17:48:59 <named list [11]>  7496378      0 #>  2   354092601 2019-02-13 14:13:11 <named list [11]>  4490852      0 #>  3   354091476 2019-02-13 14:07:23 <named list [11]>  9827276      0 #>  4   351244906 2019-02-02 11:39:58 <named list [11]>  9769203      0 #>  5   351244801 2019-02-02 11:39:37 <named list [11]>  7237138      0 #>  6   351052065 2019-02-01 20:40:53 <named list [11]> 13117042      0 #>  7   350315345 2019-01-29 15:48:37 <named list [11]>  7113575      0 #>  8   350315103 2019-01-29 15:47:22 <named list [11]> 10540713      0 #>  9   350314507 2019-01-29 15:44:08 <named list [11]> 11260950      0 #> 10   350314047 2019-01-29 15:41:35 <named list [11]> 11726853      0 #> # ℹ 145 more rows discs %>% unnest_wider(basic_information) #> Error in `unnest_wider()`: #> ! Can't duplicate names between the affected columns and the #>   original data. #> ✖ These names are duplicated: #>   ℹ `id`, from `basic_information`. #> ℹ Use `names_sep` to disambiguate using the column name. #> ℹ Or use `names_repair` to specify a repair strategy. discs %>% unnest_wider(basic_information, names_repair = \"unique\") #> New names: #> • `id` -> `id...7` #> • `id` -> `id...14` #> # A tibble: 155 × 15 #>    instance_id date_added          labels  year master_url  artists id...7 #>          <int> <dttm>              <list> <int> <chr>       <list>   <int> #>  1   354823933 2019-02-16 17:48:59 <list>  2015 NA          <list>  7.50e6 #>  2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 #>  3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 #>  4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 #>  5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 #>  6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 #>  7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 #>  8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 #>  9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 #> 10   350314047 2019-01-29 15:41:35 <list>  2017 NA          <list>  1.17e7 #> # ℹ 145 more rows #> # ℹ 8 more variables: thumb <chr>, title <chr>, formats <list>, #> #   cover_image <chr>, resource_url <chr>, master_id <int>, #> #   id...14 <int>, rating <int> discs %>%   select(!id) %>%   unnest_wider(basic_information) #> # A tibble: 155 × 14 #>    instance_id date_added          labels  year master_url  artists     id #>          <int> <dttm>              <list> <int> <chr>       <list>   <int> #>  1   354823933 2019-02-16 17:48:59 <list>  2015 NA          <list>  7.50e6 #>  2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 #>  3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 #>  4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 #>  5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 #>  6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 #>  7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 #>  8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 #>  9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 #> 10   350314047 2019-01-29 15:41:35 <list>  2017 NA          <list>  1.17e7 #> # ℹ 145 more rows #> # ℹ 7 more variables: thumb <chr>, title <chr>, formats <list>, #> #   cover_image <chr>, resource_url <chr>, master_id <int>, rating <int> discs %>%   hoist(basic_information,     title = \"title\",     year = \"year\",     label = list(\"labels\", 1, \"name\"),     artist = list(\"artists\", 1, \"name\")   ) #> # A tibble: 155 × 9 #>    instance_id date_added          title                 year label artist #>          <int> <dttm>              <chr>                <int> <chr> <chr>  #>  1   354823933 2019-02-16 17:48:59 Demo                  2015 Tobi… Mollot #>  2   354092601 2019-02-13 14:13:11 Observant Com El Mo…  2013 La V… Una B… #>  3   354091476 2019-02-13 14:07:23 I                     2017 La V… S.H.I… #>  4   351244906 2019-02-02 11:39:58 Oído Absoluto         2017 La V… Rata … #>  5   351244801 2019-02-02 11:39:37 A Cat's Cause, No D…  2015 Kato… Ivy (… #>  6   351052065 2019-02-01 20:40:53 Tashme                2019 High… Tashme #>  7   350315345 2019-01-29 15:48:37 Demo                  2014 Mind… Desgr… #>  8   350315103 2019-01-29 15:47:22 Let The Miracles Be…  2015 Not … Phant… #>  9   350314507 2019-01-29 15:44:08 Sub Space             2017 Not … Sub S… #> 10   350314047 2019-01-29 15:41:35 Demo                  2017 Pres… Small… #> # ℹ 145 more rows #> # ℹ 3 more variables: basic_information <list>, id <int>, rating <int> discs %>%   hoist(basic_information, artist = \"artists\") %>%   select(disc_id = id, artist) %>%   unnest_longer(artist) %>%   unnest_wider(artist) #> # A tibble: 167 × 8 #>     disc_id join  name              anv   tracks role  resource_url     id #>       <int> <chr> <chr>             <chr> <chr>  <chr> <chr>         <int> #>  1  7496378 \"\"    Mollot            \"\"    \"\"     \"\"    https://api… 4.62e6 #>  2  4490852 \"\"    Una Bèstia Incon… \"\"    \"\"     \"\"    https://api… 3.19e6 #>  3  9827276 \"\"    S.H.I.T. (3)      \"\"    \"\"     \"\"    https://api… 2.77e6 #>  4  9769203 \"\"    Rata Negra        \"\"    \"\"     \"\"    https://api… 4.28e6 #>  5  7237138 \"\"    Ivy (18)          \"\"    \"\"     \"\"    https://api… 3.60e6 #>  6 13117042 \"\"    Tashme            \"\"    \"\"     \"\"    https://api… 5.21e6 #>  7  7113575 \"\"    Desgraciados      \"\"    \"\"     \"\"    https://api… 4.45e6 #>  8 10540713 \"\"    Phantom Head      \"\"    \"\"     \"\"    https://api… 4.27e6 #>  9 11260950 \"\"    Sub Space (2)     \"\"    \"\"     \"\"    https://api… 5.69e6 #> 10 11726853 \"\"    Small Man (2)     \"\"    \"\"     \"\"    https://api… 6.37e6 #> # ℹ 157 more rows  discs %>%   hoist(basic_information, format = \"formats\") %>%   select(disc_id = id, format) %>%   unnest_longer(format) %>%   unnest_wider(format) %>%   unnest_longer(descriptions) #> # A tibble: 258 × 5 #>     disc_id descriptions text  name     qty   #>       <int> <chr>        <chr> <chr>    <chr> #>  1  7496378 \"Numbered\"   Black Cassette 1     #>  2  4490852 \"LP\"         NA    Vinyl    1     #>  3  9827276 \"7\\\"\"        NA    Vinyl    1     #>  4  9827276 \"45 RPM\"     NA    Vinyl    1     #>  5  9827276 \"EP\"         NA    Vinyl    1     #>  6  9769203 \"LP\"         NA    Vinyl    1     #>  7  9769203 \"Album\"      NA    Vinyl    1     #>  8  7237138 \"7\\\"\"        NA    Vinyl    1     #>  9  7237138 \"45 RPM\"     NA    Vinyl    1     #> 10 13117042 \"7\\\"\"        NA    Vinyl    1     #> # ℹ 248 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-tidying","dir":"Articles","previous_headings":"","what":"Data tidying","title":"Tidy data","text":"often said 80% data analysis spent cleaning preparing data. ’s just first step, must repeated many times course analysis new problems come light new data collected. get handle problem, paper focuses small, important, aspect data cleaning call data tidying: structuring datasets facilitate analysis. principles tidy data provide standard way organise data values within dataset. standard makes initial data cleaning easier don’t need start scratch reinvent wheel every time. tidy data standard designed facilitate initial exploration analysis data, simplify development data analysis tools work well together. Current tools often require translation. spend time munging output one tool can input another. Tidy datasets tidy tools work hand hand make data analysis easier, allowing focus interesting domain problem, uninteresting logistics data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"defining","dir":"Articles","previous_headings":"","what":"Defining tidy data","title":"Tidy data","text":"Happy families alike; every unhappy family unhappy way — Leo Tolstoy Like families, tidy datasets alike every messy dataset messy way. Tidy datasets provide standardized way link structure dataset (physical layout) semantics (meaning). section, ’ll provide standard vocabulary describing structure semantics dataset, use definitions define tidy data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-structure","dir":"Articles","previous_headings":"Defining tidy data","what":"Data structure","title":"Tidy data","text":"statistical datasets data frames made rows columns. columns almost always labeled rows sometimes labeled. following code provides data imaginary classroom format commonly seen wild. table three columns four rows, rows columns labeled. many ways structure underlying data. following table shows data , rows columns transposed. data , layout different. vocabulary rows columns simply rich enough describe two tables represent data. addition appearance, need way describe underlying semantics, meaning, values displayed table.","code":"library(tibble) classroom <- tribble(   ~name,    ~quiz1, ~quiz2, ~test1,   \"Billy\",  NA,     \"D\",    \"C\",   \"Suzy\",   \"F\",    NA,     NA,   \"Lionel\", \"B\",    \"C\",    \"B\",   \"Jenny\",  \"A\",    \"A\",    \"B\"   ) classroom #> # A tibble: 4 × 4 #>   name   quiz1 quiz2 test1 #>   <chr>  <chr> <chr> <chr> #> 1 Billy  NA    D     C     #> 2 Suzy   F     NA    NA    #> 3 Lionel B     C     B     #> 4 Jenny  A     A     B tribble(   ~assessment, ~Billy, ~Suzy, ~Lionel, ~Jenny,   \"quiz1\",     NA,     \"F\",   \"B\",     \"A\",   \"quiz2\",     \"D\",    NA,    \"C\",     \"A\",   \"test1\",     \"C\",    NA,    \"B\",     \"B\"   ) #> # A tibble: 3 × 5 #>   assessment Billy Suzy  Lionel Jenny #>   <chr>      <chr> <chr> <chr>  <chr> #> 1 quiz1      NA    F     B      A     #> 2 quiz2      D     NA    C      A     #> 3 test1      C     NA    B      B"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-semantics","dir":"Articles","previous_headings":"Defining tidy data","what":"Data semantics","title":"Tidy data","text":"dataset collection values, usually either numbers (quantitative) strings (qualitative). Values organised two ways. Every value belongs variable observation. variable contains values measure underlying attribute (like height, temperature, duration) across units. observation contains values measured unit (like person, day, race) across attributes. tidy version classroom data looks like : (’ll learn functions work little later) makes values, variables, observations clear. dataset contains 36 values representing three variables 12 observations. variables : name, four possible values (Billy, Suzy, Lionel, Jenny). assessment, three possible values (quiz1, quiz2, test1). grade, five six values depending think missing value (, B, C, D, F, NA). tidy data frame explicitly tells us definition observation. classroom, every combination name assessment single measured observation. dataset also informs us missing values, can meaning. Billy absent first quiz, tried salvage grade. Suzy failed first quiz, decided drop class. calculate Billy’s final grade, might replace missing value F (might get second chance take quiz). However, want know class average Test 1, dropping Suzy’s structural missing value appropriate imputing new value. given dataset, ’s usually easy figure observations variables, surprisingly difficult precisely define variables observations general. example, columns classroom data height weight happy call variables. columns height width, less clear cut, might think height width values dimension variable. columns home phone work phone, treat two variables, fraud detection environment might want variables phone number number type use one phone number multiple people might suggest fraud. general rule thumb easier describe functional relationships variables (e.g., z linear combination x y, density ratio weight volume) rows, easier make comparisons groups observations (e.g., average group vs. average group b) groups columns. given analysis, may multiple levels observation. example, trial new allergy medication might three observational types: demographic data collected person (age, sex, race), medical data collected person day (number sneezes, redness eyes), meteorological data collected day (temperature, pollen count). Variables may change course analysis. Often variables raw data fine grained, may add extra modelling complexity little explanatory gain. example, many surveys ask variations question better get underlying trait. early stages analysis, variables correspond questions. later stages, change focus traits, computed averaging together multiple questions. considerably simplifies analysis don’t need hierarchical model, can often pretend data continuous, discrete.","code":"library(tidyr) library(dplyr) classroom2 <- classroom %>%   pivot_longer(quiz1:test1, names_to = \"assessment\", values_to = \"grade\") %>%   arrange(name, assessment) classroom2 #> # A tibble: 12 × 3 #>    name   assessment grade #>    <chr>  <chr>      <chr> #>  1 Billy  quiz1      NA    #>  2 Billy  quiz2      D     #>  3 Billy  test1      C     #>  4 Jenny  quiz1      A     #>  5 Jenny  quiz2      A     #>  6 Jenny  test1      B     #>  7 Lionel quiz1      B     #>  8 Lionel quiz2      C     #>  9 Lionel test1      B     #> 10 Suzy   quiz1      F     #> # ℹ 2 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"tidy-data","dir":"Articles","previous_headings":"Defining tidy data","what":"Tidy data","title":"Tidy data","text":"Tidy data standard way mapping meaning dataset structure. dataset messy tidy depending rows, columns tables matched observations, variables types. tidy data: variable column; column variable. observation row; row observation. value cell; cell single value. Codd’s 3rd normal form, constraints framed statistical language, focus put single dataset rather many connected datasets common relational databases. Messy data arrangement data. Tidy data makes easy analyst computer extract needed variables provides standard way structuring dataset. Compare different versions classroom data: messy version need use different strategies extract different variables. slows analysis invites errors. consider many data analysis operations involve values variable (every aggregation function), can see important extract values simple, standard way. Tidy data particularly well suited vectorised programming languages like R, layout ensures values different variables observation always paired. order variables observations affect analysis, good ordering makes easier scan raw values. One way organising variables role analysis: values fixed design data collection, measured course experiment? Fixed variables describe experimental design known advance. Computer scientists often call fixed variables dimensions, statisticians usually denote subscripts random variables. Measured variables actually measure study. Fixed variables come first, followed measured variables, ordered related variables contiguous. Rows can ordered first variable, breaking ties second subsequent (fixed) variables. convention adopted tabular displays paper.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"tidying","dir":"Articles","previous_headings":"","what":"Tidying messy datasets","title":"Tidy data","text":"Real datasets can, often , violate three precepts tidy data almost every way imaginable. occasionally get dataset can start analysing immediately, exception, rule. section describes five common problems messy datasets, along remedies: Column headers values, variable names. Multiple variables stored one column. Variables stored rows columns. Multiple types observational units stored table. single observational unit stored multiple tables. Surprisingly, messy datasets, including types messiness explicitly described , can tidied small set tools: pivoting (longer wider) separating. following sections illustrate problem real dataset encountered, show tidy .","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"column-headers-are-values-not-variable-names","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Column headers are values, not variable names","title":"Tidy data","text":"common type messy dataset tabular data designed presentation, variables form rows columns, column headers values, variable names. call arrangement messy, cases can extremely useful. provides efficient storage completely crossed designs, can lead extremely efficient computation desired operations can expressed matrix operations. following code shows subset typical dataset form. dataset explores relationship income religion US. comes report produced Pew Research Center, American think-tank collects data attitudes topics ranging religion internet, produces many reports contain datasets format. dataset three variables, religion, income frequency. tidy , need pivot non-variable columns two-column key-value pair. action often described making wide dataset longer (taller). pivoting variables, need provide name new key-value columns create. defining columns pivot (every column except religion), need name key column, name variable defined values column headings. case, ’s income. second argument name value column, frequency. form tidy column represents variable row represents observation, case demographic unit corresponding combination religion income. format also used record regularly spaced observations time. example, Billboard dataset shown records date song first entered billboard top 100. variables artist, track, date.entered, rank week. rank week enters top 100 recorded 75 columns, wk1 wk75. form storage tidy, useful data entry. reduces duplication since otherwise song week need row, song metadata like title artist need repeated. discussed depth multiple types. tidy dataset, first use pivot_longer() make dataset longer. transform columns wk1 wk76, making new column names, week, new value values, rank: use values_drop_na = TRUE drop missing values rank column. data, missing values represent weeks song wasn’t charts, can safely dropped. case ’s also nice little cleaning, converting week variable number, figuring date corresponding week charts: Finally, ’s always good idea sort data. artist, track week: date rank:","code":"relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> # ℹ 8 more rows #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(-religion, names_to = \"income\", values_to = \"frequency\") #> # A tibble: 180 × 3 #>    religion income             frequency #>    <chr>    <chr>                  <dbl> #>  1 Agnostic <$10k                     27 #>  2 Agnostic $10-20k                   34 #>  3 Agnostic $20-30k                   60 #>  4 Agnostic $30-40k                   81 #>  5 Agnostic $40-50k                   76 #>  6 Agnostic $50-75k                  137 #>  7 Agnostic $75-100k                 122 #>  8 Agnostic $100-150k                109 #>  9 Agnostic >150k                     84 #> 10 Agnostic Don't know/refused        96 #> # ℹ 170 more rows billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard2 <- billboard %>%   pivot_longer(     wk1:wk76,     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) billboard2 #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 #> # ℹ 5,297 more rows billboard3 <- billboard2 %>%   mutate(     week = as.integer(gsub(\"wk\", \"\", week)),     date = as.Date(date.entered) + 7 * (week - 1),     date.entered = NULL   ) billboard3 #> # A tibble: 5,307 × 5 #>    artist  track                    week  rank date       #>    <chr>   <chr>                   <int> <dbl> <date>     #>  1 2 Pac   Baby Don't Cry (Keep...     1    87 2000-02-26 #>  2 2 Pac   Baby Don't Cry (Keep...     2    82 2000-03-04 #>  3 2 Pac   Baby Don't Cry (Keep...     3    72 2000-03-11 #>  4 2 Pac   Baby Don't Cry (Keep...     4    77 2000-03-18 #>  5 2 Pac   Baby Don't Cry (Keep...     5    87 2000-03-25 #>  6 2 Pac   Baby Don't Cry (Keep...     6    94 2000-04-01 #>  7 2 Pac   Baby Don't Cry (Keep...     7    99 2000-04-08 #>  8 2Ge+her The Hardest Part Of ...     1    91 2000-09-02 #>  9 2Ge+her The Hardest Part Of ...     2    87 2000-09-09 #> 10 2Ge+her The Hardest Part Of ...     3    92 2000-09-16 #> # ℹ 5,297 more rows billboard3 %>% arrange(artist, track, week) #> # A tibble: 5,307 × 5 #>    artist  track                    week  rank date       #>    <chr>   <chr>                   <int> <dbl> <date>     #>  1 2 Pac   Baby Don't Cry (Keep...     1    87 2000-02-26 #>  2 2 Pac   Baby Don't Cry (Keep...     2    82 2000-03-04 #>  3 2 Pac   Baby Don't Cry (Keep...     3    72 2000-03-11 #>  4 2 Pac   Baby Don't Cry (Keep...     4    77 2000-03-18 #>  5 2 Pac   Baby Don't Cry (Keep...     5    87 2000-03-25 #>  6 2 Pac   Baby Don't Cry (Keep...     6    94 2000-04-01 #>  7 2 Pac   Baby Don't Cry (Keep...     7    99 2000-04-08 #>  8 2Ge+her The Hardest Part Of ...     1    91 2000-09-02 #>  9 2Ge+her The Hardest Part Of ...     2    87 2000-09-09 #> 10 2Ge+her The Hardest Part Of ...     3    92 2000-09-16 #> # ℹ 5,297 more rows billboard3 %>% arrange(date, rank) #> # A tibble: 5,307 × 5 #>    artist   track   week  rank date       #>    <chr>    <chr>  <int> <dbl> <date>     #>  1 Lonestar Amazed     1    81 1999-06-05 #>  2 Lonestar Amazed     2    54 1999-06-12 #>  3 Lonestar Amazed     3    44 1999-06-19 #>  4 Lonestar Amazed     4    39 1999-06-26 #>  5 Lonestar Amazed     5    38 1999-07-03 #>  6 Lonestar Amazed     6    33 1999-07-10 #>  7 Lonestar Amazed     7    29 1999-07-17 #>  8 Amber    Sexual     1    99 1999-07-17 #>  9 Lonestar Amazed     8    29 1999-07-24 #> 10 Amber    Sexual     2    99 1999-07-24 #> # ℹ 5,297 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"multiple-variables-stored-in-one-column","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Multiple variables stored in one column","title":"Tidy data","text":"pivoting columns, key column sometimes combination multiple underlying variable names. happens tb (tuberculosis) dataset, shown . dataset comes World Health Organisation, records counts confirmed tuberculosis cases country, year, demographic group. demographic groups broken sex (m, f) age (0-14, 15-25, 25-34, 35-44, 45-54, 55-64, unknown). First use pivot_longer() gather non-variable columns: Column headers format often separated non-alphanumeric character (e.g. ., -, _, :), fixed width format, like dataset. separate() makes easy split compound variables individual variables. can either pass regular expression split (default split non-alphanumeric columns), vector character positions. case want split first character: Storing values form resolves problem original data. want compare rates, counts, means need know population. original format, easy way add population variable. stored separate table, makes hard correctly match populations counts. tidy form, adding variables population rate easy ’re just additional columns. case, also transformation single step supplying multiple column names names_to also supplying grouped regular expression names_pattern:","code":"tb <- as_tibble(read.csv(\"tb.csv\", stringsAsFactors = FALSE)) tb #> # A tibble: 5,769 × 22 #>    iso2   year   m04  m514  m014 m1524 m2534 m3544 m4554 m5564   m65    mu #>    <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #>  1 AD     1989    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  2 AD     1990    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  3 AD     1991    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  4 AD     1992    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  5 AD     1993    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  6 AD     1994    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  7 AD     1996    NA    NA     0     0     0     4     1     0     0    NA #>  8 AD     1997    NA    NA     0     0     1     2     2     1     6    NA #>  9 AD     1998    NA    NA     0     0     0     1     0     0     0    NA #> 10 AD     1999    NA    NA     0     0     0     1     1     0     0    NA #> # ℹ 5,759 more rows #> # ℹ 10 more variables: f04 <int>, f514 <int>, f014 <int>, f1524 <int>, #> #   f2534 <int>, f3544 <int>, f4554 <int>, f5564 <int>, f65 <int>, #> #   fu <int> tb2 <- tb %>%   pivot_longer(     !c(iso2, year),     names_to = \"demo\",     values_to = \"n\",     values_drop_na = TRUE   ) tb2 #> # A tibble: 35,750 × 4 #>    iso2   year demo      n #>    <chr> <int> <chr> <int> #>  1 AD     1996 m014      0 #>  2 AD     1996 m1524     0 #>  3 AD     1996 m2534     0 #>  4 AD     1996 m3544     4 #>  5 AD     1996 m4554     1 #>  6 AD     1996 m5564     0 #>  7 AD     1996 m65       0 #>  8 AD     1996 f014      0 #>  9 AD     1996 f1524     1 #> 10 AD     1996 f2534     1 #> # ℹ 35,740 more rows tb3 <- tb2 %>%   separate(demo, c(\"sex\", \"age\"), 1) tb3 #> # A tibble: 35,750 × 5 #>    iso2   year sex   age       n #>    <chr> <int> <chr> <chr> <int> #>  1 AD     1996 m     014       0 #>  2 AD     1996 m     1524      0 #>  3 AD     1996 m     2534      0 #>  4 AD     1996 m     3544      4 #>  5 AD     1996 m     4554      1 #>  6 AD     1996 m     5564      0 #>  7 AD     1996 m     65        0 #>  8 AD     1996 f     014       0 #>  9 AD     1996 f     1524      1 #> 10 AD     1996 f     2534      1 #> # ℹ 35,740 more rows tb %>% pivot_longer(   !c(iso2, year),   names_to = c(\"sex\", \"age\"),   names_pattern = \"(.)(.+)\",   values_to = \"n\",   values_drop_na = TRUE ) #> # A tibble: 35,750 × 5 #>    iso2   year sex   age       n #>    <chr> <int> <chr> <chr> <int> #>  1 AD     1996 m     014       0 #>  2 AD     1996 m     1524      0 #>  3 AD     1996 m     2534      0 #>  4 AD     1996 m     3544      4 #>  5 AD     1996 m     4554      1 #>  6 AD     1996 m     5564      0 #>  7 AD     1996 m     65        0 #>  8 AD     1996 f     014       0 #>  9 AD     1996 f     1524      1 #> 10 AD     1996 f     2534      1 #> # ℹ 35,740 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"variables-are-stored-in-both-rows-and-columns","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Variables are stored in both rows and columns","title":"Tidy data","text":"complicated form messy data occurs variables stored rows columns. code loads daily weather data Global Historical Climatology Network one weather station (MX17004) Mexico five months 2010. variables individual columns (id, year, month), spread across columns (day, d1-d31) across rows (tmin, tmax) (minimum maximum temperature). Months fewer 31 days structural missing values last day(s) month. tidy dataset first use pivot_longer gather day columns: presentation, ’ve dropped missing values, making implicit rather explicit. ok know many days month can easily reconstruct explicit missing values. ’ll also little cleaning: dataset mostly tidy, element column variable; stores names variables. (shown example meteorological variables prcp (precipitation) snow (snowfall)). Fixing requires widening data: pivot_wider() inverse pivot_longer(), pivoting element value back across multiple columns: form tidy: ’s one variable column, row represents one day.","code":"weather <- as_tibble(read.csv(\"weather.csv\", stringsAsFactors = FALSE)) weather #> # A tibble: 22 × 35 #>    id       year month element    d1    d2    d3    d4    d5    d6    d7 #>    <chr>   <int> <int> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 MX17004  2010     1 tmax       NA  NA    NA      NA  NA      NA    NA #>  2 MX17004  2010     1 tmin       NA  NA    NA      NA  NA      NA    NA #>  3 MX17004  2010     2 tmax       NA  27.3  24.1    NA  NA      NA    NA #>  4 MX17004  2010     2 tmin       NA  14.4  14.4    NA  NA      NA    NA #>  5 MX17004  2010     3 tmax       NA  NA    NA      NA  32.1    NA    NA #>  6 MX17004  2010     3 tmin       NA  NA    NA      NA  14.2    NA    NA #>  7 MX17004  2010     4 tmax       NA  NA    NA      NA  NA      NA    NA #>  8 MX17004  2010     4 tmin       NA  NA    NA      NA  NA      NA    NA #>  9 MX17004  2010     5 tmax       NA  NA    NA      NA  NA      NA    NA #> 10 MX17004  2010     5 tmin       NA  NA    NA      NA  NA      NA    NA #> # ℹ 12 more rows #> # ℹ 24 more variables: d8 <dbl>, d9 <lgl>, d10 <dbl>, d11 <dbl>, #> #   d12 <lgl>, d13 <dbl>, d14 <dbl>, d15 <dbl>, d16 <dbl>, d17 <dbl>, #> #   d18 <lgl>, d19 <lgl>, d20 <lgl>, d21 <lgl>, d22 <lgl>, d23 <dbl>, #> #   d24 <lgl>, d25 <dbl>, d26 <dbl>, d27 <dbl>, d28 <dbl>, d29 <dbl>, #> #   d30 <dbl>, d31 <dbl> weather2 <- weather %>%   pivot_longer(     d1:d31,     names_to = \"day\",     values_to = \"value\",     values_drop_na = TRUE   ) weather2 #> # A tibble: 66 × 6 #>    id       year month element day   value #>    <chr>   <int> <int> <chr>   <chr> <dbl> #>  1 MX17004  2010     1 tmax    d30    27.8 #>  2 MX17004  2010     1 tmin    d30    14.5 #>  3 MX17004  2010     2 tmax    d2     27.3 #>  4 MX17004  2010     2 tmax    d3     24.1 #>  5 MX17004  2010     2 tmax    d11    29.7 #>  6 MX17004  2010     2 tmax    d23    29.9 #>  7 MX17004  2010     2 tmin    d2     14.4 #>  8 MX17004  2010     2 tmin    d3     14.4 #>  9 MX17004  2010     2 tmin    d11    13.4 #> 10 MX17004  2010     2 tmin    d23    10.7 #> # ℹ 56 more rows weather3 <- weather2 %>%   mutate(day = as.integer(gsub(\"d\", \"\", day))) %>%   select(id, year, month, day, element, value) weather3 #> # A tibble: 66 × 6 #>    id       year month   day element value #>    <chr>   <int> <int> <int> <chr>   <dbl> #>  1 MX17004  2010     1    30 tmax     27.8 #>  2 MX17004  2010     1    30 tmin     14.5 #>  3 MX17004  2010     2     2 tmax     27.3 #>  4 MX17004  2010     2     3 tmax     24.1 #>  5 MX17004  2010     2    11 tmax     29.7 #>  6 MX17004  2010     2    23 tmax     29.9 #>  7 MX17004  2010     2     2 tmin     14.4 #>  8 MX17004  2010     2     3 tmin     14.4 #>  9 MX17004  2010     2    11 tmin     13.4 #> 10 MX17004  2010     2    23 tmin     10.7 #> # ℹ 56 more rows weather3 %>%   pivot_wider(names_from = element, values_from = value) #> # A tibble: 33 × 6 #>    id       year month   day  tmax  tmin #>    <chr>   <int> <int> <int> <dbl> <dbl> #>  1 MX17004  2010     1    30  27.8  14.5 #>  2 MX17004  2010     2     2  27.3  14.4 #>  3 MX17004  2010     2     3  24.1  14.4 #>  4 MX17004  2010     2    11  29.7  13.4 #>  5 MX17004  2010     2    23  29.9  10.7 #>  6 MX17004  2010     3     5  32.1  14.2 #>  7 MX17004  2010     3    10  34.5  16.8 #>  8 MX17004  2010     3    16  31.1  17.6 #>  9 MX17004  2010     4    27  36.3  16.7 #> 10 MX17004  2010     5    27  33.2  18.2 #> # ℹ 23 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"multiple-types","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Multiple types in one table","title":"Tidy data","text":"Datasets often involve values collected multiple levels, different types observational units. tidying, type observational unit stored table. closely related idea database normalisation, fact expressed one place. ’s important otherwise inconsistencies can arise. billboard dataset actually contains observations two types observational units: song rank week. manifests duplication facts song: artist repeated many times. dataset needs broken two pieces: song dataset stores artist song name, ranking dataset gives rank song week. first extract song dataset: use make rank dataset replacing repeated song facts pointer song details (unique song id): also imagine week dataset record background information week, maybe total number songs sold similar “demographic” information. Normalisation useful tidying eliminating inconsistencies. However, data analysis tools work directly relational data, analysis usually also requires denormalisation merging datasets back one table.","code":"song <- billboard3 %>%   distinct(artist, track) %>%   mutate(song_id = row_number()) song #> # A tibble: 317 × 3 #>    artist         track                   song_id #>    <chr>          <chr>                     <int> #>  1 2 Pac          Baby Don't Cry (Keep...       1 #>  2 2Ge+her        The Hardest Part Of ...       2 #>  3 3 Doors Down   Kryptonite                    3 #>  4 3 Doors Down   Loser                         4 #>  5 504 Boyz       Wobble Wobble                 5 #>  6 98^0           Give Me Just One Nig...       6 #>  7 A*Teens        Dancing Queen                 7 #>  8 Aaliyah        I Don't Wanna                 8 #>  9 Aaliyah        Try Again                     9 #> 10 Adams, Yolanda Open My Heart                10 #> # ℹ 307 more rows rank <- billboard3 %>%   left_join(song, c(\"artist\", \"track\")) %>%   select(song_id, date, week, rank) rank #> # A tibble: 5,307 × 4 #>    song_id date        week  rank #>      <int> <date>     <int> <dbl> #>  1       1 2000-02-26     1    87 #>  2       1 2000-03-04     2    82 #>  3       1 2000-03-11     3    72 #>  4       1 2000-03-18     4    77 #>  5       1 2000-03-25     5    87 #>  6       1 2000-04-01     6    94 #>  7       1 2000-04-08     7    99 #>  8       2 2000-09-02     1    91 #>  9       2 2000-09-09     2    87 #> 10       2 2000-09-16     3    92 #> # ℹ 5,297 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"one-type-in-multiple-tables","dir":"Articles","previous_headings":"Tidying messy datasets","what":"One type in multiple tables","title":"Tidy data","text":"’s also common find data values single type observational unit spread multiple tables files. tables files often split another variable, represents single year, person, location. long format individual records consistent, easy problem fix: Read files list tables. table, add new column records original file name (file name often value important variable). Combine tables single table. Purrr makes straightforward R. following code generates vector file names directory (data/) match regular expression (ends .csv). Next name element vector name file. preserve names following step, ensuring row final data frame labeled source. Finally, map_dfr() loops path, reading csv file combining results single data frame. single table, can perform additional tidying needed. example type cleaning can found https://github.com/hadley/data-baby-names takes 129 yearly baby name tables provided US Social Security Administration combines single file. complicated situation occurs dataset structure changes time. example, datasets may contain different variables, variables different names, different file formats, different conventions missing values. may require tidy file individually (, ’re lucky, small groups) combine tidied. example type tidying illustrated https://github.com/hadley/data-fuel-economy, shows tidying epa fuel economy data 50,000 cars 1978 2008. raw data available online, year stored separate file four major formats many minor variations, making tidying dataset considerable challenge.","code":"library(purrr) paths <- dir(\"data\", pattern = \"\\\\.csv$\", full.names = TRUE) names(paths) <- basename(paths) map_dfr(paths, read.csv, stringsAsFactors = FALSE, .id = \"filename\")"},{"path":"https://tidyr.tidyverse.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Hadley Wickham. Author, maintainer. Davis Vaughan. Author. Maximilian Girlich. Author. Kevin Ushey. Contributor. . Copyright holder, funder.","code":""},{"path":"https://tidyr.tidyverse.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy Data. R package version 1.3.1.9000, https://github.com/tidyverse/tidyr, https://tidyr.tidyverse.org.","code":"@Manual{,   title = {tidyr: Tidy Messy Data},   author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich},   year = {2024},   note = {R package version 1.3.1.9000, https://github.com/tidyverse/tidyr},   url = {https://tidyr.tidyverse.org}, }"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Tidy Messy Data","text":"goal tidyr help create tidy data. Tidy data data : variable column; column variable. observation row; row observation. value cell; cell single value. Tidy data describes standard way storing data used wherever possible throughout tidyverse. ensure data tidy, ’ll spend less time fighting tools time working analysis. Learn tidy data vignette(\"tidy-data\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tidy Messy Data","text":"","code":"# The easiest way to get tidyr is to install the whole tidyverse: install.packages(\"tidyverse\")  # Alternatively, install just tidyr: install.packages(\"tidyr\")  # Or the development version from GitHub: # install.packages(\"pak\") pak::pak(\"tidyverse/tidyr\")"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Tidy Messy Data","text":"tidyr functions fall five main categories: “Pivoting” converts long wide forms. tidyr 1.0.0 introduces pivot_longer() pivot_wider(), replacing older spread() gather() functions. See vignette(\"pivot\") details. “Rectangling”, turns deeply nested lists (JSON) tidy tibbles. See unnest_longer(), unnest_wider(), hoist(), vignette(\"rectangle\") details. Nesting converts grouped data form group becomes single row containing nested data frame, unnesting opposite. See nest(), unnest(), vignette(\"nest\") details. Splitting combining character columns. Use separate_wider_delim(), separate_wider_position(), separate_wider_regex() pull single character column multiple columns; use unite() combine multiple columns single character column. Make implicit missing values explicit complete(); make explicit missing values implicit drop_na(); replace missing values next/previous value fill(), known value replace_na().","code":"library(tidyr)"},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Tidy Messy Data","text":"tidyr supersedes reshape2 (2010-2014) reshape (2005-2010). Somewhat counterintuitively, iteration package done less. tidyr designed specifically tidying data, general reshaping (reshape2), general aggregation (reshape). data.table provides high-performance implementations melt() dcast() ’d like read data reshaping CS perspective, ’d recommend following three papers: Wrangler: Interactive visual specification data transformation scripts interactive framework data cleaning (Potter’s wheel) efficiently implementing SchemaSQL SQL database system guide reading, ’s translation terminology used different places:","code":""},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting help","title":"Tidy Messy Data","text":"encounter clear bug, please file minimal reproducible example github. questions discussion, please use forum.posit.co. Please note tidyr project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Song rankings for Billboard top 100 in the year 2000 — billboard","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"Song rankings Billboard top 100 year 2000","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"","code":"billboard"},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"dataset variables: artist Artist name track Song name date.enter Date song entered top 100 wk1 – wk76 Rank song week entered","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"\"Whitburn\" project, https://waxy.org/2008/05/the_whitburn_project/, (downloaded April 2008)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Check assumptions about a pivot spec — check_pivot_spec","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"check_pivot_spec() developer facing helper function validating pivot spec used pivot_longer_spec() pivot_wider_spec(). useful extending pivot_longer() pivot_wider() new S3 methods. check_pivot_spec() makes following assertions: spec must data frame. spec must character column named .name. spec must character column named .value. .name column must unique. .name .value columns must first two columns data frame, reordered true.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"","code":"check_pivot_spec(spec, call = caller_env())"},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"spec specification data frame. useful complex pivots gives greater control metadata stored columns become column names result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"","code":"# A valid spec spec <- tibble(.name = \"a\", .value = \"b\", foo = 1) check_pivot_spec(spec) #> # A tibble: 1 × 3 #>   .name .value   foo #>   <chr> <chr>  <dbl> #> 1 a     b          1  spec <- tibble(.name = \"a\") try(check_pivot_spec(spec)) #> Error in eval(expr, envir, enclos) :  #>   `spec` must have `.name` and `.value` columns.  # `.name` and `.value` are forced to be the first two columns spec <- tibble(foo = 1, .value = \"b\", .name = \"a\") check_pivot_spec(spec) #> # A tibble: 1 × 3 #>   .name .value   foo #>   <chr> <chr>  <dbl> #> 1 a     b          1"},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":null,"dir":"Reference","previous_headings":"","what":"Chop and unchop — chop","title":"Chop and unchop — chop","text":"Chopping unchopping preserve width data frame, changing length. chop() makes df shorter converting rows within group list-columns. unchop() makes df longer expanding list-columns element list-column gets row output. chop() unchop() building blocks complicated functions (like unnest(), unnest_longer(), unnest_wider()) generally suitable programming interactive data analysis.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chop and unchop — chop","text":"","code":"chop(data, cols, ..., error_call = current_env())  unchop(   data,   cols,   ...,   keep_empty = FALSE,   ptype = NULL,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chop and unchop — chop","text":"data data frame. cols <tidy-select> Columns chop unchop. unchop(), column list-column containing generalised vectors (e.g. mix NULLs, atomic vector, S3 vectors, lists, data frames). ... dots future extensions must empty. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. ptype Optionally, named list column name-prototype pairs coerce cols , overriding default guessed combining individual values. Alternatively, single empty ptype can supplied, applied cols.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Chop and unchop — chop","text":"Generally, unchopping useful chopping simplifies complex data structure, nest()ing usually appropriate chop()ing since better preserves connections observations. chop() creates list-columns class vctrs::list_of() ensure consistent behaviour chopped data frame emptied. instance helps getting back original column types roundtrip chop unchop. <list_of> keeps tracks type elements, unchop() able reconstitute correct vector type even empty list-columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Chop and unchop — chop","text":"","code":"# Chop ---------------------------------------------------------------------- df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1) # Note that we get one row of output for each unique combination of # non-chopped variables df %>% chop(c(y, z)) #> # A tibble: 3 × 3 #>       x           y           z #>   <dbl> <list<int>> <list<int>> #> 1     1         [3]         [3] #> 2     2         [2]         [2] #> 3     3         [1]         [1] # cf nest df %>% nest(data = c(y, z)) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Unchop -------------------------------------------------------------------- df <- tibble(x = 1:4, y = list(integer(), 1L, 1:2, 1:3)) df %>% unchop(y) #> # A tibble: 6 × 2 #>       x     y #>   <int> <int> #> 1     2     1 #> 2     3     1 #> 3     3     2 #> 4     4     1 #> 5     4     2 #> 6     4     3 df %>% unchop(y, keep_empty = TRUE) #> # A tibble: 7 × 2 #>       x     y #>   <int> <int> #> 1     1    NA #> 2     2     1 #> 3     3     1 #> 4     3     2 #> 5     4     1 #> 6     4     2 #> 7     4     3  # unchop will error if the types are not compatible: df <- tibble(x = 1:2, y = list(\"1\", 1:3)) try(df %>% unchop(y)) #> Error in list_unchop(col, ptype = col_ptype) :  #>   Can't combine `x[[1]]` <character> and `x[[2]]` <integer>.  # Unchopping a list-col of data frames must generate a df-col because # unchop leaves the column names unchanged df <- tibble(x = 1:3, y = list(NULL, tibble(x = 1), tibble(y = 1:2))) df %>% unchop(y) #> # A tibble: 3 × 2 #>       x   y$x    $y #>   <int> <dbl> <int> #> 1     2     1    NA #> 2     3    NA     1 #> 3     3    NA     2 df %>% unchop(y, keep_empty = TRUE) #> # A tibble: 4 × 2 #>       x   y$x    $y #>   <int> <dbl> <int> #> 1     1    NA    NA #> 2     2     1    NA #> 3     3    NA     1 #> 4     3    NA     2"},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"Two datasets public data provided Centers Medicare & Medicaid Services, https://data.cms.gov. cms_patient_experience contains lightly cleaned data \"Hospice - Provider Data\", provides list hospice agencies along data quality patient care, https://data.cms.gov/provider-data/dataset/252m-zfp9. cms_patient_care \"Doctors Clinicians Quality Payment Program PY 2020 Virtual Group Public Reporting\", https://data.cms.gov/provider-data/dataset/8c70-d353","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"","code":"cms_patient_experience  cms_patient_care"},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"cms_patient_experience data frame 500 observations five variables: org_pac_id,org_nm Organisation ID name measure_cd,measure_title Measure code title prf_rate Measure performance rate cms_patient_care data frame 252 observations five variables: ccn,facility_name Facility ID name measure_abbr Abbreviated measurement title, suitable use variable name score Measure score type Whether score refers rating 100 (\"observed\"), maximum possible value raw score (\"denominator\")","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"","code":"cms_patient_experience %>%   dplyr::distinct(measure_cd, measure_title) #> # A tibble: 6 × 2 #>   measure_cd   measure_title                                               #>   <chr>        <chr>                                                       #> 1 CAHPS_GRP_1  CAHPS for MIPS SSM: Getting Timely Care, Appointments, and… #> 2 CAHPS_GRP_2  CAHPS for MIPS SSM: How Well Providers Communicate          #> 3 CAHPS_GRP_3  CAHPS for MIPS SSM: Patient's Rating of Provider            #> 4 CAHPS_GRP_5  CAHPS for MIPS SSM: Health Promotion and Education          #> 5 CAHPS_GRP_8  CAHPS for MIPS SSM: Courteous and Helpful Office Staff      #> 6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources         cms_patient_experience %>%   pivot_wider(     id_cols = starts_with(\"org\"),     names_from = measure_cd,     values_from = prf_rate  ) #> # A tibble: 95 × 8 #>    org_pac_id org_nm       CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 #>    <chr>      <chr>              <dbl>       <dbl>       <dbl>       <dbl> #>  1 0446157747 USC CARE ME…          63          87          86          57 #>  2 0446162697 ASSOCIATION…          59          85          83          63 #>  3 0547164295 BEAVER MEDI…          49          NA          75          44 #>  4 0749333730 CAPE PHYSIC…          67          84          85          65 #>  5 0840104360 ALLIANCE PH…          66          87          87          64 #>  6 0840109864 REX HOSPITA…          73          87          84          67 #>  7 0840513552 SCL HEALTH …          58          83          76          58 #>  8 0941545784 GRITMAN MED…          46          86          81          54 #>  9 1052612785 COMMUNITY M…          65          84          80          58 #> 10 1254237779 OUR LADY OF…          61          NA          NA          65 #> # ℹ 85 more rows #> # ℹ 2 more variables: CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>  cms_patient_care %>%   pivot_wider(     names_from = type,     values_from = score   ) #> # A tibble: 126 × 5 #>    ccn    facility_name                  measure_abbr denominator observed #>    <chr>  <chr>                          <chr>              <dbl>    <dbl> #>  1 011500 BAPTIST HOSPICE                beliefs_add…         202    100   #>  2 011500 BAPTIST HOSPICE                composite_p…         202     88.1 #>  3 011500 BAPTIST HOSPICE                dyspena_tre…         110     99.1 #>  4 011500 BAPTIST HOSPICE                dyspnea_scr…         202    100   #>  5 011500 BAPTIST HOSPICE                opioid_bowel          61    100   #>  6 011500 BAPTIST HOSPICE                pain_assess…         107    100   #>  7 011500 BAPTIST HOSPICE                pain_screen…         202     88.6 #>  8 011500 BAPTIST HOSPICE                treat_pref           202    100   #>  9 011500 BAPTIST HOSPICE                visits_immi…         232     96.1 #> 10 011501 SOUTHERNCARE NEW BEACON N. BI… beliefs_add…         525    100   #> # ℹ 116 more rows  cms_patient_care %>%   pivot_wider(     names_from = measure_abbr,     values_from = score   ) #> # A tibble: 28 × 12 #>    ccn    facility_name          type  beliefs_addressed composite_process #>    <chr>  <chr>                  <chr>             <dbl>             <dbl> #>  1 011500 BAPTIST HOSPICE        deno…             202               202   #>  2 011500 BAPTIST HOSPICE        obse…             100                88.1 #>  3 011501 SOUTHERNCARE NEW BEAC… deno…             525               525   #>  4 011501 SOUTHERNCARE NEW BEAC… obse…             100               100   #>  5 011502 COMFORT CARE COASTAL … deno…             295               295   #>  6 011502 COMFORT CARE COASTAL … obse…             100                99.3 #>  7 011503 SAAD HOSPICE SERVICES  deno…             694               694   #>  8 011503 SAAD HOSPICE SERVICES  obse…              99.9              96   #>  9 011505 HOSPICE FAMILY CARE    deno…             600               600   #> 10 011505 HOSPICE FAMILY CARE    obse…              97.8              92   #> # ℹ 18 more rows #> # ℹ 7 more variables: dyspena_treatment <dbl>, dyspnea_screening <dbl>, #> #   opioid_bowel <dbl>, pain_assessment <dbl>, pain_screening <dbl>, #> #   treat_pref <dbl>, visits_imminent <dbl>  cms_patient_care %>%   pivot_wider(     names_from = c(measure_abbr, type),     values_from = score   ) #> # A tibble: 14 × 20 #>    ccn    facility_name      beliefs_addressed_de…¹ beliefs_addressed_ob…² #>    <chr>  <chr>                               <dbl>                  <dbl> #>  1 011500 BAPTIST HOSPICE                       202                  100   #>  2 011501 SOUTHERNCARE NEW …                    525                  100   #>  3 011502 COMFORT CARE COAS…                    295                  100   #>  4 011503 SAAD HOSPICE SERV…                    694                   99.9 #>  5 011505 HOSPICE FAMILY CA…                    600                   97.8 #>  6 011506 SOUTHERNCARE NEW …                    589                  100   #>  7 011508 SOUTHERNCARE NEW …                    420                  100   #>  8 011510 CULLMAN REGIONAL …                     54                  100   #>  9 011511 HOSPICE OF THE VA…                    179                  100   #> 10 011512 SOUTHERNCARE NEW …                    396                  100   #> 11 011513 SHEPHERD'S COVE H…                    335                   99.1 #> 12 011514 ST VINCENT'S HOSP…                    210                  100   #> 13 011516 HOSPICE OF LIMEST…                    103                  100   #> 14 011517 HOSPICE OF WEST A…                    400                   99.8 #> # ℹ abbreviated names: ¹​beliefs_addressed_denominator, #> #   ²​beliefs_addressed_observed #> # ℹ 16 more variables: composite_process_denominator <dbl>, #> #   composite_process_observed <dbl>, #> #   dyspena_treatment_denominator <dbl>, #> #   dyspena_treatment_observed <dbl>, #> #   dyspnea_screening_denominator <dbl>, …"},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":null,"dir":"Reference","previous_headings":"","what":"Complete a data frame with missing combinations of data — complete","title":"Complete a data frame with missing combinations of data — complete","text":"Turns implicit missing values explicit missing values. wrapper around expand(), dplyr::full_join() replace_na() useful completing missing combinations data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Complete a data frame with missing combinations of data — complete","text":"","code":"complete(data, ..., fill = list(), explicit = TRUE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Complete a data frame with missing combinations of data — complete","text":"data data frame. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). fill named list variable supplies single value use instead NA missing combinations. explicit implicit (newly created) explicit (pre-existing) missing values filled fill? default, TRUE, set FALSE limit fill implicit missing values.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Complete a data frame with missing combinations of data — complete","text":"grouped data frames created dplyr::group_by(), complete() operates within group. , complete grouping column.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Complete a data frame with missing combinations of data — complete","text":"","code":"df <- tibble(   group = c(1:2, 1, 2),   item_id = c(1:2, 2, 3),   item_name = c(\"a\", \"a\", \"b\", \"b\"),   value1 = c(1, NA, 3, 4),   value2 = 4:7 ) df #> # A tibble: 4 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     2       2 a             NA      5 #> 3     1       2 b              3      6 #> 4     2       3 b              4      7  # Combinations -------------------------------------------------------------- # Generate all possible combinations of `group`, `item_id`, and `item_name` # (whether or not they appear in the data) df %>% complete(group, item_id, item_name) #> # A tibble: 12 × 5 #>    group item_id item_name value1 value2 #>    <dbl>   <dbl> <chr>      <dbl>  <int> #>  1     1       1 a              1      4 #>  2     1       1 b             NA     NA #>  3     1       2 a             NA     NA #>  4     1       2 b              3      6 #>  5     1       3 a             NA     NA #>  6     1       3 b             NA     NA #>  7     2       1 a             NA     NA #>  8     2       1 b             NA     NA #>  9     2       2 a             NA      5 #> 10     2       2 b             NA     NA #> 11     2       3 a             NA     NA #> 12     2       3 b              4      7  # Cross all possible `group` values with the unique pairs of # `(item_id, item_name)` that already exist in the data df %>% complete(group, nesting(item_id, item_name)) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a             NA     NA #> 3     1       2 b              3      6 #> 4     1       3 b             NA     NA #> 5     2       1 a             NA     NA #> 6     2       2 a             NA      5 #> 7     2       2 b             NA     NA #> 8     2       3 b              4      7  # Within each `group`, generate all possible combinations of # `item_id` and `item_name` that occur in that group df %>%   dplyr::group_by(group) %>%   complete(item_id, item_name) #> # A tibble: 8 × 5 #> # Groups:   group [2] #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       1 b             NA     NA #> 3     1       2 a             NA     NA #> 4     1       2 b              3      6 #> 5     2       2 a             NA      5 #> 6     2       2 b             NA     NA #> 7     2       3 a             NA     NA #> 8     2       3 b              4      7  # Supplying values for new rows --------------------------------------------- # Use `fill` to replace NAs with some value. By default, affects both new # (implicit) and pre-existing (explicit) missing values. df %>%   complete(     group,     nesting(item_id, item_name),     fill = list(value1 = 0, value2 = 99)   ) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a              0     99 #> 3     1       2 b              3      6 #> 4     1       3 b              0     99 #> 5     2       1 a              0     99 #> 6     2       2 a              0      5 #> 7     2       2 b              0     99 #> 8     2       3 b              4      7  # Limit the fill to only the newly created (i.e. previously implicit) # missing values with `explicit = FALSE` df %>%   complete(     group,     nesting(item_id, item_name),     fill = list(value1 = 0, value2 = 99),     explicit = FALSE   ) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a              0     99 #> 3     1       2 b              3      6 #> 4     1       3 b              0     99 #> 5     2       1 a              0     99 #> 6     2       2 a             NA      5 #> 7     2       2 b              0     99 #> 8     2       3 b              4      7"},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":null,"dir":"Reference","previous_headings":"","what":"Completed construction in the US in 2018 — construction","title":"Completed construction in the US in 2018 — construction","text":"Completed construction US 2018","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Completed construction in the US in 2018 — construction","text":"","code":"construction"},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Completed construction in the US in 2018 — construction","text":"dataset variables: Year,Month Record date 1 unit, 2 4 units, 5 units mote Number completed units size Northeast,Midwest,South,West Number completed units region","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Completed construction in the US in 2018 — construction","text":"Completions \"New Residential Construction\" found Table 5 https://www.census.gov/construction/nrc/xls/newresconst.xls (downloaded March 2019)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated SE versions of main verbs — deprecated-se","title":"Deprecated SE versions of main verbs — deprecated-se","text":"tidyr used offer twin versions verb suffixed underscore. versions standard evaluation (SE) semantics: rather taking arguments code, like NSE verbs, took arguments value. purpose make possible program tidyr. However, tidyr now uses tidy evaluation semantics. NSE verbs still capture arguments, can now unquote parts arguments. offers full programmability NSE verbs. Thus, underscored versions now superfluous. Unquoting triggers immediate evaluation operand inlines result within captured expression. result can value expression evaluated later rest argument. See vignette(\"programming\", \"dplyr\") information.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated SE versions of main verbs — deprecated-se","text":"","code":"complete_(data, cols, fill = list(), ...)  drop_na_(data, vars)  expand_(data, dots, ...)  crossing_(x)  nesting_(x)  extract_(   data,   col,   into,   regex = \"([[:alnum:]]+)\",   remove = TRUE,   convert = FALSE,   ... )  fill_(data, fill_cols, .direction = c(\"down\", \"up\"))  gather_(   data,   key_col,   value_col,   gather_cols,   na.rm = FALSE,   convert = FALSE,   factor_key = FALSE )  nest_(...)  separate_rows_(data, cols, sep = \"[^[:alnum:].]+\", convert = FALSE)  separate_(   data,   col,   into,   sep = \"[^[:alnum:]]+\",   remove = TRUE,   convert = FALSE,   extra = \"warn\",   fill = \"warn\",   ... )  spread_(   data,   key_col,   value_col,   fill = NA,   convert = FALSE,   drop = TRUE,   sep = NULL )  unite_(data, col, from, sep = \"_\", remove = TRUE)  unnest_(...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated SE versions of main verbs — deprecated-se","text":"data data frame fill named list variable supplies single value use instead NA missing combinations. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). vars, cols, col Name columns. x nesting_ crossing_ list variables. Names new variables create character vector. Use NA omit variable output. regex string representing regular expression used extract desired values. one group (defined ()) element . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. fill_cols Character vector column names. .direction Direction fill missing values. Currently either \"\" (default), \"\", \"downup\" (.e. first ) \"updown\" (first ). key_col, value_col Strings giving names key value cols. gather_cols Character vector giving column names gathered pair key-value columns. na.rm TRUE, remove rows output value column NA. factor_key FALSE, default, key values stored character vector. TRUE, stored factor, preserves original ordering columns. sep Separator delimiting collapsed values. extra sep character vector, controls happens many pieces. three valid options: \"warn\" (default): emit warning drop extra values. \"drop\": drop extra values without warning. \"merge\": splits length() times drop FALSE, keep factor levels appear data, filling missing combinations fill. Names existing columns character vector","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop rows containing missing values — drop_na","title":"Drop rows containing missing values — drop_na","text":"drop_na() drops rows column specified ... contains missing value.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop rows containing missing values — drop_na","text":"","code":"drop_na(data, ...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop rows containing missing values — drop_na","text":"data data frame. ... <tidy-select> Columns inspect missing values. empty, columns used.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop rows containing missing values — drop_na","text":"Another way interpret drop_na() keeps \"complete\" rows (rows contain missing values). Internally, completeness computed vctrs::vec_detect_complete().","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop rows containing missing values — drop_na","text":"","code":"df <- tibble(x = c(1, 2, NA), y = c(\"a\", NA, \"b\")) df %>% drop_na() #> # A tibble: 1 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     df %>% drop_na(x) #> # A tibble: 2 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     #> 2     2 NA     vars <- \"y\" df %>% drop_na(x, any_of(vars)) #> # A tibble: 1 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand data frame to include all possible combinations of values — expand","title":"Expand data frame to include all possible combinations of values — expand","text":"expand() generates combination variables found dataset. paired nesting() crossing() helpers. crossing() wrapper around expand_grid() de-duplicates sorts inputs; nesting() helper finds combinations already present data. expand() often useful conjunction joins: use right_join() convert implicit missing values explicit missing values (e.g., fill gaps data frame). use anti_join() figure combinations missing (e.g., identify gaps data frame).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand data frame to include all possible combinations of values — expand","text":"","code":"expand(data, ..., .name_repair = \"check_unique\")  crossing(..., .name_repair = \"check_unique\")  nesting(..., .name_repair = \"check_unique\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand data frame to include all possible combinations of values — expand","text":"data data frame. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Expand data frame to include all possible combinations of values — expand","text":"grouped data frames created dplyr::group_by(), expand() operates within group. , expand grouping column.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand data frame to include all possible combinations of values — expand","text":"","code":"# Finding combinations ------------------------------------------------------ fruits <- tibble(   type = c(\"apple\", \"orange\", \"apple\", \"orange\", \"orange\", \"orange\"),   year = c(2010, 2010, 2012, 2010, 2011, 2012),   size = factor(     c(\"XS\", \"S\", \"M\", \"S\", \"S\", \"M\"),     levels = c(\"XS\", \"S\", \"M\", \"L\")   ),   weights = rnorm(6, as.numeric(size) + 2) )  # All combinations, including factor levels that are not used fruits %>% expand(type) #> # A tibble: 2 × 1 #>   type   #>   <chr>  #> 1 apple  #> 2 orange fruits %>% expand(size) #> # A tibble: 4 × 1 #>   size  #>   <fct> #> 1 XS    #> 2 S     #> 3 M     #> 4 L     fruits %>% expand(type, size) #> # A tibble: 8 × 2 #>   type   size  #>   <chr>  <fct> #> 1 apple  XS    #> 2 apple  S     #> 3 apple  M     #> 4 apple  L     #> 5 orange XS    #> 6 orange S     #> 7 orange M     #> 8 orange L     fruits %>% expand(type, size, year) #> # A tibble: 24 × 3 #>    type  size   year #>    <chr> <fct> <dbl> #>  1 apple XS     2010 #>  2 apple XS     2011 #>  3 apple XS     2012 #>  4 apple S      2010 #>  5 apple S      2011 #>  6 apple S      2012 #>  7 apple M      2010 #>  8 apple M      2011 #>  9 apple M      2012 #> 10 apple L      2010 #> # ℹ 14 more rows  # Only combinations that already appear in the data fruits %>% expand(nesting(type)) #> # A tibble: 2 × 1 #>   type   #>   <chr>  #> 1 apple  #> 2 orange fruits %>% expand(nesting(size)) #> # A tibble: 3 × 1 #>   size  #>   <fct> #> 1 XS    #> 2 S     #> 3 M     fruits %>% expand(nesting(type, size)) #> # A tibble: 4 × 2 #>   type   size  #>   <chr>  <fct> #> 1 apple  XS    #> 2 apple  M     #> 3 orange S     #> 4 orange M     fruits %>% expand(nesting(type, size, year)) #> # A tibble: 5 × 3 #>   type   size   year #>   <chr>  <fct> <dbl> #> 1 apple  XS     2010 #> 2 apple  M      2012 #> 3 orange S      2010 #> 4 orange S      2011 #> 5 orange M      2012  # Other uses ---------------------------------------------------------------- # Use with `full_seq()` to fill in values of continuous variables fruits %>% expand(type, size, full_seq(year, 1)) #> # A tibble: 24 × 3 #>    type  size  `full_seq(year, 1)` #>    <chr> <fct>               <dbl> #>  1 apple XS                   2010 #>  2 apple XS                   2011 #>  3 apple XS                   2012 #>  4 apple S                    2010 #>  5 apple S                    2011 #>  6 apple S                    2012 #>  7 apple M                    2010 #>  8 apple M                    2011 #>  9 apple M                    2012 #> 10 apple L                    2010 #> # ℹ 14 more rows fruits %>% expand(type, size, 2010:2013) #> # A tibble: 32 × 3 #>    type  size  `2010:2013` #>    <chr> <fct>       <int> #>  1 apple XS           2010 #>  2 apple XS           2011 #>  3 apple XS           2012 #>  4 apple XS           2013 #>  5 apple S            2010 #>  6 apple S            2011 #>  7 apple S            2012 #>  8 apple S            2013 #>  9 apple M            2010 #> 10 apple M            2011 #> # ℹ 22 more rows  # Use `anti_join()` to determine which observations are missing all <- fruits %>% expand(type, size, year) all #> # A tibble: 24 × 3 #>    type  size   year #>    <chr> <fct> <dbl> #>  1 apple XS     2010 #>  2 apple XS     2011 #>  3 apple XS     2012 #>  4 apple S      2010 #>  5 apple S      2011 #>  6 apple S      2012 #>  7 apple M      2010 #>  8 apple M      2011 #>  9 apple M      2012 #> 10 apple L      2010 #> # ℹ 14 more rows all %>% dplyr::anti_join(fruits) #> Joining with `by = join_by(type, size, year)` #> # A tibble: 19 × 3 #>    type   size   year #>    <chr>  <fct> <dbl> #>  1 apple  XS     2011 #>  2 apple  XS     2012 #>  3 apple  S      2010 #>  4 apple  S      2011 #>  5 apple  S      2012 #>  6 apple  M      2010 #>  7 apple  M      2011 #>  8 apple  L      2010 #>  9 apple  L      2011 #> 10 apple  L      2012 #> 11 orange XS     2010 #> 12 orange XS     2011 #> 13 orange XS     2012 #> 14 orange S      2012 #> 15 orange M      2010 #> 16 orange M      2011 #> 17 orange L      2010 #> 18 orange L      2011 #> 19 orange L      2012  # Use with `right_join()` to fill in missing rows (like `complete()`) fruits %>% dplyr::right_join(all) #> Joining with `by = join_by(type, year, size)` #> # A tibble: 25 × 4 #>    type    year size  weights #>    <chr>  <dbl> <fct>   <dbl> #>  1 apple   2010 XS       1.60 #>  2 orange  2010 S        4.26 #>  3 apple   2012 M        2.56 #>  4 orange  2010 S        3.99 #>  5 orange  2011 S        4.62 #>  6 orange  2012 M        6.15 #>  7 apple   2011 XS      NA    #>  8 apple   2012 XS      NA    #>  9 apple   2010 S       NA    #> 10 apple   2011 S       NA    #> # ℹ 15 more rows  # Use with `group_by()` to expand within each group fruits %>%   dplyr::group_by(type) %>%   expand(year, size) #> # A tibble: 20 × 3 #> # Groups:   type [2] #>    type    year size  #>    <chr>  <dbl> <fct> #>  1 apple   2010 XS    #>  2 apple   2010 S     #>  3 apple   2010 M     #>  4 apple   2010 L     #>  5 apple   2012 XS    #>  6 apple   2012 S     #>  7 apple   2012 M     #>  8 apple   2012 L     #>  9 orange  2010 XS    #> 10 orange  2010 S     #> 11 orange  2010 M     #> 12 orange  2010 L     #> 13 orange  2011 XS    #> 14 orange  2011 S     #> 15 orange  2011 M     #> 16 orange  2011 L     #> 17 orange  2012 XS    #> 18 orange  2012 S     #> 19 orange  2012 M     #> 20 orange  2012 L"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tibble from all combinations of inputs — expand_grid","title":"Create a tibble from all combinations of inputs — expand_grid","text":"expand_grid() heavily motivated expand.grid(). Compared expand.grid(), : Produces sorted output (varying first column slowest, rather fastest). Returns tibble, data frame. Never converts strings factors. add additional attributes. Can expand generalised vector, including data frames.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tibble from all combinations of inputs — expand_grid","text":"","code":"expand_grid(..., .name_repair = \"check_unique\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tibble from all combinations of inputs — expand_grid","text":"... Name-value pairs. name become column name output. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tibble from all combinations of inputs — expand_grid","text":"tibble one column input .... output one row combination inputs, .e. size equal product sizes inputs. implies input length 0, output zero rows.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tibble from all combinations of inputs — expand_grid","text":"","code":"expand_grid(x = 1:3, y = 1:2) #> # A tibble: 6 × 2 #>       x     y #>   <int> <int> #> 1     1     1 #> 2     1     2 #> 3     2     1 #> 4     2     2 #> 5     3     1 #> 6     3     2 expand_grid(l1 = letters, l2 = LETTERS) #> # A tibble: 676 × 2 #>    l1    l2    #>    <chr> <chr> #>  1 a     A     #>  2 a     B     #>  3 a     C     #>  4 a     D     #>  5 a     E     #>  6 a     F     #>  7 a     G     #>  8 a     H     #>  9 a     I     #> 10 a     J     #> # ℹ 666 more rows  # Can also expand data frames expand_grid(df = tibble(x = 1:2, y = c(2, 1)), z = 1:3) #> # A tibble: 6 × 2 #>    df$x    $y     z #>   <int> <dbl> <int> #> 1     1     2     1 #> 2     1     2     2 #> 3     1     2     3 #> 4     2     1     1 #> 5     2     1     2 #> 6     2     1     3 # And matrices expand_grid(x1 = matrix(1:4, nrow = 2), x2 = matrix(5:8, nrow = 2)) #> # A tibble: 4 × 2 #>   x1[,1]  [,2] x2[,1]  [,2] #>    <int> <int>  <int> <int> #> 1      1     3      5     7 #> 2      1     3      6     8 #> 3      2     4      5     7 #> 4      2     4      6     8"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a character column into multiple columns using regular expression groups — extract","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"extract() superseded favour separate_wider_regex() polished API better handling problems. Superseded functions go away, receive critical bug fixes. Given regular expression capturing groups, extract() turns group new column. groups match, input NA, output NA.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"","code":"extract(   data,   col,   into,   regex = \"([[:alnum:]]+)\",   remove = TRUE,   convert = FALSE,   ... )"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"data data frame. col <tidy-select> Column expand. Names new variables create character vector. Use NA omit variable output. regex string representing regular expression used extract desired values. one group (defined ()) element . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. ... Additional arguments passed methods.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"","code":"df <- tibble(x = c(NA, \"a-b\", \"a-d\", \"b-c\", \"d-e\")) df %>% extract(x, \"A\") #> # A tibble: 5 × 1 #>   A     #>   <chr> #> 1 NA    #> 2 a     #> 3 a     #> 4 b     #> 5 d     df %>% extract(x, c(\"A\", \"B\"), \"([[:alnum:]]+)-([[:alnum:]]+)\") #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 d     e      # Now recommended df %>%   separate_wider_regex(     x,     patterns = c(A = \"[[:alnum:]]+\", \"-\", B = \"[[:alnum:]]+\")   ) #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 d     e      # If no match, NA: df %>% extract(x, c(\"A\", \"B\"), \"([a-d]+)-([a-d]+)\") #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 NA    NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract numeric component of variable. — extract_numeric","title":"Extract numeric component of variable. — extract_numeric","text":"DEPRECATED: please use readr::parse_number() instead.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract numeric component of variable. — extract_numeric","text":"","code":"extract_numeric(x)"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract numeric component of variable. — extract_numeric","text":"x character vector (factor).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":null,"dir":"Reference","previous_headings":"","what":"Fill in missing values with previous or next value — fill","title":"Fill in missing values with previous or next value — fill","text":"Fills missing values selected columns using next previous entry. useful common output format values repeated, recorded change.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fill in missing values with previous or next value — fill","text":"","code":"fill(data, ..., .direction = c(\"down\", \"up\", \"downup\", \"updown\"))"},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fill in missing values with previous or next value — fill","text":"data data frame. ... <tidy-select> Columns fill. .direction Direction fill missing values. Currently either \"\" (default), \"\", \"downup\" (.e. first ) \"updown\" (first ).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fill in missing values with previous or next value — fill","text":"Missing values replaced atomic vectors; NULLs replaced lists.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Fill in missing values with previous or next value — fill","text":"grouped data frames created dplyr::group_by(), fill() applied within group, meaning fill across group boundaries.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fill in missing values with previous or next value — fill","text":"","code":"# direction = \"down\" -------------------------------------------------------- # Value (year) is recorded only when it changes sales <- tibble::tribble(   ~quarter, ~year, ~sales,   \"Q1\",    2000,    66013,   \"Q2\",      NA,    69182,   \"Q3\",      NA,    53175,   \"Q4\",      NA,    21001,   \"Q1\",    2001,    46036,   \"Q2\",      NA,    58842,   \"Q3\",      NA,    44568,   \"Q4\",      NA,    50197,   \"Q1\",    2002,    39113,   \"Q2\",      NA,    41668,   \"Q3\",      NA,    30144,   \"Q4\",      NA,    52897,   \"Q1\",    2004,    32129,   \"Q2\",      NA,    67686,   \"Q3\",      NA,    31768,   \"Q4\",      NA,    49094 ) # `fill()` defaults to replacing missing data from top to bottom sales %>% fill(year) #> # A tibble: 16 × 3 #>    quarter  year sales #>    <chr>   <dbl> <dbl> #>  1 Q1       2000 66013 #>  2 Q2       2000 69182 #>  3 Q3       2000 53175 #>  4 Q4       2000 21001 #>  5 Q1       2001 46036 #>  6 Q2       2001 58842 #>  7 Q3       2001 44568 #>  8 Q4       2001 50197 #>  9 Q1       2002 39113 #> 10 Q2       2002 41668 #> 11 Q3       2002 30144 #> 12 Q4       2002 52897 #> 13 Q1       2004 32129 #> 14 Q2       2004 67686 #> 15 Q3       2004 31768 #> 16 Q4       2004 49094  # direction = \"up\" ---------------------------------------------------------- # Value (pet_type) is missing above tidy_pets <- tibble::tribble(   ~rank, ~pet_type, ~breed,   1L,        NA,    \"Boston Terrier\",   2L,        NA,    \"Retrievers (Labrador)\",   3L,        NA,    \"Retrievers (Golden)\",   4L,        NA,    \"French Bulldogs\",   5L,        NA,    \"Bulldogs\",   6L,     \"Dog\",    \"Beagles\",   1L,        NA,    \"Persian\",   2L,        NA,    \"Maine Coon\",   3L,        NA,    \"Ragdoll\",   4L,        NA,    \"Exotic\",   5L,        NA,    \"Siamese\",   6L,     \"Cat\",    \"American Short\" )  # For values that are missing above you can use `.direction = \"up\"` tidy_pets %>%   fill(pet_type, .direction = \"up\") #> # A tibble: 12 × 3 #>     rank pet_type breed                 #>    <int> <chr>    <chr>                 #>  1     1 Dog      Boston Terrier        #>  2     2 Dog      Retrievers (Labrador) #>  3     3 Dog      Retrievers (Golden)   #>  4     4 Dog      French Bulldogs       #>  5     5 Dog      Bulldogs              #>  6     6 Dog      Beagles               #>  7     1 Cat      Persian               #>  8     2 Cat      Maine Coon            #>  9     3 Cat      Ragdoll               #> 10     4 Cat      Exotic                #> 11     5 Cat      Siamese               #> 12     6 Cat      American Short         # direction = \"downup\" ------------------------------------------------------ # Value (n_squirrels) is missing above and below within a group squirrels <- tibble::tribble(   ~group,    ~name,     ~role,     ~n_squirrels,   1,      \"Sam\",    \"Observer\",   NA,   1,     \"Mara\", \"Scorekeeper\",    8,   1,    \"Jesse\",    \"Observer\",   NA,   1,      \"Tom\",    \"Observer\",   NA,   2,     \"Mike\",    \"Observer\",   NA,   2,  \"Rachael\",    \"Observer\",   NA,   2,  \"Sydekea\", \"Scorekeeper\",   14,   2, \"Gabriela\",    \"Observer\",   NA,   3,  \"Derrick\",    \"Observer\",   NA,   3,     \"Kara\", \"Scorekeeper\",    9,   3,    \"Emily\",    \"Observer\",   NA,   3, \"Danielle\",    \"Observer\",   NA )  # The values are inconsistently missing by position within the group # Use .direction = \"downup\" to fill missing values in both directions squirrels %>%   dplyr::group_by(group) %>%   fill(n_squirrels, .direction = \"downup\") %>%   dplyr::ungroup() #> # A tibble: 12 × 4 #>    group name     role        n_squirrels #>    <dbl> <chr>    <chr>             <dbl> #>  1     1 Sam      Observer              8 #>  2     1 Mara     Scorekeeper           8 #>  3     1 Jesse    Observer              8 #>  4     1 Tom      Observer              8 #>  5     2 Mike     Observer             14 #>  6     2 Rachael  Observer             14 #>  7     2 Sydekea  Scorekeeper          14 #>  8     2 Gabriela Observer             14 #>  9     3 Derrick  Observer              9 #> 10     3 Kara     Scorekeeper           9 #> 11     3 Emily    Observer              9 #> 12     3 Danielle Observer              9  # Using `.direction = \"updown\"` accomplishes the same goal in this example"},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":null,"dir":"Reference","previous_headings":"","what":"Fish encounters — fish_encounters","title":"Fish encounters — fish_encounters","text":"Information fish swimming river: station represents autonomous monitor records tagged fish seen location. Fish travel one direction (migrating downstream). Information misses just important hits, directly recorded form data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fish encounters — fish_encounters","text":"","code":"fish_encounters"},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fish encounters — fish_encounters","text":"dataset variables: fish Fish identifier station Measurement station seen fish seen? (1 yes, true rows)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fish encounters — fish_encounters","text":"Dataset provided Myfanwy Johnston; details https://fishsciences.github.io/post/visualizing-fish-encounter-histories/","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":null,"dir":"Reference","previous_headings":"","what":"Create the full sequence of values in a vector — full_seq","title":"Create the full sequence of values in a vector — full_seq","text":"useful want fill missing values observed . example, full_seq(c(1, 2, 4, 6), 1) return 1:6.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create the full sequence of values in a vector — full_seq","text":"","code":"full_seq(x, period, tol = 1e-06)"},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create the full sequence of values in a vector — full_seq","text":"x numeric vector. period Gap observation. existing data checked ensure actually periodicity. tol Numerical tolerance checking periodicity.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create the full sequence of values in a vector — full_seq","text":"","code":"full_seq(c(1, 2, 4, 5, 10), 1) #>  [1]  1  2  3  4  5  6  7  8  9 10"},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":null,"dir":"Reference","previous_headings":"","what":"Gather columns into key-value pairs — gather","title":"Gather columns into key-value pairs — gather","text":"Development gather() complete, new code recommend switching pivot_longer(), easier use, featureful, still active development. df %>% gather(\"key\", \"value\", x, y, z) equivalent df %>% pivot_longer(c(x, y, z), names_to = \"key\", values_to = \"value\") See details vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gather columns into key-value pairs — gather","text":"","code":"gather(   data,   key = \"key\",   value = \"value\",   ...,   na.rm = FALSE,   convert = FALSE,   factor_key = FALSE )"},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gather columns into key-value pairs — gather","text":"data data frame. key, value Names new key value columns, strings symbols. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). ... selection columns. empty, variables selected. can supply bare variable names, select variables x z x:z, exclude y -y. options, see dplyr::select() documentation. See also section selection rules . na.rm TRUE, remove rows output value column NA. convert TRUE automatically run type.convert() key column. useful column types actually numeric, integer, logical. factor_key FALSE, default, key values stored character vector. TRUE, stored factor, preserves original ordering columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"rules-for-selection","dir":"Reference","previous_headings":"","what":"Rules for selection","title":"Gather columns into key-value pairs — gather","text":"Arguments selecting columns passed tidyselect::vars_select() treated specially. Unlike verbs, selecting functions make strict distinction data expressions context expressions. data expression either bare name like x expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. instance, col1:col3 data expression refers data columns, seq(start, end) context expression refers objects contexts. need refer contextual objects data expression, can use all_of() any_of(). functions used select data-variables whose names stored env-variable. instance, all_of() selects variables listed character vector . details, see tidyselect::select_helpers() documentation.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gather columns into key-value pairs — gather","text":"","code":"# From https://stackoverflow.com/questions/1181060 stocks <- tibble(   time = as.Date(\"2009-01-01\") + 0:9,   X = rnorm(10, 0, 1),   Y = rnorm(10, 0, 2),   Z = rnorm(10, 0, 4) )  gather(stocks, \"stock\", \"price\", -time) #> # A tibble: 30 × 3 #>    time       stock  price #>    <date>     <chr>  <dbl> #>  1 2009-01-01 X     -1.82  #>  2 2009-01-02 X     -0.247 #>  3 2009-01-03 X     -0.244 #>  4 2009-01-04 X     -0.283 #>  5 2009-01-05 X     -0.554 #>  6 2009-01-06 X      0.629 #>  7 2009-01-07 X      2.07  #>  8 2009-01-08 X     -1.63  #>  9 2009-01-09 X      0.512 #> 10 2009-01-10 X     -1.86  #> # ℹ 20 more rows stocks %>% gather(\"stock\", \"price\", -time) #> # A tibble: 30 × 3 #>    time       stock  price #>    <date>     <chr>  <dbl> #>  1 2009-01-01 X     -1.82  #>  2 2009-01-02 X     -0.247 #>  3 2009-01-03 X     -0.244 #>  4 2009-01-04 X     -0.283 #>  5 2009-01-05 X     -0.554 #>  6 2009-01-06 X      0.629 #>  7 2009-01-07 X      2.07  #>  8 2009-01-08 X     -1.63  #>  9 2009-01-09 X      0.512 #> 10 2009-01-10 X     -1.86  #> # ℹ 20 more rows  # get first observation for each Species in iris data -- base R mini_iris <- iris[c(1, 51, 101), ] # gather Sepal.Length, Sepal.Width, Petal.Length, Petal.Width gather(mini_iris, key = \"flower_att\", value = \"measurement\",        Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) #>       Species   flower_att measurement #> 1      setosa Sepal.Length         5.1 #> 2  versicolor Sepal.Length         7.0 #> 3   virginica Sepal.Length         6.3 #> 4      setosa  Sepal.Width         3.5 #> 5  versicolor  Sepal.Width         3.2 #> 6   virginica  Sepal.Width         3.3 #> 7      setosa Petal.Length         1.4 #> 8  versicolor Petal.Length         4.7 #> 9   virginica Petal.Length         6.0 #> 10     setosa  Petal.Width         0.2 #> 11 versicolor  Petal.Width         1.4 #> 12  virginica  Petal.Width         2.5 # same result but less verbose gather(mini_iris, key = \"flower_att\", value = \"measurement\", -Species) #>       Species   flower_att measurement #> 1      setosa Sepal.Length         5.1 #> 2  versicolor Sepal.Length         7.0 #> 3   virginica Sepal.Length         6.3 #> 4      setosa  Sepal.Width         3.5 #> 5  versicolor  Sepal.Width         3.2 #> 6   virginica  Sepal.Width         3.3 #> 7      setosa Petal.Length         1.4 #> 8  versicolor Petal.Length         4.7 #> 9   virginica Petal.Length         6.0 #> 10     setosa  Petal.Width         0.2 #> 11 versicolor  Petal.Width         1.4 #> 12  virginica  Petal.Width         2.5"},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":null,"dir":"Reference","previous_headings":"","what":"Hoist values out of list-columns — hoist","title":"Hoist values out of list-columns — hoist","text":"hoist() allows selectively pull components list-column top-level columns, using syntax purrr::pluck(). Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hoist values out of list-columns — hoist","text":"","code":"hoist(   .data,   .col,   ...,   .remove = TRUE,   .simplify = TRUE,   .ptype = NULL,   .transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hoist values out of list-columns — hoist","text":".data data frame. .col <tidy-select> List-column extract components . ... <dynamic-dots> Components .col turn columns form col_name = \"pluck_specification\". can pluck name character vector, position integer vector, combination two list. See purrr::pluck() details. column names must unique call hoist(), although existing columns name overwritten. plucking single string can choose omit name, .e. hoist(df, col, \"x\") short-hand hoist(df, col, x = \"x\"). .remove TRUE, default, remove extracted components .col. ensures value lives one place. components removed .col, .col removed result entirely. .simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. .ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. .transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hoist values out of list-columns — hoist","text":"","code":"df <- tibble(   character = c(\"Toothless\", \"Dory\"),   metadata = list(     list(       species = \"dragon\",       color = \"black\",       films = c(         \"How to Train Your Dragon\",         \"How to Train Your Dragon 2\",         \"How to Train Your Dragon: The Hidden World\"       )     ),     list(       species = \"blue tang\",       color = \"blue\",       films = c(\"Finding Nemo\", \"Finding Dory\")     )   ) ) df #> # A tibble: 2 × 2 #>   character metadata         #>   <chr>     <list>           #> 1 Toothless <named list [3]> #> 2 Dory      <named list [3]>  # Extract only specified components df %>% hoist(metadata,   \"species\",   first_film = list(\"films\", 1L),   third_film = list(\"films\", 3L) ) #> # A tibble: 2 × 5 #>   character species   first_film               third_film     metadata     #>   <chr>     <chr>     <chr>                    <chr>          <list>       #> 1 Toothless dragon    How to Train Your Dragon How to Train … <named list> #> 2 Dory      blue tang Finding Nemo             NA             <named list>"},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":null,"dir":"Reference","previous_headings":"","what":"Household data — household","title":"Household data — household","text":"dataset based example vignette(\"datatable-reshape\", package = \"data.table\")","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Household data — household","text":"","code":"household"},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Household data — household","text":"data frame 5 rows 5 columns: family Family identifier dob_child1 Date birth first child dob_child2 Date birth second child name_child1 Name first child name_child2 Name second child","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":null,"dir":"Reference","previous_headings":"","what":"Nest rows into a list-column of data frames — nest","title":"Nest rows into a list-column of data frames — nest","text":"Nesting creates list-column data frames; unnesting flattens back regular columns. Nesting implicitly summarising operation: get one row group defined non-nested columns. useful conjunction summaries work whole datasets, notably models. Learn vignette(\"nest\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nest rows into a list-column of data frames — nest","text":"","code":"nest(.data, ..., .by = NULL, .key = NULL, .names_sep = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nest rows into a list-column of data frames — nest","text":".data data frame. ... <tidy-select> Columns nest; appear inner data frames. Specified using name-variable pairs form new_col = c(col1, col2, col3). right hand side can valid tidyselect expression. supplied, ... derived columns selected ., use column name .key. : previously write df %>% nest(x, y, z). Convert df %>% nest(data = c(x, y, z)). .<tidy-select> Columns nest ; remain outer data frame. .can used place conjunction columns supplied .... supplied, .derived columns selected .... .key name resulting nested column. applicable ... specified, .e. case df %>% nest(.= x). NULL, \"data\" used default. .names_sep NULL, default, inner names come former outer names. string,  new inner names use outer names names_sep automatically stripped. makes names_sep roughly symmetric nesting unnesting.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nest rows into a list-column of data frames — nest","text":"neither ... .supplied, nest() nest variables, use column name supplied .key.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"new-syntax","dir":"Reference","previous_headings":"","what":"New syntax","title":"Nest rows into a list-column of data frames — nest","text":"tidyr 1.0.0 introduced new syntax nest() unnest() designed similar functions. Converting new syntax straightforward (guided message receive) just need run old analysis, can easily revert previous behaviour using nest_legacy() unnest_legacy() follows:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Nest rows into a list-column of data frames — nest","text":"df %>% nest(data = c(x, y)) specifies columns nested; .e. columns appear inner data frame. df %>% nest(.= c(x, y)) specifies columns nest ; .e. columns remain outer data frame. alternative way achieve latter nest() grouped data frame created dplyr::group_by(). grouping variables remain outer data frame others nested. result preserves grouping input. Variables supplied nest() override grouping variables df %>% group_by(x, y) %>% nest(data = !z) equivalent df %>% nest(data = !z). supply .grouped data frame, groups already represent nesting .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nest rows into a list-column of data frames — nest","text":"","code":"df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)  # Specify variables to nest using name-variable pairs. # Note that we get one row of output for each unique combination of # non-nested variables. df %>% nest(data = c(y, z)) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Specify variables to nest by (rather than variables to nest) using `.by` df %>% nest(.by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # In this case, since `...` isn't used you can specify the resulting column # name with `.key` df %>% nest(.by = x, .key = \"cols\") #> # A tibble: 3 × 2 #>       x cols             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Use tidyselect syntax and helpers, just like in `dplyr::select()` df %>% nest(data = any_of(c(\"y\", \"z\"))) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # `...` and `.by` can be used together to drop columns you no longer need, # or to include the columns you are nesting by in the inner data frame too. # This drops `z`: df %>% nest(data = y, .by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 1]> #> 2     2 <tibble [2 × 1]> #> 3     3 <tibble [1 × 1]> # This includes `x` in the inner data frame: df %>% nest(data = everything(), .by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 3]> #> 2     2 <tibble [2 × 3]> #> 3     3 <tibble [1 × 3]>  # Multiple nesting structures can be specified at once iris %>%   nest(petal = starts_with(\"Petal\"), sepal = starts_with(\"Sepal\")) #> # A tibble: 3 × 3 #>   Species    petal             sepal             #>   <fct>      <list>            <list>            #> 1 setosa     <tibble [50 × 2]> <tibble [50 × 2]> #> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]> #> 3 virginica  <tibble [50 × 2]> <tibble [50 × 2]> iris %>%   nest(width = contains(\"Width\"), length = contains(\"Length\")) #> # A tibble: 3 × 3 #>   Species    width             length            #>   <fct>      <list>            <list>            #> 1 setosa     <tibble [50 × 2]> <tibble [50 × 2]> #> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]> #> 3 virginica  <tibble [50 × 2]> <tibble [50 × 2]>  # Nesting a grouped data frame nests all variables apart from the group vars fish_encounters %>%   dplyr::group_by(fish) %>%   nest() #> # A tibble: 19 × 2 #> # Groups:   fish [19] #>    fish  data              #>    <fct> <list>            #>  1 4842  <tibble [11 × 2]> #>  2 4843  <tibble [11 × 2]> #>  3 4844  <tibble [11 × 2]> #>  4 4845  <tibble [5 × 2]>  #>  5 4847  <tibble [3 × 2]>  #>  6 4848  <tibble [4 × 2]>  #>  7 4849  <tibble [2 × 2]>  #>  8 4850  <tibble [6 × 2]>  #>  9 4851  <tibble [2 × 2]>  #> 10 4854  <tibble [2 × 2]>  #> 11 4855  <tibble [5 × 2]>  #> 12 4857  <tibble [9 × 2]>  #> 13 4858  <tibble [11 × 2]> #> 14 4859  <tibble [5 × 2]>  #> 15 4861  <tibble [11 × 2]> #> 16 4862  <tibble [9 × 2]>  #> 17 4863  <tibble [2 × 2]>  #> 18 4864  <tibble [2 × 2]>  #> 19 4865  <tibble [3 × 2]>   # That is similar to `nest(.by = )`, except here the result isn't grouped fish_encounters %>%   nest(.by = fish) #> # A tibble: 19 × 2 #>    fish  data              #>    <fct> <list>            #>  1 4842  <tibble [11 × 2]> #>  2 4843  <tibble [11 × 2]> #>  3 4844  <tibble [11 × 2]> #>  4 4845  <tibble [5 × 2]>  #>  5 4847  <tibble [3 × 2]>  #>  6 4848  <tibble [4 × 2]>  #>  7 4849  <tibble [2 × 2]>  #>  8 4850  <tibble [6 × 2]>  #>  9 4851  <tibble [2 × 2]>  #> 10 4854  <tibble [2 × 2]>  #> 11 4855  <tibble [5 × 2]>  #> 12 4857  <tibble [9 × 2]>  #> 13 4858  <tibble [11 × 2]> #> 14 4859  <tibble [5 × 2]>  #> 15 4861  <tibble [11 × 2]> #> 16 4862  <tibble [9 × 2]>  #> 17 4863  <tibble [2 × 2]>  #> 18 4864  <tibble [2 × 2]>  #> 19 4865  <tibble [3 × 2]>   # Nesting is often useful for creating per group models mtcars %>%   nest(.by = cyl) %>%   dplyr::mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df))) #> # A tibble: 3 × 3 #>     cyl data               models #>   <dbl> <list>             <list> #> 1     6 <tibble [7 × 10]>  <lm>   #> 2     4 <tibble [11 × 10]> <lm>   #> 3     8 <tibble [14 × 10]> <lm>"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy versions of nest() and unnest() — nest_legacy","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"tidyr 1.0.0 introduced new syntax nest() unnest(). majority existing usage automatically translated new syntax warning. However, need quickly roll back previous behaviour, functions provide previous interface. make old code work , add following code top script:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"","code":"nest_legacy(data, ..., .key = \"data\")  unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"data data frame. ... Specification columns unnest. Use bare variable names functions variables. omitted, defaults list-cols. .key name new column, string symbol. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). .drop additional list columns dropped? default, unnest() drop unnesting specified columns requires rows duplicated. .id Data frame identifier - supplied, create new column name .id, giving unique identifier. useful list column named. .sep non-NULL, names unnested data frame columns combine name original list-col names nested data frame, separated .sep. .preserve Optionally, list-columns preserve output. duplicated way atomic vectors. dplyr::select() semantics can preserve multiple variables .preserve = c(x, y) .preserve = starts_with(\"list\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"","code":"# Nest and unnest are inverses df <- tibble(x = c(1, 1, 2), y = 3:1) df %>% nest_legacy(y) #> # A tibble: 2 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [2 × 1]> #> 2     2 <tibble [1 × 1]> df %>% nest_legacy(y) %>% unnest_legacy() #> # A tibble: 3 × 2 #>       x     y #>   <dbl> <int> #> 1     1     3 #> 2     1     2 #> 3     2     1  # nesting ------------------------------------------------------------------- as_tibble(iris) %>% nest_legacy(!Species) #> # A tibble: 3 × 2 #>   Species    data              #>   <fct>      <list>            #> 1 setosa     <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica  <tibble [50 × 4]> as_tibble(chickwts) %>% nest_legacy(weight) #> # A tibble: 6 × 2 #>   feed      data              #>   <fct>     <list>            #> 1 horsebean <tibble [10 × 1]> #> 2 linseed   <tibble [12 × 1]> #> 3 soybean   <tibble [14 × 1]> #> 4 sunflower <tibble [12 × 1]> #> 5 meatmeal  <tibble [11 × 1]> #> 6 casein    <tibble [12 × 1]>  # unnesting ----------------------------------------------------------------- df <- tibble(   x = 1:2,   y = list(     tibble(z = 1),     tibble(z = 3:4)   ) ) df %>% unnest_legacy(y) #> # A tibble: 3 × 2 #>       x     z #>   <int> <dbl> #> 1     1     1 #> 2     2     3 #> 3     2     4  # You can also unnest multiple columns simultaneously df <- tibble(   a = list(c(\"a\", \"b\"), \"c\"),   b = list(1:2, 3),   c = c(11, 22) ) df %>% unnest_legacy(a, b) #> # A tibble: 3 × 3 #>       c a         b #>   <dbl> <chr> <dbl> #> 1    11 a         1 #> 2    11 b         2 #> 3    22 c         3 # If you omit the column names, it'll unnest all list-cols df %>% unnest_legacy() #> # A tibble: 3 × 3 #>       c a         b #>   <dbl> <chr> <dbl> #> 1    11 a         1 #> 2    11 b         2 #> 3    22 c         3"},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":null,"dir":"Reference","previous_headings":"","what":"Pack and unpack — pack","title":"Pack and unpack — pack","text":"Packing unpacking preserve length data frame, changing width. pack() makes df narrow collapsing set columns single df-column. unpack() makes data wider expanding df-columns back individual columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pack and unpack — pack","text":"","code":"pack(.data, ..., .names_sep = NULL, .error_call = current_env())  unpack(   data,   cols,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pack and unpack — pack","text":"... pack(), <tidy-select> columns pack, specified using name-variable pairs form new_col = c(col1, col2, col3). right hand side can valid tidy select expression. unpack(), dots future extensions must empty. data, .data data frame. cols <tidy-select> Columns unpack. names_sep, .names_sep NULL, default, names left . pack(), inner names come former outer names; unpack(), new outer names come inner names. string, inner outer names used together. unpack(), names new outer columns formed pasting together outer inner column names, separated names_sep. pack(), new inner names outer names + names_sep automatically stripped. makes names_sep roughly symmetric packing unpacking. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . error_call, .error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pack and unpack — pack","text":"Generally, unpacking useful packing simplifies complex data structure. Currently, functions work df-cols, mostly curiosity, seem worth exploring mimic nested column headers popular Excel.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pack and unpack — pack","text":"","code":"# Packing ------------------------------------------------------------------- # It's not currently clear why you would ever want to pack columns # since few functions work with this sort of data. df <- tibble(x1 = 1:3, x2 = 4:6, x3 = 7:9, y = 1:3) df #> # A tibble: 3 × 4 #>      x1    x2    x3     y #>   <int> <int> <int> <int> #> 1     1     4     7     1 #> 2     2     5     8     2 #> 3     3     6     9     3 df %>% pack(x = starts_with(\"x\")) #> # A tibble: 3 × 2 #>       y  x$x1   $x2   $x3 #>   <int> <int> <int> <int> #> 1     1     1     4     7 #> 2     2     2     5     8 #> 3     3     3     6     9 df %>% pack(x = c(x1, x2, x3), y = y) #> # A tibble: 3 × 2 #>    x$x1   $x2   $x3   y$y #>   <int> <int> <int> <int> #> 1     1     4     7     1 #> 2     2     5     8     2 #> 3     3     6     9     3  # .names_sep allows you to strip off common prefixes; this # acts as a natural inverse to name_sep in unpack() iris %>%   as_tibble() %>%   pack(     Sepal = starts_with(\"Sepal\"),     Petal = starts_with(\"Petal\"),     .names_sep = \".\"   ) #> # A tibble: 150 × 3 #>    Species Sepal$Length $Width Petal$Length $Width #>    <fct>          <dbl>  <dbl>        <dbl>  <dbl> #>  1 setosa           5.1    3.5          1.4    0.2 #>  2 setosa           4.9    3            1.4    0.2 #>  3 setosa           4.7    3.2          1.3    0.2 #>  4 setosa           4.6    3.1          1.5    0.2 #>  5 setosa           5      3.6          1.4    0.2 #>  6 setosa           5.4    3.9          1.7    0.4 #>  7 setosa           4.6    3.4          1.4    0.3 #>  8 setosa           5      3.4          1.5    0.2 #>  9 setosa           4.4    2.9          1.4    0.2 #> 10 setosa           4.9    3.1          1.5    0.1 #> # ℹ 140 more rows  # Unpacking ----------------------------------------------------------------- df <- tibble(   x = 1:3,   y = tibble(a = 1:3, b = 3:1),   z = tibble(X = c(\"a\", \"b\", \"c\"), Y = runif(3), Z = c(TRUE, FALSE, NA)) ) df #> # A tibble: 3 × 3 #>       x   y$a    $b z$X       $Y $Z    #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(y) #> # A tibble: 3 × 4 #>       x     a     b z$X       $Y $Z    #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(c(y, z)) #> # A tibble: 3 × 6 #>       x     a     b X          Y Z     #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(c(y, z), names_sep = \"_\") #> # A tibble: 3 × 6 #>       x   y_a   y_b z_X      z_Y z_Z   #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from wide to long — pivot_longer","title":"Pivot data from wide to long — pivot_longer","text":"pivot_longer() \"lengthens\" data, increasing number rows decreasing number columns. inverse transformation pivot_wider() Learn vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from wide to long — pivot_longer","text":"","code":"pivot_longer(   data,   cols,   ...,   cols_vary = \"fastest\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   names_ptypes = NULL,   names_transform = NULL,   names_repair = \"check_unique\",   values_to = \"value\",   values_drop_na = FALSE,   values_ptypes = NULL,   values_transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from wide to long — pivot_longer","text":"data data frame pivot. cols <tidy-select> Columns pivot longer format. ... Additional arguments passed methods. cols_vary pivoting cols longer format, output rows arranged relative original row number? \"fastest\", default, keeps individual rows cols close together output. often produces intuitively ordered output least one key column data involved pivoting process. \"slowest\" keeps individual columns cols close together output. often produces intuitively ordered output utilize columns data pivoting process. names_to character vector specifying new column columns create information stored column names data specified cols. length 0, NULL supplied, columns created. length 1, single column created contain column names specified cols. length >1, multiple columns created. case, one names_sep names_pattern must supplied specify column names split. also two additional character values can take advantage : NA discard corresponding component column name. \".value\" indicates corresponding component column name defines name output column containing cell values, overriding values_to entirely. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, arguments control column name broken . names_sep takes specification separate(), can either numeric vector (specifying positions break ), single string (specifying regular expression split ). names_pattern takes specification extract(), regular expression containing matching groups (()). arguments give enough control, use pivot_longer_spec() create spec object process manually needed. names_ptypes, values_ptypes Optionally, list column name-prototype pairs. Alternatively, single empty prototype can supplied, applied columns. prototype (ptype short) zero-length vector (like integer() numeric()) defines type, class, attributes vector. Use arguments want confirm created columns types expect. Note want change (instead confirm) types specific columns, use names_transform values_transform instead. names_transform, values_transform Optionally, list column name-function pairs. Alternatively, single function can supplied, applied columns. Use arguments need change types specific columns. example, names_transform = list(week = .integer) convert character variable called week integer. specified, type columns generated names_to character, type variables generated values_to common type input columns used generate . names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_to string specifying name column create data stored cell values. names_to character containing special .value sentinel, value ignored, name value column derived part existing column names. values_drop_na TRUE, drop rows contain NAs value_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pivot data from wide to long — pivot_longer","text":"pivot_longer() updated approach gather(), designed simpler use handle use cases. recommend use pivot_longer() new code; gather() going away longer active development.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from wide to long — pivot_longer","text":"","code":"# See vignette(\"pivot\") for examples and explanation  # Simplest case where column names are character data relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> 11 Mainline Prot     289       495       619       655       651      1107 #> 12 Mormon             29        40        48        51        56       112 #> 13 Muslim              6         7         9        10         9        23 #> 14 Orthodox           13        17        23        32        32        47 #> 15 Other Christ…       9         7        11        13        13        14 #> 16 Other Faiths       20        33        40        46        49        63 #> 17 Other World …       5         2         3         4         2         7 #> 18 Unaffiliated      217       299       374       365       341       528 #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(!religion, names_to = \"income\", values_to = \"count\") #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows  # Slightly more complex case where columns have common prefix, # and missing missings are structural so should be dropped. billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_prefix = \"wk\",     values_to = \"rank\",     values_drop_na = TRUE   ) #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   1        87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   2        82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   3        72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   4        77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   5        87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   6        94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   7        99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   1        91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   2        87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   3        92 #> # ℹ 5,297 more rows  # Multiple variables stored in column names who %>% pivot_longer(   cols = new_sp_m014:newrel_f65,   names_to = c(\"diagnosis\", \"gender\", \"age\"),   names_pattern = \"new_?(.*)_(.)(.*)\",   values_to = \"count\" ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows  # Multiple observations per row. Since all columns are used in the pivoting # process, we'll use `cols_vary` to keep values from the original columns # close together in the output. anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89 anscombe %>%   pivot_longer(     everything(),     cols_vary = \"slowest\",     names_to = c(\".value\", \"set\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 44 × 3 #>    set       x     y #>    <chr> <dbl> <dbl> #>  1 1        10  8.04 #>  2 1         8  6.95 #>  3 1        13  7.58 #>  4 1         9  8.81 #>  5 1        11  8.33 #>  6 1        14  9.96 #>  7 1         6  7.24 #>  8 1         4  4.26 #>  9 1        12 10.8  #> 10 1         7  4.82 #> # ℹ 34 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from wide to long using a spec — pivot_longer_spec","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"low level interface pivoting, inspired cdata package, allows describe pivoting data frame.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"","code":"pivot_longer_spec(   data,   spec,   ...,   cols_vary = \"fastest\",   names_repair = \"check_unique\",   values_drop_na = FALSE,   values_ptypes = NULL,   values_transform = NULL,   error_call = current_env() )  build_longer_spec(   data,   cols,   ...,   names_to = \"name\",   values_to = \"value\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   names_ptypes = NULL,   names_transform = NULL,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"data data frame pivot. spec specification data frame. useful complex pivots gives greater control metadata stored column names turns columns result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting. ... dots future extensions must empty. cols_vary pivoting cols longer format, output rows arranged relative original row number? \"fastest\", default, keeps individual rows cols close together output. often produces intuitively ordered output least one key column data involved pivoting process. \"slowest\" keeps individual columns cols close together output. often produces intuitively ordered output utilize columns data pivoting process. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_drop_na TRUE, drop rows contain NAs value_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. cols <tidy-select> Columns pivot longer format. names_to character vector specifying new column columns create information stored column names data specified cols. length 0, NULL supplied, columns created. length 1, single column created contain column names specified cols. length >1, multiple columns created. case, one names_sep names_pattern must supplied specify column names split. also two additional character values can take advantage : NA discard corresponding component column name. \".value\" indicates corresponding component column name defines name output column containing cell values, overriding values_to entirely. values_to string specifying name column create data stored cell values. names_to character containing special .value sentinel, value ignored, name value column derived part existing column names. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, arguments control column name broken . names_sep takes specification separate(), can either numeric vector (specifying positions break ), single string (specifying regular expression split ). names_pattern takes specification extract(), regular expression containing matching groups (()). arguments give enough control, use pivot_longer_spec() create spec object process manually needed. names_ptypes, values_ptypes Optionally, list column name-prototype pairs. Alternatively, single empty prototype can supplied, applied columns. prototype (ptype short) zero-length vector (like integer() numeric()) defines type, class, attributes vector. Use arguments want confirm created columns types expect. Note want change (instead confirm) types specific columns, use names_transform values_transform instead. names_transform, values_transform Optionally, list column name-function pairs. Alternatively, single function can supplied, applied columns. Use arguments need change types specific columns. example, names_transform = list(week = .integer) convert character variable called week integer. specified, type columns generated names_to character, type variables generated values_to common type input columns used generate .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"","code":"# See vignette(\"pivot\") for examples and explanation  # Use `build_longer_spec()` to build `spec` using similar syntax to `pivot_longer()` # and run `pivot_longer_spec()` based on `spec`. spec <- relig_income %>% build_longer_spec(   cols = !religion,   names_to = \"income\",   values_to = \"count\" ) spec #> # A tibble: 10 × 3 #>    .name              .value income             #>    <chr>              <chr>  <chr>              #>  1 <$10k              count  <$10k              #>  2 $10-20k            count  $10-20k            #>  3 $20-30k            count  $20-30k            #>  4 $30-40k            count  $30-40k            #>  5 $40-50k            count  $40-50k            #>  6 $50-75k            count  $50-75k            #>  7 $75-100k           count  $75-100k           #>  8 $100-150k          count  $100-150k          #>  9 >150k              count  >150k              #> 10 Don't know/refused count  Don't know/refused  pivot_longer_spec(relig_income, spec) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows  # Is equivalent to: relig_income %>% pivot_longer(   cols = !religion,   names_to = \"income\",   values_to = \"count\" ) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from long to wide — pivot_wider","title":"Pivot data from long to wide — pivot_wider","text":"pivot_wider() \"widens\" data, increasing number columns decreasing number rows. inverse transformation pivot_longer(). Learn vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from long to wide — pivot_wider","text":"","code":"pivot_wider(   data,   ...,   id_cols = NULL,   id_expand = FALSE,   names_from = name,   names_prefix = \"\",   names_sep = \"_\",   names_glue = NULL,   names_sort = FALSE,   names_vary = \"fastest\",   names_expand = FALSE,   names_repair = \"check_unique\",   values_from = value,   values_fill = NULL,   values_fn = NULL,   unused_fn = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from long to wide — pivot_wider","text":"data data frame pivot. ... Additional arguments passed methods. id_cols <tidy-select> set columns uniquely identify observation. Typically used redundant variables, .e. variables whose values perfectly correlated existing variables. Defaults columns data except columns specified names_from values_from. tidyselect expression supplied, evaluated data removing columns specified names_from values_from. id_expand values id_cols columns expanded expand() pivoting? results rows, output contain complete expansion possible values id_cols. Implicit factor levels represented data become explicit. Additionally, row values corresponding expanded id_cols sorted. names_from, values_from <tidy-select> pair arguments describing column (columns) get name output column (names_from), column (columns) get cell values (values_from). values_from contains multiple values, value added front output column. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns (special .value) create custom column names. names_sort column names sorted? FALSE, default, column names ordered first appearance. names_vary names_from identifies column (columns) multiple unique values, multiple values_from columns provided, order resulting column names combined? \"fastest\" varies names_from values fastest, resulting column naming scheme form: value1_name1, value1_name2, value2_name1, value2_name2. default. \"slowest\" varies names_from values slowest, resulting column naming scheme form: value1_name1, value2_name1, value1_name2, value2_name2. names_expand values names_from columns expanded expand() pivoting? results columns, output contain column names corresponding complete expansion possible values names_from. Implicit factor levels represented data become explicit. Additionally, column names sorted, identical names_sort produce. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_fill Optionally, (scalar) value specifies value filled missing. can named list want apply different fill values different value columns. values_fn Optionally, function applied value cell output. typically use combination id_cols names_from columns uniquely identify observation. can named list want apply different aggregations different values_from columns. unused_fn Optionally, function applied summarize values unused columns (.e. columns identified id_cols, names_from, values_from). default drops unused columns result. can named list want apply different aggregations different unused columns. id_cols must supplied unused_fn useful, since otherwise unspecified columns considered id_cols. similar grouping id_cols summarizing unused columns using unused_fn.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pivot data from long to wide — pivot_wider","text":"pivot_wider() updated approach spread(), designed simpler use handle use cases. recommend use pivot_wider() new code; spread() going away longer active development.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from long to wide — pivot_wider","text":"","code":"# See vignette(\"pivot\") for examples and explanation  fish_encounters #> # A tibble: 114 × 3 #>    fish  station  seen #>    <fct> <fct>   <int> #>  1 4842  Release     1 #>  2 4842  I80_1       1 #>  3 4842  Lisbon      1 #>  4 4842  Rstr        1 #>  5 4842  Base_TD     1 #>  6 4842  BCE         1 #>  7 4842  BCW         1 #>  8 4842  BCE2        1 #>  9 4842  BCW2        1 #> 10 4842  MAE         1 #> # ℹ 104 more rows fish_encounters %>%   pivot_wider(names_from = station, values_from = seen) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA #>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA #>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA #>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA #>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA #>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 11 4855        1     1      1     1       1    NA    NA    NA    NA    NA #> 12 4857        1     1      1     1       1     1     1     1     1    NA #> 13 4858        1     1      1     1       1     1     1     1     1     1 #> 14 4859        1     1      1     1       1    NA    NA    NA    NA    NA #> 15 4861        1     1      1     1       1     1     1     1     1     1 #> 16 4862        1     1      1     1       1     1     1     1     1    NA #> 17 4863        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 18 4864        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 19 4865        1     1      1    NA      NA    NA    NA    NA    NA    NA #> # ℹ 1 more variable: MAW <int> # Fill in missing values fish_encounters %>%   pivot_wider(names_from = station, values_from = seen, values_fill = 0) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1     0     0     0     0     0 #>  5 4847        1     1      1     0       0     0     0     0     0     0 #>  6 4848        1     1      1     1       0     0     0     0     0     0 #>  7 4849        1     1      0     0       0     0     0     0     0     0 #>  8 4850        1     1      0     1       1     1     1     0     0     0 #>  9 4851        1     1      0     0       0     0     0     0     0     0 #> 10 4854        1     1      0     0       0     0     0     0     0     0 #> 11 4855        1     1      1     1       1     0     0     0     0     0 #> 12 4857        1     1      1     1       1     1     1     1     1     0 #> 13 4858        1     1      1     1       1     1     1     1     1     1 #> 14 4859        1     1      1     1       1     0     0     0     0     0 #> 15 4861        1     1      1     1       1     1     1     1     1     1 #> 16 4862        1     1      1     1       1     1     1     1     1     0 #> 17 4863        1     1      0     0       0     0     0     0     0     0 #> 18 4864        1     1      0     0       0     0     0     0     0     0 #> 19 4865        1     1      1     0       0     0     0     0     0     0 #> # ℹ 1 more variable: MAW <int>  # Generate column names from multiple variables us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # You can control whether `names_from` values vary fastest or slowest # relative to the `values_from` column names using `names_vary`. us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe),     names_vary = \"slowest\"   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income moe_income estimate_rent moe_rent #>    <chr> <chr>                     <dbl>      <dbl>         <dbl>    <dbl> #>  1 01    Alabama                   24476        136           747        3 #>  2 02    Alaska                    32940        508          1200       13 #>  3 04    Arizona                   27517        148           972        4 #>  4 05    Arkansas                  23789        165           709        5 #>  5 06    California                29454        109          1358        3 #>  6 08    Colorado                  32401        109          1125        5 #>  7 09    Connecticut               35326        195          1123        5 #>  8 10    Delaware                  31560        247          1076       10 #>  9 11    District of Co…           43198        681          1424       17 #> 10 12    Florida                   25952         70          1077        3 #> # ℹ 42 more rows  # When there are multiple `names_from` or `values_from`, you can use # use `names_sep` or `names_glue` to control the output variable names us_rent_income %>%   pivot_wider(     names_from = variable,     names_sep = \".\",     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate.income estimate.rent moe.income moe.rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     names_glue = \"{variable}_{.value}\",     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            income_estimate rent_estimate income_moe rent_moe #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # Can perform aggregation with `values_fn` warpbreaks <- as_tibble(warpbreaks[c(\"wool\", \"tension\", \"breaks\")]) warpbreaks #> # A tibble: 54 × 3 #>    wool  tension breaks #>    <fct> <fct>    <dbl> #>  1 A     L           26 #>  2 A     L           30 #>  3 A     L           54 #>  4 A     L           25 #>  5 A     L           70 #>  6 A     L           52 #>  7 A     L           51 #>  8 A     L           26 #>  9 A     L           67 #> 10 A     M           18 #> # ℹ 44 more rows warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = mean   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        44.6  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8  # Can pass an anonymous function to `values_fn` when you # need to supply additional arguments warpbreaks$breaks[1] <- NA warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = ~ mean(.x, na.rm = TRUE)   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        46.9  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from long to wide using a spec — pivot_wider_spec","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"low level interface pivoting, inspired cdata package, allows describe pivoting data frame.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"","code":"pivot_wider_spec(   data,   spec,   ...,   names_repair = \"check_unique\",   id_cols = NULL,   id_expand = FALSE,   values_fill = NULL,   values_fn = NULL,   unused_fn = NULL,   error_call = current_env() )  build_wider_spec(   data,   ...,   names_from = name,   values_from = value,   names_prefix = \"\",   names_sep = \"_\",   names_glue = NULL,   names_sort = FALSE,   names_vary = \"fastest\",   names_expand = FALSE,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"data data frame pivot. spec specification data frame. useful complex pivots gives greater control metadata stored columns become column names result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting. ... dots future extensions must empty. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. id_cols <tidy-select> set columns uniquely identifies observation. Defaults columns data except columns specified spec$.value columns spec named .name .value. Typically used redundant variables, .e. variables whose values perfectly correlated existing variables. id_expand values id_cols columns expanded expand() pivoting? results rows, output contain complete expansion possible values id_cols. Implicit factor levels represented data become explicit. Additionally, row values corresponding expanded id_cols sorted. values_fill Optionally, (scalar) value specifies value filled missing. can named list want apply different fill values different value columns. values_fn Optionally, function applied value cell output. typically use combination id_cols names_from columns uniquely identify observation. can named list want apply different aggregations different values_from columns. unused_fn Optionally, function applied summarize values unused columns (.e. columns identified id_cols, names_from, values_from). default drops unused columns result. can named list want apply different aggregations different unused columns. id_cols must supplied unused_fn useful, since otherwise unspecified columns considered id_cols. similar grouping id_cols summarizing unused columns using unused_fn. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. names_from, values_from <tidy-select> pair arguments describing column (columns) get name output column (names_from), column (columns) get cell values (values_from). values_from contains multiple values, value added front output column. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns (special .value) create custom column names. names_sort column names sorted? FALSE, default, column names ordered first appearance. names_vary names_from identifies column (columns) multiple unique values, multiple values_from columns provided, order resulting column names combined? \"fastest\" varies names_from values fastest, resulting column naming scheme form: value1_name1, value1_name2, value2_name1, value2_name2. default. \"slowest\" varies names_from values slowest, resulting column naming scheme form: value1_name1, value2_name1, value1_name2, value2_name2. names_expand values names_from columns expanded expand() pivoting? results columns, output contain column names corresponding complete expansion possible values names_from. Implicit factor levels represented data become explicit. Additionally, column names sorted, identical names_sort produce.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"","code":"# See vignette(\"pivot\") for examples and explanation  us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows spec1 <- us_rent_income %>%   build_wider_spec(names_from = variable, values_from = c(estimate, moe)) spec1 #> # A tibble: 4 × 3 #>   .name           .value   variable #>   <chr>           <chr>    <chr>    #> 1 estimate_income estimate income   #> 2 estimate_rent   estimate rent     #> 3 moe_income      moe      income   #> 4 moe_rent        moe      rent      us_rent_income %>%   pivot_wider_spec(spec1) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # Is equivalent to us_rent_income %>%   pivot_wider(names_from = variable, values_from = c(estimate, moe)) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # `pivot_wider_spec()` provides more control over column names and output format # instead of creating columns with estimate_ and moe_ prefixes, # keep original variable name for estimates and attach _moe as suffix spec2 <- tibble(   .name = c(\"income\", \"rent\", \"income_moe\", \"rent_moe\"),   .value = c(\"estimate\", \"estimate\", \"moe\", \"moe\"),   variable = c(\"income\", \"rent\", \"income\", \"rent\") )  us_rent_income %>%   pivot_wider_spec(spec2) #> # A tibble: 52 × 6 #>    GEOID NAME                 income  rent income_moe rent_moe #>    <chr> <chr>                 <dbl> <dbl>      <dbl>    <dbl> #>  1 01    Alabama               24476   747        136        3 #>  2 02    Alaska                32940  1200        508       13 #>  3 04    Arizona               27517   972        148        4 #>  4 05    Arkansas              23789   709        165        5 #>  5 06    California            29454  1358        109        3 #>  6 08    Colorado              32401  1125        109        5 #>  7 09    Connecticut           35326  1123        195        5 #>  8 10    Delaware              31560  1076        247       10 #>  9 11    District of Columbia  43198  1424        681       17 #> 10 12    Florida               25952  1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. tibble as_tibble, tibble, tribble tidyselect all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, one_of, starts_with","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":null,"dir":"Reference","previous_headings":"","what":"Pew religion and income survey — relig_income","title":"Pew religion and income survey — relig_income","text":"Pew religion income survey","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pew religion and income survey — relig_income","text":"","code":"relig_income"},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pew religion and income survey — relig_income","text":"dataset variables: religion Name religion <$10k-Don\\'t know/refused Number respondees income range column name","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pew religion and income survey — relig_income","text":"Downloaded https://www.pewresearch.org/religious-landscape-study/database/ (downloaded November 2009)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NAs with specified values — replace_na","title":"Replace NAs with specified values — replace_na","text":"Replace NAs specified values","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NAs with specified values — replace_na","text":"","code":"replace_na(data, replace, ...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NAs with specified values — replace_na","text":"data data frame vector. replace data data frame, replace takes named list values, one value column missing values replaced. value replace cast type column data used replacement . data vector, replace takes single value. single value replaces missing values vector. replace cast type data. ... Additional arguments methods. Currently unused.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NAs with specified values — replace_na","text":"replace_na() returns object type data.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace NAs with specified values — replace_na","text":"","code":"# Replace NAs in a data frame df <- tibble(x = c(1, 2, NA), y = c(\"a\", NA, \"b\")) df %>% replace_na(list(x = 0, y = \"unknown\")) #> # A tibble: 3 × 2 #>       x y       #>   <dbl> <chr>   #> 1     1 a       #> 2     2 unknown #> 3     0 b        # Replace NAs in a vector df %>% dplyr::mutate(x = replace_na(x, 0)) #> # A tibble: 3 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     #> 2     2 NA    #> 3     0 b     # OR df$x %>% replace_na(0) #> [1] 1 2 0 df$y %>% replace_na(\"unknown\") #> [1] \"a\"       \"unknown\" \"b\"        # Replace NULLs in a list: NULLs are the list-col equivalent of NAs df_list <- tibble(z = list(1:5, NULL, 10:20)) df_list %>% replace_na(list(z = list(5))) #> # A tibble: 3 × 1 #>   z          #>   <list>     #> 1 <int [5]>  #> 2 <dbl [1]>  #> 3 <int [11]>"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"separate() superseded favour separate_wider_position() separate_wider_delim() two functions make two uses obvious, API polished, handling problems better. Superseded functions go away, receive critical bug fixes. Given either regular expression vector character positions, separate() turns single character column multiple columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"","code":"separate(   data,   col,   into,   sep = \"[^[:alnum:]]+\",   remove = TRUE,   convert = FALSE,   extra = \"warn\",   fill = \"warn\",   ... )"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"data data frame. col <tidy-select> Column expand. Names new variables create character vector. Use NA omit variable output. sep Separator columns. character, sep interpreted regular expression. default value regular expression matches sequence non-alphanumeric values. numeric, sep interpreted character positions split . Positive values start 1 far-left string; negative value start -1 far-right string. length sep one less . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. extra sep character vector, controls happens many pieces. three valid options: \"warn\" (default): emit warning drop extra values. \"drop\": drop extra values without warning. \"merge\": splits length() times fill sep character vector, controls happens enough pieces. three valid options: \"warn\" (default): emit warning fill right \"right\": fill missing values right \"left\": fill missing values left ... Additional arguments passed methods.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"","code":"# If you want to split by any non-alphanumeric value (the default): df <- tibble(x = c(NA, \"x.y\", \"x.z\", \"y.z\")) df %>% separate(x, c(\"A\", \"B\")) #> # A tibble: 4 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 x     y     #> 3 x     z     #> 4 y     z      # If you just want the second variable: df %>% separate(x, c(NA, \"B\")) #> # A tibble: 4 × 1 #>   B     #>   <chr> #> 1 NA    #> 2 y     #> 3 z     #> 4 z      # We now recommend separate_wider_delim() instead: df %>% separate_wider_delim(x, \".\", names = c(\"A\", \"B\")) #> # A tibble: 4 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 x     y     #> 3 x     z     #> 4 y     z     df %>% separate_wider_delim(x, \".\", names = c(NA, \"B\")) #> # A tibble: 4 × 1 #>   B     #>   <chr> #> 1 NA    #> 2 y     #> 3 z     #> 4 z      # Controlling uneven splits ------------------------------------------------- # If every row doesn't split into the same number of pieces, use # the extra and fill arguments to control what happens: df <- tibble(x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate(x, c(\"a\", \"b\")) #> Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3]. #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1]. #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 x     NA    #> 2 x     y     #> 3 x     y     #> 4 NA    NA    # The same behaviour as previous, but drops the c without warnings: df %>% separate(x, c(\"a\", \"b\"), extra = \"drop\", fill = \"right\") #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 x     NA    #> 2 x     y     #> 3 x     y     #> 4 NA    NA    # Opposite of previous, keeping the c and filling left: df %>% separate(x, c(\"a\", \"b\"), extra = \"merge\", fill = \"left\") #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 NA    x     #> 2 x     y     #> 3 x     y z   #> 4 NA    NA    # Or you can keep all three: df %>% separate(x, c(\"a\", \"b\", \"c\")) #> Warning: Expected 3 pieces. Missing pieces filled with `NA` in 2 rows [1, 2]. #> # A tibble: 4 × 3 #>   a     b     c     #>   <chr> <chr> <chr> #> 1 x     NA    NA    #> 2 x     y     NA    #> 3 x     y     z     #> 4 NA    NA    NA     # To only split a specified number of times use extra = \"merge\": df <- tibble(x = c(\"x: 123\", \"y: error: 7\")) df %>% separate(x, c(\"key\", \"value\"), \": \", extra = \"merge\") #> # A tibble: 2 × 2 #>   key   value    #>   <chr> <chr>    #> 1 x     123      #> 2 y     error: 7  # Controlling column types -------------------------------------------------- # convert = TRUE detects column classes: df <- tibble(x = c(\"x:1\", \"x:2\", \"y:4\", \"z\", NA)) df %>% separate(x, c(\"key\", \"value\"), \":\") %>% str() #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4]. #> tibble [5 × 2] (S3: tbl_df/tbl/data.frame) #>  $ key  : chr [1:5] \"x\" \"x\" \"y\" \"z\" ... #>  $ value: chr [1:5] \"1\" \"2\" \"4\" NA ... df %>% separate(x, c(\"key\", \"value\"), \":\", convert = TRUE) %>% str() #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4]. #> tibble [5 × 2] (S3: tbl_df/tbl/data.frame) #>  $ key  : chr [1:5] \"x\" \"x\" \"y\" \"z\" ... #>  $ value: int [1:5] 1 2 4 NA NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a string into rows — separate_longer_delim","title":"Split a string into rows — separate_longer_delim","text":"functions takes string splits multiple rows: separate_longer_delim() splits delimiter. separate_longer_position() splits fixed width.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a string into rows — separate_longer_delim","text":"","code":"separate_longer_delim(data, cols, delim, ...)  separate_longer_position(data, cols, width, ..., keep_empty = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a string into rows — separate_longer_delim","text":"data data frame. cols <tidy-select> Columns separate. delim separate_longer_delim(), string giving delimiter values. default, interpreted fixed string; use stringr::regex() friends split ways. ... dots future extensions must empty. width separate_longer_position(), integer giving number characters split . keep_empty default, get ceiling(nchar(x) / width) rows observation. nchar(x) zero, means entire input row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements missing value.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a string into rows — separate_longer_delim","text":"data frame based data. columns, different rows.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a string into rows — separate_longer_delim","text":"","code":"df <- tibble(id = 1:4, x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate_longer_delim(x, delim = \" \") #> # A tibble: 7 × 2 #>      id x     #>   <int> <chr> #> 1     1 x     #> 2     2 x     #> 3     2 y     #> 4     3 x     #> 5     3 y     #> 6     3 z     #> 7     4 NA     # You can separate multiple columns at once if they have the same structure df <- tibble(id = 1:3, x = c(\"x\", \"x y\", \"x y z\"), y = c(\"a\", \"a b\", \"a b c\")) df %>% separate_longer_delim(c(x, y), delim = \" \") #> # A tibble: 6 × 3 #>      id x     y     #>   <int> <chr> <chr> #> 1     1 x     a     #> 2     2 x     a     #> 3     2 y     b     #> 4     3 x     a     #> 5     3 y     b     #> 6     3 z     c      # Or instead split by a fixed length df <- tibble(id = 1:3, x = c(\"ab\", \"def\", \"\")) df %>% separate_longer_position(x, 1) #> # A tibble: 5 × 2 #>      id x     #>   <int> <chr> #> 1     1 a     #> 2     1 b     #> 3     2 d     #> 4     2 e     #> 5     2 f     df %>% separate_longer_position(x, 2) #> # A tibble: 3 × 2 #>      id x     #>   <int> <chr> #> 1     1 ab    #> 2     2 de    #> 3     2 f     df %>% separate_longer_position(x, 2, keep_empty = TRUE) #> # A tibble: 4 × 2 #>      id x     #>   <int> <chr> #> 1     1 ab    #> 2     2 de    #> 3     2 f     #> 4     3 NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate a collapsed column into multiple rows — separate_rows","title":"Separate a collapsed column into multiple rows — separate_rows","text":"separate_rows() superseded favour separate_longer_delim() consistent API separate functions. Superseded functions go away, receive critical bug fixes. variable contains observations multiple delimited values, separate_rows() separates values places one row.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate a collapsed column into multiple rows — separate_rows","text":"","code":"separate_rows(data, ..., sep = \"[^[:alnum:].]+\", convert = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate a collapsed column into multiple rows — separate_rows","text":"data data frame. ... <tidy-select> Columns separate across multiple rows sep Separator delimiting collapsed values. convert TRUE automatically run type.convert() key column. useful column types actually numeric, integer, logical.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate a collapsed column into multiple rows — separate_rows","text":"","code":"df <- tibble(   x = 1:3,   y = c(\"a\", \"d,e,f\", \"g,h\"),   z = c(\"1\", \"2,3,4\", \"5,6\") ) separate_rows(df, y, z, convert = TRUE) #> # A tibble: 6 × 3 #>       x y         z #>   <int> <chr> <int> #> 1     1 a         1 #> 2     2 d         2 #> 3     2 e         3 #> 4     2 f         4 #> 5     3 g         5 #> 6     3 h         6  # Now recommended df %>%   separate_longer_delim(c(y, z), delim = \",\") #> # A tibble: 6 × 3 #>       x y     z     #>   <int> <chr> <chr> #> 1     1 a     1     #> 2     2 d     2     #> 3     2 e     3     #> 4     2 f     4     #> 5     3 g     5     #> 6     3 h     6"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a string into columns — separate_wider_delim","title":"Split a string into columns — separate_wider_delim","text":"functions takes string column splits multiple new columns: separate_wider_delim() splits delimiter. separate_wider_position() splits fixed widths. separate_wider_regex() splits regular expression matches. functions equivalent separate() extract(), use stringr underlying string manipulation engine, interfaces reflect learned unnest_wider() unnest_longer().","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a string into columns — separate_wider_delim","text":"","code":"separate_wider_delim(   data,   cols,   delim,   ...,   names = NULL,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\", \"align_end\"),   too_many = c(\"error\", \"debug\", \"drop\", \"merge\"),   cols_remove = TRUE )  separate_wider_position(   data,   cols,   widths,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\"),   too_many = c(\"error\", \"debug\", \"drop\"),   cols_remove = TRUE )  separate_wider_regex(   data,   cols,   patterns,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\"),   cols_remove = TRUE )"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a string into columns — separate_wider_delim","text":"data data frame. cols <tidy-select> Columns separate. delim separate_wider_delim(), string giving delimiter values. default, interpreted fixed string; use stringr::regex() friends split ways. ... dots future extensions must empty. names separate_wider_delim(), character vector output column names. Use NA components want appear output; number non-NA elements determines number new columns result. names_sep supplied, output names composed input column name followed separator followed new column name. Required cols selects multiple columns. separate_wider_delim() can specify instead names, case names generated source column name, names_sep, numeric suffix. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . too_few happen value separates pieces? \"error\", default, throw error. \"debug\" adds additional columns output help locate resolve underlying problem. option intended help debug issue address generally remain final code. \"align_start\" aligns starts short matches, adding NA end pad correct length. \"align_end\" (separate_wider_delim() ) aligns ends short matches, adding NA start pad correct length. too_many happen value separates many pieces? \"error\", default, throw error. \"debug\" add additional columns output help locate resolve underlying problem. \"drop\" silently drop extra pieces. \"merge\" (separate_wider_delim() ) merge together additional pieces. cols_remove input cols removed output? Always FALSE too_few too_many set \"debug\". widths named numeric vector names become column names, values specify column width. Unnamed components match, included output. patterns named character vector names become column names values regular expressions match contents vector. Unnamed components match, included output.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a string into columns — separate_wider_delim","text":"data frame based data. rows, different columns: primary purpose functions create new columns components string. separate_wider_delim() names new columns come names. separate_wider_position() names come names widths. separate_wider_regex() names come names patterns. too_few too_many \"debug\", output contain additional columns useful debugging: {col}_ok: logical vector tells input ok . Use quickly find problematic rows. {col}_remainder: text remaining separation. {col}_pieces, {col}_width, {col}_matches: number pieces, number characters, number matches separate_wider_delim(), separate_wider_position() separate_regexp_wider() respectively. cols_remove = TRUE (default), input cols removed output.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a string into columns — separate_wider_delim","text":"","code":"df <- tibble(id = 1:3, x = c(\"m-123\", \"f-455\", \"f-123\")) # There are three basic ways to split up a string into pieces: # 1. with a delimiter df %>% separate_wider_delim(x, delim = \"-\", names = c(\"gender\", \"unit\")) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123   # 2. by length df %>% separate_wider_position(x, c(gender = 1, 1, unit = 3)) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123   # 3. defining each component with a regular expression df %>% separate_wider_regex(x, c(gender = \".\", \".\", unit = \"\\\\d+\")) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123    # Sometimes you split on the \"last\" delimiter df <- tibble(var = c(\"race_1\", \"race_2\", \"age_bucket_1\", \"age_bucket_2\")) # _delim won't help because it always splits on the first delimiter try(df %>% separate_wider_delim(var, \"_\", names = c(\"var1\", \"var2\"))) #> Error in separate_wider_delim(., var, \"_\", names = c(\"var1\", \"var2\")) :  #>   Expected 2 pieces in each element of `var`. #> ! 2 values were too long. #> ℹ Use `too_many = \"debug\"` to diagnose the problem. #> ℹ Use `too_many = \"drop\"/\"merge\"` to silence this message. df %>% separate_wider_delim(var, \"_\", names = c(\"var1\", \"var2\"), too_many = \"merge\") #> # A tibble: 4 × 2 #>   var1  var2     #>   <chr> <chr>    #> 1 race  1        #> 2 race  2        #> 3 age   bucket_1 #> 4 age   bucket_2 # Instead, you can use _regex df %>% separate_wider_regex(var, c(var1 = \".*\", \"_\", var2 = \".*\")) #> # A tibble: 4 × 2 #>   var1       var2  #>   <chr>      <chr> #> 1 race       1     #> 2 race       2     #> 3 age_bucket 1     #> 4 age_bucket 2     # this works because * is greedy; you can mimic the _delim behaviour with .*? df %>% separate_wider_regex(var, c(var1 = \".*?\", \"_\", var2 = \".*\")) #> # A tibble: 4 × 2 #>   var1  var2     #>   <chr> <chr>    #> 1 race  1        #> 2 race  2        #> 3 age   bucket_1 #> 4 age   bucket_2  # If the number of components varies, it's most natural to split into rows df <- tibble(id = 1:4, x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate_longer_delim(x, delim = \" \") #> # A tibble: 7 × 2 #>      id x     #>   <int> <chr> #> 1     1 x     #> 2     2 x     #> 3     2 y     #> 4     3 x     #> 5     3 y     #> 6     3 z     #> 7     4 NA    # But separate_wider_delim() provides some tools to deal with the problem # The default behaviour tells you that there's a problem try(df %>% separate_wider_delim(x, delim = \" \", names = c(\"a\", \"b\"))) #> Error in separate_wider_delim(., x, delim = \" \", names = c(\"a\", \"b\")) :  #>   Expected 2 pieces in each element of `x`. #> ! 1 value was too short. #> ℹ Use `too_few = \"debug\"` to diagnose the problem. #> ℹ Use `too_few = \"align_start\"/\"align_end\"` to silence this message. #> ! 1 value was too long. #> ℹ Use `too_many = \"debug\"` to diagnose the problem. #> ℹ Use `too_many = \"drop\"/\"merge\"` to silence this message. # You can get additional insight by using the debug options df %>%   separate_wider_delim(     x,     delim = \" \",     names = c(\"a\", \"b\"),     too_few = \"debug\",     too_many = \"debug\"   ) #> Warning: Debug mode activated: adding variables `x_ok`, `x_pieces`, and #> `x_remainder`. #> # A tibble: 4 × 7 #>      id a     b     x     x_ok  x_pieces x_remainder #>   <int> <chr> <chr> <chr> <lgl>    <int> <chr>       #> 1     1 x     NA    x     FALSE        1 \"\"          #> 2     2 x     y     x y   TRUE         2 \"\"          #> 3     3 x     y     x y z FALSE        3 \" z\"        #> 4     4 NA    NA    NA    TRUE        NA  NA          # But you can suppress the warnings df %>%   separate_wider_delim(     x,     delim = \" \",     names = c(\"a\", \"b\"),     too_few = \"align_start\",     too_many = \"merge\"   ) #> # A tibble: 4 × 3 #>      id a     b     #>   <int> <chr> <chr> #> 1     1 x     NA    #> 2     2 x     y     #> 3     3 x     y z   #> 4     4 NA    NA     # Or choose to automatically name the columns, producing as many as needed df %>% separate_wider_delim(x, delim = \" \", names_sep = \"\", too_few = \"align_start\") #> # A tibble: 4 × 4 #>      id x1    x2    x3    #>   <int> <chr> <chr> <chr> #> 1     1 x     NA    NA    #> 2     2 x     y     NA    #> 3     3 x     y     z     #> 4     4 NA    NA    NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":null,"dir":"Reference","previous_headings":"","what":"Some data about the Smith family — smiths","title":"Some data about the Smith family — smiths","text":"small demo dataset describing John Mary Smith.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Some data about the Smith family — smiths","text":"","code":"smiths"},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Some data about the Smith family — smiths","text":"data frame 2 rows 5 columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":null,"dir":"Reference","previous_headings":"","what":"Spread a key-value pair across multiple columns — spread","title":"Spread a key-value pair across multiple columns — spread","text":"Development spread() complete, new code recommend switching pivot_wider(), easier use, featureful, still active development. df %>% spread(key, value) equivalent df %>% pivot_wider(names_from = key, values_from = value) See details vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spread a key-value pair across multiple columns — spread","text":"","code":"spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spread a key-value pair across multiple columns — spread","text":"data data frame. key, value <tidy-select> Columns use key value. fill set, missing values replaced value. Note two types missingness input: explicit missing values (.e. NA), implicit missings, rows simply present. types missing value replaced fill. convert TRUE, type.convert() asis =   TRUE run new columns. useful value column mix variables coerced string. class value column factor date, note true new columns produced, coerced character type conversion. drop FALSE, keep factor levels appear data, filling missing combinations fill. sep NULL, column names taken values key variable. non-NULL, column names given \"<key_name><sep><key_value>\".","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spread a key-value pair across multiple columns — spread","text":"","code":"stocks <- tibble(   time = as.Date(\"2009-01-01\") + 0:9,   X = rnorm(10, 0, 1),   Y = rnorm(10, 0, 2),   Z = rnorm(10, 0, 4) ) stocksm <- stocks %>% gather(stock, price, -time) stocksm %>% spread(stock, price) #> # A tibble: 10 × 4 #>    time            X      Y        Z #>    <date>      <dbl>  <dbl>    <dbl> #>  1 2009-01-01 -2.05  -1.40   0.00192 #>  2 2009-01-02  0.151  1.95   3.02    #>  3 2009-01-03 -0.293 -0.154  1.37    #>  4 2009-01-04  0.255  1.79   0.674   #>  5 2009-01-05 -0.553 -1.56   5.59    #>  6 2009-01-06  1.41   0.874 -2.72    #>  7 2009-01-07 -0.795  0.827  2.95    #>  8 2009-01-08 -1.57   1.95  -3.44    #>  9 2009-01-09 -1.04   2.29   1.68    #> 10 2009-01-10  1.02   2.43   5.80    stocksm %>% spread(time, price) #> # A tibble: 3 × 11 #>   stock `2009-01-01` `2009-01-02` `2009-01-03` `2009-01-04` `2009-01-05` #>   <chr>        <dbl>        <dbl>        <dbl>        <dbl>        <dbl> #> 1 X         -2.05           0.151       -0.293        0.255       -0.553 #> 2 Y         -1.40           1.95        -0.154        1.79        -1.56  #> 3 Z          0.00192        3.02         1.37         0.674        5.59  #> # ℹ 5 more variables: `2009-01-06` <dbl>, `2009-01-07` <dbl>, #> #   `2009-01-08` <dbl>, `2009-01-09` <dbl>, `2009-01-10` <dbl>  # Spread and gather are complements df <- tibble(x = c(\"a\", \"b\"), y = c(3, 4), z = c(5, 6)) df %>%   spread(x, y) %>%   gather(\"x\", \"y\", a:b, na.rm = TRUE) #> # A tibble: 2 × 3 #>       z x         y #>   <dbl> <chr> <dbl> #> 1     5 a         3 #> 2     6 b         4  # Use 'convert = TRUE' to produce variables of mixed type df <- tibble(   row = rep(c(1, 51), each = 3),   var = rep(c(\"Sepal.Length\", \"Species\", \"Species_num\"), 2),   value = c(5.1, \"setosa\", 1, 7.0, \"versicolor\", 2) ) df %>% spread(var, value) %>% str() #> tibble [2 × 4] (S3: tbl_df/tbl/data.frame) #>  $ row         : num [1:2] 1 51 #>  $ Sepal.Length: chr [1:2] \"5.1\" \"7\" #>  $ Species     : chr [1:2] \"setosa\" \"versicolor\" #>  $ Species_num : chr [1:2] \"1\" \"2\" df %>% spread(var, value, convert = TRUE) %>% str() #> tibble [2 × 4] (S3: tbl_df/tbl/data.frame) #>  $ row         : num [1:2] 1 51 #>  $ Sepal.Length: num [1:2] 5.1 7 #>  $ Species     : chr [1:2] \"setosa\" \"versicolor\" #>  $ Species_num : int [1:2] 1 2"},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":null,"dir":"Reference","previous_headings":"","what":"Example tabular representations — table1","title":"Example tabular representations — table1","text":"Data sets demonstrate multiple ways layout tabular data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example tabular representations — table1","text":"","code":"table1  table2  table3  table4a  table4b  table5"},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example tabular representations — table1","text":"https://www..int/teams/global-tuberculosis-programme/data","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Example tabular representations — table1","text":"table1, table2, table3, table4a, table4b, table5 display number TB cases documented World Health Organization Afghanistan, Brazil, China 1999 2000. data contains values associated four variables (country, year, cases, population), table organizes values different layout. data subset data contained World Health Organization Global Tuberculosis Report","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tidyr: Tidy Messy Data — tidyr-package","title":"tidyr: Tidy Messy Data — tidyr-package","text":"Tools help create tidy data, column variable, row observation, cell contains single value. 'tidyr' contains tools changing shape (pivoting) hierarchy (nesting 'unnesting') dataset, turning deeply nested lists rectangular data frames ('rectangling'), extracting values string columns. also includes tools working missing values (implicit explicit).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tidyr: Tidy Messy Data — tidyr-package","text":"Maintainer: Hadley Wickham hadley@posit.co Authors: Davis Vaughan davis@posit.co Maximilian Girlich contributors: Kevin Ushey kevin@posit.co [contributor] Posit Software, PBC [copyright holder, funder]","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Argument type: data-masking — tidyr_data_masking","title":"Argument type: data-masking — tidyr_data_masking","text":"page describes <data-masking> argument modifier indicates argument uses data masking, sub-type tidy evaluation. never heard tidy evaluation , start practical introduction https://r4ds.hadley.nz/functions.html#data-frame-functions read underlying theory https://rlang.r-lib.org/reference/topic-data-mask.html.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":"key-techniques","dir":"Reference","previous_headings":"","what":"Key techniques","title":"Argument type: data-masking — tidyr_data_masking","text":"allow user supply column name function argument, embrace argument, e.g. filter(df, {{ var }}).   work column name recorded string, use .data pronoun, e.g. summarise(df, mean = mean(.data[[var]])).   suppress R CMD check NOTEs unknown variables use .data$var instead var:   also need import .data rlang (e.g.) @importFrom rlang .data.","code":"dist_summary <- function(df, var) {   df %>%     summarise(n = n(), min = min({{ var }}), max = max({{ var }})) } mtcars %>% dist_summary(mpg) mtcars %>% group_by(cyl) %>% dist_summary(mpg) for (var in names(mtcars)) {   mtcars %>% count(.data[[var]]) %>% print() }  lapply(names(mtcars), function(var) mtcars %>% count(.data[[var]])) # has NOTE df %>% mutate(z = x + y)  # no NOTE df %>% mutate(z = .data$x + .data$y)"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":"dot-dot-dot-","dir":"Reference","previous_headings":"","what":"Dot-dot-dot (...)","title":"Argument type: data-masking — tidyr_data_masking","text":"... automatically provides indirection, can use (.e. without embracing) inside function:   can also use := instead = enable glue-like syntax creating variables user supplied data:   Learn https://rlang.r-lib.org/reference/topic-data-mask-programming.html.","code":"grouped_mean <- function(df, var, ...) {   df %>%     group_by(...) %>%     summarise(mean = mean({{ var }})) } var_name <- \"l100km\" mtcars %>% mutate(\"{var_name}\" := 235 / mpg)  summarise_mean <- function(df, var) {   df %>%     summarise(\"mean_of_{{var}}\" := mean({{ var }})) } mtcars %>% group_by(cyl) %>% summarise_mean(mpg)"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy name repair — tidyr_legacy","title":"Legacy name repair — tidyr_legacy","text":"Ensures column names unique using approach found tidyr 0.8.3 earlier. use function want preserve naming strategy, otherwise better adopting new tidyverse standard name_repair = \"universal\"","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy name repair — tidyr_legacy","text":"","code":"tidyr_legacy(nms, prefix = \"V\", sep = \"\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy name repair — tidyr_legacy","text":"nms Character vector names prefix prefix Prefix use unnamed column sep Separator use name unique suffix","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy name repair — tidyr_legacy","text":"","code":"df <- tibble(x = 1:2, y = list(tibble(x = 3:5), tibble(x = 4:7)))  # Doesn't work because it would produce a data frame with two # columns called x if (FALSE) { # \\dontrun{ unnest(df, y) } # }  # The new tidyverse standard: unnest(df, y, names_repair = \"universal\") #> New names: #> • `x` -> `x...1` #> • `x` -> `x...2` #> # A tibble: 7 × 2 #>   x...1 x...2 #>   <int> <int> #> 1     1     3 #> 2     1     4 #> 3     1     5 #> 4     2     4 #> 5     2     5 #> 6     2     6 #> 7     2     7  # The old tidyr approach unnest(df, y, names_repair = tidyr_legacy) #> # A tibble: 7 × 2 #>       x    x1 #>   <int> <int> #> 1     1     3 #> 2     1     4 #> 3     1     5 #> 4     2     4 #> 5     2     5 #> 6     2     6 #> 7     2     7"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Argument type: tidy-select — tidyr_tidy_select","title":"Argument type: tidy-select — tidyr_tidy_select","text":"page describes <tidy-select> argument modifier indicates argument uses tidy selection, sub-type tidy evaluation. never heard tidy evaluation , start practical introduction https://r4ds.hadley.nz/functions.html#data-frame-functions read underlying theory https://rlang.r-lib.org/reference/topic-data-mask.html.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":"overview-of-selection-features","dir":"Reference","previous_headings":"","what":"Overview of selection features","title":"Argument type: tidy-select — tidyr_tidy_select","text":"tidyselect implements DSL selecting variables. provides helpers selecting variables: var1:var10: variables lying var1 left var10 right. starts_with(\"\"): names start \"\". ends_with(\"z\"): names end \"z\". contains(\"b\"): names contain \"b\". matches(\"x.y\"): names match regular expression x.y. num_range(x, 1:4): names following pattern, x1, x2, ..., x4. all_of(vars)/any_of(vars): matches names stored character vector vars. all_of(vars) error variables present; any_of(var) match just variables exist. everything(): variables. last_col(): furthest column right. (.numeric): variables .numeric() returns TRUE. well operators combining selections: !selection: variables match selection. selection1 & selection2: variables included selection1 selection2. selection1 | selection2: variables match either selection1 selection2.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":"key-techniques","dir":"Reference","previous_headings":"","what":"Key techniques","title":"Argument type: tidy-select — tidyr_tidy_select","text":"want user supply tidyselect specification function argument, need tunnel selection function argument. done embracing function argument {{ }}, e.g unnest(df, {{ vars }}). character vector column names, use all_of() any_of(), depending whether want unknown variable names cause error, e.g unnest(df, all_of(vars)), unnest(df, !any_of(vars)). suppress R CMD check NOTEs unknown variables use \"var\" instead var:","code":"# has NOTE df %>% select(x, y, z)  # no NOTE df %>% select(\"x\", \"y\", \"z\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":null,"dir":"Reference","previous_headings":"","what":"","title":"","text":"Performs opposite operation dplyr::count(), duplicating rows according weighting variable (expression).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"","text":"","code":"uncount(data, weights, ..., .remove = TRUE, .id = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"","text":"data data frame, tibble, grouped tibble. weights vector weights. Evaluated context data; supports quasiquotation. ... Additional arguments passed methods. .remove TRUE, weights name column data, column removed. .id Supply string create new variable gives unique identifier created row.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"","text":"","code":"df <- tibble(x = c(\"a\", \"b\"), n = c(1, 2)) uncount(df, n) #> # A tibble: 3 × 1 #>   x     #>   <chr> #> 1 a     #> 2 b     #> 3 b     uncount(df, n, .id = \"id\") #> # A tibble: 3 × 2 #>   x        id #>   <chr> <int> #> 1 a         1 #> 2 b         1 #> 3 b         2  # You can also use constants uncount(df, 2) #> # A tibble: 4 × 2 #>   x         n #>   <chr> <dbl> #> 1 a         1 #> 2 a         1 #> 3 b         2 #> 4 b         2  # Or expressions uncount(df, 2 / n) #> # A tibble: 3 × 2 #>   x         n #>   <chr> <dbl> #> 1 a         1 #> 2 a         1 #> 3 b         2"},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":null,"dir":"Reference","previous_headings":"","what":"Unite multiple columns into one by pasting strings together — unite","title":"Unite multiple columns into one by pasting strings together — unite","text":"Convenience function paste together multiple columns one.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unite multiple columns into one by pasting strings together — unite","text":"","code":"unite(data, col, ..., sep = \"_\", remove = TRUE, na.rm = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unite multiple columns into one by pasting strings together — unite","text":"data data frame. col name new column, string symbol. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). ... <tidy-select> Columns unite sep Separator use values. remove TRUE, remove input columns output data frame. na.rm TRUE, missing values removed prior uniting value.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unite multiple columns into one by pasting strings together — unite","text":"","code":"df <- expand_grid(x = c(\"a\", NA), y = c(\"b\", NA)) df #> # A tibble: 4 × 2 #>   x     y     #>   <chr> <chr> #> 1 a     b     #> 2 a     NA    #> 3 NA    b     #> 4 NA    NA     df %>% unite(\"z\", x:y, remove = FALSE) #> # A tibble: 4 × 3 #>   z     x     y     #>   <chr> <chr> <chr> #> 1 a_b   a     b     #> 2 a_NA  a     NA    #> 3 NA_b  NA    b     #> 4 NA_NA NA    NA    # To remove missing values: df %>% unite(\"z\", x:y, na.rm = TRUE, remove = FALSE) #> # A tibble: 4 × 3 #>   z     x     y     #>   <chr> <chr> <chr> #> 1 \"a_b\" a     b     #> 2 \"a\"   a     NA    #> 3 \"b\"   NA    b     #> 4 \"\"    NA    NA     # Separate is almost the complement of unite df %>%   unite(\"xy\", x:y) %>%   separate(xy, c(\"x\", \"y\")) #> # A tibble: 4 × 2 #>   x     y     #>   <chr> <chr> #> 1 a     b     #> 2 a     NA    #> 3 NA    b     #> 4 NA    NA    # (but note `x` and `y` contain now \"NA\" not NA)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column of data frames into rows and columns — unnest","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"Unnest expands list-column containing data frames rows columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"","code":"unnest(   data,   cols,   ...,   keep_empty = FALSE,   ptype = NULL,   names_sep = NULL,   names_repair = \"check_unique\",   .drop = deprecated(),   .id = deprecated(),   .sep = deprecated(),   .preserve = deprecated() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"data data frame. cols <tidy-select> List-columns unnest. selecting multiple columns, values row recycled common size. ... : previously write df %>% unnest(x, y, z). Convert df %>% unnest(c(x, y, z)). previously created new variable unnest() now need explicitly mutate(). Convert df %>% unnest(y = fun(x, y, z)) df %>% mutate(y = fun(x, y, z)) %>% unnest(y). keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. ptype Optionally, named list column name-prototype pairs coerce cols , overriding default guessed combining individual values. Alternatively, single empty ptype can supplied, applied cols. names_sep NULL, default, outer names come inner names. string, outer names formed pasting together outer inner column names, separated names_sep. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . .drop, .preserve : list-columns now preserved; want output use select() remove prior unnesting. .id : convert df %>% unnest(x, .id = \"id\") df %>% mutate(id = names(x)) %>% unnest(x)). .sep : use names_sep instead.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"new-syntax","dir":"Reference","previous_headings":"","what":"New syntax","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"tidyr 1.0.0 introduced new syntax nest() unnest() designed similar functions. Converting new syntax straightforward (guided message receive) just need run old analysis, can easily revert previous behaviour using nest_legacy() unnest_legacy() follows:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"","code":"# unnest() is designed to work with lists of data frames df <- tibble(   x = 1:3,   y = list(     NULL,     tibble(a = 1, b = 2),     tibble(a = 1:3, b = 3:1, c = 4)   ) ) # unnest() recycles input rows for each row of the list-column # and adds a column for each column df %>% unnest(y) #> # A tibble: 4 × 4 #>       x     a     b     c #>   <int> <dbl> <dbl> <dbl> #> 1     2     1     2    NA #> 2     3     1     3     4 #> 3     3     2     2     4 #> 4     3     3     1     4  # input rows with 0 rows in the list-column will usually disappear, # but you can keep them (generating NAs) with keep_empty = TRUE: df %>% unnest(y, keep_empty = TRUE) #> # A tibble: 5 × 4 #>       x     a     b     c #>   <int> <dbl> <dbl> <dbl> #> 1     1    NA    NA    NA #> 2     2     1     2    NA #> 3     3     1     3     4 #> 4     3     2     2     4 #> 5     3     3     1     4  # Multiple columns ---------------------------------------------------------- # You can unnest multiple columns simultaneously df <- tibble(   x = 1:2,   y = list(     tibble(a = 1, b = 2),     tibble(a = 3:4, b = 5:6)   ),   z = list(     tibble(c = 1, d = 2),     tibble(c = 3:4, d = 5:6)   ) ) df %>% unnest(c(y, z)) #> # A tibble: 3 × 5 #>       x     a     b     c     d #>   <int> <dbl> <dbl> <dbl> <dbl> #> 1     1     1     2     1     2 #> 2     2     3     5     3     5 #> 3     2     4     6     4     6  # Compare with unnesting one column at a time, which generates # the Cartesian product df %>%   unnest(y) %>%   unnest(z) #> # A tibble: 5 × 5 #>       x     a     b     c     d #>   <int> <dbl> <dbl> <dbl> <dbl> #> 1     1     1     2     1     2 #> 2     2     3     5     3     5 #> 3     2     3     5     4     6 #> 4     2     4     6     3     5 #> 5     2     4     6     4     6"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"unnest_auto() picks unnest_wider() unnest_longer() inspecting inner names list-col: elements unnamed, uses unnest_longer(indices_include = FALSE). elements named, least one name common across components, uses unnest_wider(). Otherwise, falls back unnest_longer(indices_include = TRUE). handy rapid interactive exploration recommend using scripts, succeed even underlying data radically changes.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"","code":"unnest_auto(data, col)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"data data frame. col <tidy-select> List-column unnest.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column into rows — unnest_longer","title":"Unnest a list-column into rows — unnest_longer","text":"unnest_longer() turns element list-column row. naturally suited list-columns elements unnamed length element varies row row. unnest_longer() generally preserves number columns x modifying number rows. Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column into rows — unnest_longer","text":"","code":"unnest_longer(   data,   col,   values_to = NULL,   indices_to = NULL,   indices_include = NULL,   keep_empty = FALSE,   names_repair = \"check_unique\",   simplify = TRUE,   ptype = NULL,   transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column into rows — unnest_longer","text":"data data frame. col <tidy-select> List-column(s) unnest. selecting multiple columns, values row recycled common size. values_to string giving column name (names) store unnested values . multiple columns specified col, can also glue string containing \"{col}\" provide template column names. default, NULL, gives output columns names input columns. indices_to string giving column name (names) store inner names positions (named) values. multiple columns specified col, can also glue string containing \"{col}\" provide template column names. default, NULL, gives output columns names values_to, suffixed \"_id\". indices_include single logical value specifying whether add index column. value inner names, index column character vector names, otherwise integer vector positions. NULL, defaults TRUE value inner names indices_to provided. indices_to provided, indices_include FALSE. keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column into rows — unnest_longer","text":"","code":"# `unnest_longer()` is useful when each component of the list should # form a row df <- tibble(   x = 1:4,   y = list(NULL, 1:3, 4:5, integer()) ) df %>% unnest_longer(y) #> # A tibble: 5 × 2 #>       x     y #>   <int> <int> #> 1     2     1 #> 2     2     2 #> 3     2     3 #> 4     3     4 #> 5     3     5  # Note that empty values like `NULL` and `integer()` are dropped by # default. If you'd like to keep them, set `keep_empty = TRUE`. df %>% unnest_longer(y, keep_empty = TRUE) #> # A tibble: 7 × 2 #>       x     y #>   <int> <int> #> 1     1    NA #> 2     2     1 #> 3     2     2 #> 4     2     3 #> 5     3     4 #> 6     3     5 #> 7     4    NA  # If the inner vectors are named, the names are copied to an `_id` column df <- tibble(   x = 1:2,   y = list(c(a = 1, b = 2), c(a = 10, b = 11, c = 12)) ) df %>% unnest_longer(y) #> # A tibble: 5 × 3 #>       x     y y_id  #>   <int> <dbl> <chr> #> 1     1     1 a     #> 2     1     2 b     #> 3     2    10 a     #> 4     2    11 b     #> 5     2    12 c      # Multiple columns ---------------------------------------------------------- # If columns are aligned, you can unnest simultaneously df <- tibble(   x = 1:2,   y = list(1:2, 3:4),   z = list(5:6, 7:8) ) df %>%   unnest_longer(c(y, z)) #> # A tibble: 4 × 3 #>       x     y     z #>   <int> <int> <int> #> 1     1     1     5 #> 2     1     2     6 #> 3     2     3     7 #> 4     2     4     8  # This is important because sequential unnesting would generate the # Cartesian product of the rows df %>%   unnest_longer(y) %>%   unnest_longer(z) #> # A tibble: 8 × 3 #>       x     y     z #>   <int> <int> <int> #> 1     1     1     5 #> 2     1     1     6 #> 3     1     2     5 #> 4     1     2     6 #> 5     2     3     7 #> 6     2     3     8 #> 7     2     4     7 #> 8     2     4     8"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column into columns — unnest_wider","title":"Unnest a list-column into columns — unnest_wider","text":"unnest_wider() turns element list-column column. naturally suited list-columns every element named, names consistent row--row. unnest_wider() preserves rows x modifying columns. Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column into columns — unnest_wider","text":"","code":"unnest_wider(   data,   col,   names_sep = NULL,   simplify = TRUE,   strict = FALSE,   names_repair = \"check_unique\",   ptype = NULL,   transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column into columns — unnest_wider","text":"data data frame. col <tidy-select> List-column(s) unnest. selecting multiple columns, values row recycled common size. names_sep NULL, default, names left . string, outer inner names pasted together using names_sep separator. values unnested unnamed, names_sep must supplied, otherwise error thrown. names_sep supplied, names automatically generated unnamed values increasing sequence integers. simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. strict single logical specifying whether apply strict vctrs typing rules. FALSE, typed empty values (like list() integer()) nested within list-columns treated like NULL contribute type unnested column. useful working JSON, empty values tend lose type information show list(). names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column into columns — unnest_wider","text":"","code":"df <- tibble(   character = c(\"Toothless\", \"Dory\"),   metadata = list(     list(       species = \"dragon\",       color = \"black\",       films = c(         \"How to Train Your Dragon\",         \"How to Train Your Dragon 2\",         \"How to Train Your Dragon: The Hidden World\"       )     ),     list(       species = \"blue tang\",       color = \"blue\",       films = c(\"Finding Nemo\", \"Finding Dory\")     )   ) ) df #> # A tibble: 2 × 2 #>   character metadata         #>   <chr>     <list>           #> 1 Toothless <named list [3]> #> 2 Dory      <named list [3]>  # Turn all components of metadata into columns df %>% unnest_wider(metadata) #> # A tibble: 2 × 4 #>   character species   color films     #>   <chr>     <chr>     <chr> <list>    #> 1 Toothless dragon    black <chr [3]> #> 2 Dory      blue tang blue  <chr [2]>  # Choose not to simplify list-cols of length-1 elements df %>% unnest_wider(metadata, simplify = FALSE) #> # A tibble: 2 × 4 #>   character species   color     films     #>   <chr>     <list>    <list>    <list>    #> 1 Toothless <chr [1]> <chr [1]> <chr [3]> #> 2 Dory      <chr [1]> <chr [1]> <chr [2]> df %>% unnest_wider(metadata, simplify = list(color = FALSE)) #> # A tibble: 2 × 4 #>   character species   color     films     #>   <chr>     <chr>     <list>    <list>    #> 1 Toothless dragon    <chr [1]> <chr [3]> #> 2 Dory      blue tang <chr [1]> <chr [2]>  # You can also widen unnamed list-cols: df <- tibble(   x = 1:3,   y = list(NULL, 1:3, 4:5) ) # but you must supply `names_sep` to do so, which generates automatic names: df %>% unnest_wider(y, names_sep = \"_\") #> # A tibble: 3 × 4 #>       x   y_1   y_2   y_3 #>   <int> <int> <int> <int> #> 1     1    NA    NA    NA #> 2     2     1     2     3 #> 3     3     4     5    NA  # 0-length elements --------------------------------------------------------- # The defaults of `unnest_wider()` treat empty types (like `list()`) as `NULL`. json <- list(   list(x = 1:2, y = 1:2),   list(x = list(), y = 3:4),   list(x = 3L, y = list()) )  df <- tibble(json = json) df %>%   unnest_wider(json) #> # A tibble: 3 × 2 #>   x         y         #>   <list>    <list>    #> 1 <int [2]> <int [2]> #> 2 <NULL>    <int [2]> #> 3 <int [1]> <NULL>     # To instead enforce strict vctrs typing rules, use `strict` df %>%   unnest_wider(json, strict = TRUE) #> # A tibble: 3 × 2 #>   x          y          #>   <list>     <list>     #> 1 <int [2]>  <int [2]>  #> 2 <list [0]> <int [2]>  #> 3 <int [1]>  <list [0]>"},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":null,"dir":"Reference","previous_headings":"","what":"US rent and income data — us_rent_income","title":"US rent and income data — us_rent_income","text":"Captured 2017 American Community Survey using tidycensus package.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US rent and income data — us_rent_income","text":"","code":"us_rent_income"},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US rent and income data — us_rent_income","text":"dataset variables: GEOID FIP state identifier NAME Name state variable Variable name: income = median yearly income, rent = median monthly rent estimate Estimated value moe 90% margin error","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":null,"dir":"Reference","previous_headings":"","what":"World Health Organization TB data — who","title":"World Health Organization TB data — who","text":"subset data World Health Organization Global Tuberculosis Report, accompanying global populations. uses original codes World Health Organization. column names columns 5 60 made combining new_ : method diagnosis (rel = relapse, sn = negative pulmonary smear, sp = positive pulmonary smear, ep = extrapulmonary), gender (f = female, m = male), age group (014 = 0-14 yrs age, 1524 = 15-24, 2534 = 25-34, 3544 = 35-44 years age, 4554 = 45-54, 5564 = 55-64, 65 = 65 years older). who2 lightly modified version makes teaching basics easier tweaking variables slightly consistent dropping iso2 iso3. newrel replaced new_rel, _ added gender.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"World Health Organization TB data — who","text":"","code":"who  who2  population"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"who","dir":"Reference","previous_headings":"","what":"who","title":"World Health Organization TB data — who","text":"data frame 7,240 rows 60 columns: country Country name iso2, iso3 2 & 3 letter ISO country codes year Year new_sp_m014 - new_rel_f65 Counts new TB cases recorded group. Column names encode three variables describe group.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"who-","dir":"Reference","previous_headings":"","what":"who2","title":"World Health Organization TB data — who","text":"data frame 7,240 rows 58 columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"population","dir":"Reference","previous_headings":"","what":"population","title":"World Health Organization TB data — who","text":"data frame 4,060 rows three columns: country Country name year Year population Population","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"World Health Organization TB data — who","text":"https://www..int/teams/global-tuberculosis-programme/data","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":null,"dir":"Reference","previous_headings":"","what":"Population data from the World Bank — world_bank_pop","title":"Population data from the World Bank — world_bank_pop","text":"Data population World Bank.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population data from the World Bank — world_bank_pop","text":"","code":"world_bank_pop"},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Population data from the World Bank — world_bank_pop","text":"dataset variables: country Three letter country code indicator Indicator name: SP.POP.GROW = population growth, SP.POP.TOTL = total population, SP.URB.GROW = urban population growth, SP.URB.TOTL = total urban population 2000-2018 Value year","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Population data from the World Bank — world_bank_pop","text":"Dataset World Bank data bank: https://data.worldbank.org","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-development-version","dir":"Changelog","previous_headings":"","what":"tidyr (development version)","title":"tidyr (development version)","text":"tidyr now requires dplyr >=1.1.0 (#1568, @catalamarti).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-131","dir":"Changelog","previous_headings":"","what":"tidyr 1.3.1","title":"tidyr 1.3.1","text":"CRAN release: 2024-01-24 pivot_wider now uses .|> syntax dplyr helper message identify duplicates (@boshek, #1516)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-130","dir":"Changelog","previous_headings":"","what":"tidyr 1.3.0","title":"tidyr 1.3.0","text":"CRAN release: 2023-01-24","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-1-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 1.3.0","text":"New family consistent string separating functions: separate_wider_delim(), separate_wider_position(), separate_wider_regex(), separate_longer_delim(), separate_longer_position(). functions thorough refreshes separate() extract(), featuring improved performance, greater consistency, polished API, new approach handling problems. use stringr supersede extract(), separate(), separate_rows() (#1304). named character vector interface used separate_wider_regex() similar nc package Toby Dylan Hocking. nest() gains .argument allows specify columns nest (rather columns nest, .e. ...). Additionally, .key argument longer deprecated, used whenever ... isn’t specified (#1458). unnest_longer() gains keep_empty argument like unnest() (#1339). pivot_longer() gains cols_vary argument controlling ordering output rows relative original row number (#1312). New datasets who2, household, cms_patient_experience, cms_patient_care demonstrate various tidying challenges (#1333).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.3.0","text":"... argument pivot_longer() pivot_wider() moved front function signature, required arguments optional ones. Additionally, pivot_longer_spec(), pivot_wider_spec(), build_longer_spec(), build_wider_spec() gained ... arguments similar location. change allows us easily add new features pivoting functions without breaking existing CRAN packages user scripts. pivot_wider() provides temporary backwards compatible support case single unnamed argument previously positionally matched id_cols. one special case still works, throw warning encouraging explicitly name id_cols argument. read pattern, see https://design.tidyverse.org/dots--required.html (#1350).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"lifecycle-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Lifecycle changes","title":"tidyr 1.3.0","text":"functions deprecated tidyr 1.0 1.2 (old lazyeval functions ending _ various arguments unnest()) now warn every use. made defunct 2024 (#1406).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-3-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.3.0","text":"unnest_longer() now consistently drops rows either NULL empty vectors (like integer()) default. Set new keep_empty argument TRUE retain . Previously, keep_empty = TRUE implicitly used NULL, keep_empty = FALSE used empty vectors, inconsistent tidyr verbs argument (#1363). unnest_longer() now uses \"\" index column fully unnamed vectors. also now consistently uses NA index column empty vectors “kept” keep_empty = TRUE (#1442). unnest_wider() now errors values unnested unnamed names_sep provided (#1367). unnest_wider() now generates automatic names partially unnamed vectors. Previously generated fully unnamed vectors, resulting strange mix automatic names name-repaired names (#1367).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"General","title":"tidyr 1.3.0","text":"tidyr functions now consistently disallow renaming tidy-selection. Renaming never meaningful functions, previously either effect caused problems (#1449, #1104). tidyr errors (including input validation) thoroughly reviewed generally likely point right direction (#1313, #1400). uncount() now generic implementations can provided objects data frames (@mgirlich, #1358). uncount() gains ... argument. comes required optional arguments (@mgirlich, #1358). nest(), complete(), expand(), fill() now document support grouped data frames created dplyr::group_by() (#952). built datasets now standard tibbles (#1459). R >=3.4.0 now required, line tidyverse standard supporting previous 5 minor releases R. rlang >=1.0.4 vctrs >=0.5.2 now required (#1344, #1470). Removed dependency ellipsis favor equivalent functions rlang (#1314).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-packing-and-chopping-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Nesting, packing, and chopping","title":"tidyr 1.3.0","text":"unnest(), unchop(), unnest_longer(), unnest_wider() better handle lists additional classes (#1327). pack(), unpack(), chop(), unchop() gain error_call argument, turn improves error calls shown nest() various unnest() adjacent functions (#1446). chop(), unpack(), unchop() gain ..., must empty (#1447). unpack() better job reporting column name duplication issues gives better advice resolve using names_sep. also improves errors functions use unpack(), like unnest() unnest_wider() (#1425, #1367).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Pivoting","title":"tidyr 1.3.0","text":"pivot_longer() longer supports interpreting values_ptypes = list() names_ptypes = list() NULL. empty list() now interpreted <list> prototype apply columns, consistent 0-length value interpreted (#1296). pivot_longer(values_drop_na = TRUE) faster aren’t missing values drop (#1392, @mgirlich). pivot_longer() now memory efficient due usage vctrs::vec_interleave() (#1310, @mgirlich). pivot_longer() now throws slightly better error message values_ptypes names_ptypes provided coercion can’t made (#1364). pivot_wider() now throws better error message column selected names_from values_from also selected id_cols (#1318). pivot_wider() now faster names_sep provided (@mgirlich, #1426). pivot_longer_spec(), pivot_wider_spec(), build_longer_spec(), build_wider_spec() gain error_call argument, resulting better error reporting pivot_longer() pivot_wider() (#1408).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Missing values","title":"tidyr 1.3.0","text":"fill() now works correctly column named .direction data (#1319, @tjmahr). replace_na() faster aren’t missing values replace (#1392, @mgirlich). documentation replace argument replace_na() now mentions replace always cast type data (#1317).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-121","dir":"Changelog","previous_headings":"","what":"tidyr 1.2.1","title":"tidyr 1.2.1","text":"CRAN release: 2022-09-08 Hot patch release resolve R CMD check failures.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-120","dir":"Changelog","previous_headings":"","what":"tidyr 1.2.0","title":"tidyr 1.2.0","text":"CRAN release: 2022-02-01","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.2.0","text":"complete() expand() longer allow complete expand grouping column. never well-defined since completion/expansion grouped data frame happens “within” group otherwise potential produce erroneous results (#1299). replace_na() longer allows type data change replacement applied. replace now always cast type data replacement made. example, means using replacement value 1.5 integer column longer allowed. Similarly, replacing missing values list-column must now done list(\"foo\") rather just \"foo\".","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-2-0","dir":"Changelog","previous_headings":"","what":"Pivoting","title":"tidyr 1.2.0","text":"pivot_wider() gains new names_expand id_expand arguments turning implicit missing factor levels variable combinations explicit ones. similar drop argument spread() (#770). pivot_wider() gains new names_vary argument controlling ordering combining names_from values values_from column names (#839). pivot_wider() gains new unused_fn argument controlling summarize unused columns aren’t involved pivoting process (#990, thanks @mgirlich initial implementation). pivot_longer()’s names_transform values_transform arguments now accept single function applied columns (#1284, thanks @smingerson initial implementation). pivot_longer()’s names_ptypes values_ptypes arguments now accept single empty ptype applied columns (#1284).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-2-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.2.0","text":"unnest() unchop()’s ptype argument now accepts single empty ptype applied cols (#1284). unpack() now silently skips non-data frame columns specified cols. matches existing behavior unchop() unnest() (#1153).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-2-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.2.0","text":"unnest_wider() unnest_longer() can now unnest multiple columns (#740). unnest_longer()’s indices_to values_to arguments now accept glue specification, useful unnesting multiple columns. hoist(), unnest_longer(), unnest_wider(), ptype supplied, column can’t simplified, result list-column element type ptype (#998). unnest_wider() gains new strict argument controls whether strict vctrs typing rules applied. defaults FALSE backwards compatibility, often useful lax unnesting JSON, doesn’t always map one--one R’s types (#1125). hoist(), unnest_longer(), unnest_wider()’s simplify argument now accepts named list TRUE FALSE control simplification per column basis (#995). hoist(), unnest_longer(), unnest_wider()’s transform argument now accepts single function applied components (#1284). hoist(), unnest_longer(), unnest_wider()’s ptype argument now accepts single empty ptype applied components (#1284).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"grids-1-2-0","dir":"Changelog","previous_headings":"","what":"Grids","title":"tidyr 1.2.0","text":"complete() gains new explicit argument limiting fill implicit missing values. useful don’t want fill pre-existing missing values (#1270). complete() gains grouped data frame method. generates correct completed data frame groups involved (#396, #966).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-2-0","dir":"Changelog","previous_headings":"","what":"Missing values","title":"tidyr 1.2.0","text":"drop_na(), replace_na(), fill() updated utilize vctrs. means can use functions wider variety column types, including lubridate’s Period types (#1094), data frame columns, rcrd type vctrs. replace_na() longer replaces empty atomic elements list-columns (like integer(0)). value replaced list-column NULL (#1168). drop_na() longer drops empty atomic elements list-columns (like integer(0)). value dropped list-column NULL (#1228).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-1-2-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"General","title":"tidyr 1.2.0","text":"@mgirlich now tidyr author recognition significant sustained contributions. lazyeval variants tidyr verbs soft-deprecated. Expect move defunct stage next minor release tidyr (#1294). any_of() all_of() tidyselect now re-exported (#1217). dplyr >= 1.0.0 now required.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Pivoting","title":"tidyr 1.2.0","text":"pivot_wider() now gives better advice identify duplicates values uniquely identified (#1113). pivot_wider() now throws informative error values_fn doesn’t result single summary value (#1238). pivot_wider() pivot_longer() now generate informative errors related name repair (#987). pivot_wider() now works correctly values_fill data frame. pivot_wider() longer accidentally retains values_from pivoting zero row data frame (#1249). pivot_wider() now correctly handles case id column name collides value names_from (#1107). pivot_wider() pivot_longer() now check spec columns .name .value character vectors. Additionally, .name column must unique (#1107). pivot_wider()’s names_from values_from arguments now required default values name value don’t correspond columns data. Additionally, must identify least 1 column data (#1240). pivot_wider()’s values_fn argument now correctly allows anonymous functions (#1114). pivot_wider_spec() now works correctly 0-row data frame spec doesn’t identify rows (#1250, #1252). pivot_longer()’s names_ptypes argument now applied names_transform consistency rectangling functions (.e. hoist()) (#1233). check_pivot_spec() new developer facing function validating pivot spec argument. useful extending pivot_longer() pivot_wider() new S3 methods (#1087).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Nesting","title":"tidyr 1.2.0","text":"nest() generic now avoids computing .data, making compatible lazy tibbles (#1134). .names_sep argument data.frame method nest() now actually used (#1174). unnest()’s ptype argument now works expected (#1158). unpack() longer drops empty columns specified cols (#1191). unpack() now works correctly data frame columns containing 1 row 0 columns (#1189). chop() now works correctly data frames 0 rows (#1206). chop()’s cols argument longer optional. matches behavior cols seen elsewhere tidyr (#1205). unchop() now respects ptype unnesting non-list column (#1211).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Rectangling","title":"tidyr 1.2.0","text":"hoist() longer accidentally removes elements duplicated names (#1259).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"grids-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Grids","title":"tidyr 1.2.0","text":"grouped data frame methods complete() expand() now move group columns front result (addition columns completed expanded, already moved front). make intuitive sense, completing expanding “within” group, group columns first thing see (#1289). complete() now applies fill even columns complete specified (#1272). expand(), crossing(), nesting() now correctly retain NA values factors (#1275). expand_grid(), expand(), nesting(), crossing() now silently apply name repair automatically named inputs. avoids number issues resulting duplicate truncated names (#1116, #1221, #1092, #1037, #992). expand_grid(), expand(), nesting(), crossing() now allow columns unnamed data frames used expressions data frame specified, like expand_grid(tibble(x = 1), y = x). consistent tibble() behaves. expand_grid(), expand(), nesting(), crossing() now work correctly data frames containing 0 columns >0 rows (#1189). expand_grid(), expand(), nesting(), crossing() now return 1 row data frame inputs supplied, consistent prod() == 1L idea computations involving number combinations computed empty set return 1 (#1258).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Missing values","title":"tidyr 1.2.0","text":"drop_na() longer drops missing values columns tidyselect expression results 0 columns selected used (#1227). fill() now treats NaN like missing value (#982).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-114","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.4","title":"tidyr 1.1.4","text":"CRAN release: 2021-09-27 expand_grid() now twice fast pivot_wider() bit faster (@mgirlich, #1130). unchop() now much faster, propagates various functions, unnest(), unnest_longer(), unnest_wider(), separate_rows() (@mgirlich, @DavisVaughan, #1127). unnest() now much faster (@mgirlich, @DavisVaughan, #1127). unnest() longer allows unnesting list-col containing mix vector data frame elements. Previously, worked accident, considered -label usage unnest() now become error.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-113","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.3","title":"tidyr 1.1.3","text":"CRAN release: 2021-03-03 tidyr verbs longer “default” methods lazyeval fallbacks. means ’ll get clearer error messages (#1036). uncount() error non-integer weights gives clearer error message negative weights (@mgirlich, #1069). can unnest dates (#1021, #1089). pivot_wider() works data.table empty key variables (@mgirlich, #1066). separate_rows() works factor columns (@mgirlich, #1058).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-112","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.2","title":"tidyr 1.1.2","text":"CRAN release: 2020-08-27 separate_rows() returns 1.1.0 behaviour empty strings (@rjpatm, #1014).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-111","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.1","title":"tidyr 1.1.1","text":"CRAN release: 2020-07-31 New tidyr logo! stringi dependency removed; substantial dependency make tidyr hard compile resource constrained environments (@rjpat, #936). Replace Rcpp cpp11. See https://cpp11.r-lib.org/articles/motivations.html reasons .","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-110","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.0","title":"tidyr 1.1.0","text":"CRAN release: 2020-05-20","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-features-1-1-0","dir":"Changelog","previous_headings":"","what":"General features","title":"tidyr 1.1.0","text":"pivot_longer(), hoist(), unnest_wider(), unnest_longer() gain new transform arguments; allow transform values “flight”. partly needed vctrs coercion rules become stricter, give greater flexibility available previously (#921). Arguments use tidy selection syntax now clearly documented updated use tidyselect 1.1.0 (#872).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Pivoting improvements","title":"tidyr 1.1.0","text":"pivot_wider() pivot_longer() considerably performant, thanks largely improvements underlying vctrs code (#790, @DavisVaughan). pivot_longer() now supports names_to = character() prevents name column created (#961). {r}   df <- tibble(id = 1:3, x_1 = 1:3, x_2 = 4:6)     df %>% pivot_longer(-id, names_to = character()) pivot_longer() longer creates .copy variable presence duplicate column names. makes consistent handling non-unique specs. pivot_longer() automatically disambiguates non-unique ouputs, can occur input variables include additional component don’t care want discard (#792, #793). {r}   df <- tibble(id = 1:3, x_1 = 1:3, x_2 = 4:6)     df %>% pivot_longer(-id, names_pattern = \"(.)_.\")   df %>% pivot_longer(-id, names_sep = \"_\", names_to = c(\"name\", NA))   df %>% pivot_longer(-id, names_sep = \"_\", names_to = c(\".value\", NA)) pivot_wider() gains names_sort argument allows sort column names order. default, FALSE, orders columns first appearance (#839). future version, ’ll consider changing default TRUE. pivot_wider() gains names_glue argument allows construct output column names glue specification. pivot_wider() arguments values_fn values_fill can now single values; now need use named list want use different values different value columns (#739, #746). also get improved errors ’re expected type.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-1-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.1.0","text":"hoist() now automatically names pluckers single string (#837). error use duplicated column names (@mgirlich, #834), now uses rlang::list2() behind scenes (means can now use !!! :=) (#801). unnest_longer(), unnest_wider(), hoist() better job simplifying list-cols. longer add unneeded unspecified() result still list (#806), work list contains non-vectors (#810, #848). unnest_wider(names_sep = \"\") now provides default names unnamed inputs, suppressing many previous name repair messages (#742).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-1-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.1.0","text":"pack() nest() gains .names_sep argument allows strip outer names inner names, symmetrical way argument unpack() unnest() combines inner outer names (#795, #797). unnest_wider() unnest_longer() can now unnest list_of columns. important unnesting columns created nest() pivot_wider(), create list_of columns id columns non-unique (#741).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 1.1.0","text":"chop() now creates list-columns class vctrs::list_of(). helps keep track type case chopped data frame empty, allowing unchop() reconstitute data frame correct number types column even observations. drop_na() now preserves attributes unclassed vectors (#905). expand(), expand_grid(), crossing(), nesting() evaluate inputs iteratively, can refer freshly created columns, e.g. crossing(x = seq(-2, 2), y = x) (#820). expand(), expand_grid(), crossing(), nesting() gain .name_repair giving control name repair strategy (@jeffreypullin, #798). extract() lets use NA , documented (#793). extract(), separate(), hoist(), unnest_longer(), unnest_wider() give better error message col missing (#805). pack()’s first argument now .data instead data (#759). pivot_longer() now errors values_to length-1 character vector (#949). pivot_longer() pivot_wider() now generic implementations can provided objects data frames (#800). pivot_wider() can now pivot data frame columns (#926) unite(na.rm = TRUE) now works types variable, just character vectors (#765). unnest_wider() gives better error message attempt unnest multiple columns (#740). unnest_auto() works input data contains column called col (#959).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-102","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.2","title":"tidyr 1.0.2","text":"CRAN release: 2020-01-24 Minor fixes dev versions rlang, tidyselect, tibble.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-101","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.1","title":"tidyr 1.0.1","text":"exist since accidentally released v1.0.2","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-100","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.0","title":"tidyr 1.0.0","text":"CRAN release: 2019-09-11","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.0.0","text":"See vignette(\"-packages\") detailed transition guide. nest() unnest() new syntax. majority existing usage automatically translated new syntax warning. doesn’t work, put script use old versions can take closer look update code: nest() now preserves grouping, implications downstream calls group-aware functions, dplyr::mutate() filter(). first argument nest() changed data .data. unnest() uses emerging tidyverse standard disambiguate unique names. Use names_repair = tidyr_legacy request previous approach. unnest_()/nest_() lazyeval methods unnest()/nest() now defunct. deprecated time, , since interface changed, package authors need update avoid deprecation warnings. think one clean break less work everyone. lazyeval functions formally deprecated, made defunct next major release. (See lifecycle vignette details deprecation stages). crossing() nesting() now return 0-row outputs input length-0 vector. want preserve previous behaviour silently dropped inputs, convert empty vectors NULL. (discussion general pattern https://github.com/tidyverse/principles/issues/24)","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-0-0","dir":"Changelog","previous_headings":"","what":"Pivoting","title":"tidyr 1.0.0","text":"New pivot_longer() pivot_wider() provide modern alternatives spread() gather(). carefully redesigned easier learn remember, include many new features. Learn vignette(\"pivot\"). functions resolve multiple existing issues spread()/gather(). functions now handle mulitple value columns (#149/#150), support vector types (#333), use tidyverse conventions duplicated column names (#496, #478), symmetric (#453). pivot_longer() gracefully handles duplicated column names (#472), can directly split column names multiple variables. pivot_wider() can now aggregate (#474), select keys (#572), control generated column names (#208). demonstrate functions work practice, tidyr gained several new datasets: relig_income, construction, billboard, us_rent_income, fish_encounters world_bank_pop. Finally, tidyr demos removed. dated, superseded vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-0-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.0.0","text":"tidyr contains four new functions support rectangling, turning deeply nested list tidy tibble: unnest_longer(), unnest_wider(), unnest_auto(), hoist(). documented new vignette: vignette(\"rectangle\"). unnest_longer() unnest_wider() make easier unnest list-columns vectors either rows columns (#418). unnest_auto() automatically picks _longer() _wider() using heuristics based presence common names. New hoist() provides convenient way plucking components list-column top-level columns (#341). particularly useful working deeply nested JSON, provides convenient shortcut mutate() + map() pattern: {r} df %>% hoist(metadata, name = \"name\") # shortcut df %>% mutate(name = map_chr(metadata, \"name\"))","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-0-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.0.0","text":"nest() unnest() updated new interfaces closely aligned evolving tidyverse conventions. use theory developed vctrs consistently handle mixtures input types, arguments overhauled based last years experience. supported new vignette(\"nest\"), outlines main ideas nested data (’s still rough, get better time). biggest change operation multiple columns: df %>% unnest(x, y, z) becomes df %>% unnest(c(x, y, z)) df %>% nest(x, y, z) becomes df %>% nest(data = c(x, y, z)). done best ensure common uses nest() unnest() continue work, generating informative warning telling precisely need update code. Please file issue ’ve missed important use case. unnest() overhauled: New keep_empty parameter ensures every row input gets least one row output, inserting missing values needed (#358). Provides names_sep argument control inner outer column names combined. Uses standard tidyverse name-repair rules, default get error output contain multiple columns name. can override using name_repair (#514). Now supports NULL entries (#436).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"packing-and-chopping-1-0-0","dir":"Changelog","previous_headings":"","what":"Packing and chopping","title":"tidyr 1.0.0","text":"hood, nest() unnest() implemented chop(), pack(), unchop(), unpack(): pack() unpack() allow pack unpack columns data frame columns (#523). chop() unchop() chop rows sets list-columns. Packing chopping interesting primarily atomic operations underlying nesting (similarly, unchop unpacking underlie unnesting), don’t expect used directly often.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-1-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 1.0.0","text":"New expand_grid(), tidy version expand.grid(), lower-level existing expand() crossing() functions, takes individual vectors, sort uniquify . crossing(), nesting(), expand() rewritten use vctrs package. affect much existing code, considerably simplies implementation ensures functions work consistently across generalised vectors (#557). part alignment, functions now drop NULL inputs, 0-length vector.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-1-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 1.0.0","text":"full_seq() now also works gaps observations shorter given period, within tolerance given tol. Previously, gaps consecutive observations range [period, period + tol]; gaps can now range [period - tol, period + tol] (@ha0ye, #657). tidyr now re-exports tibble(), as_tibble(), tribble(), well tidyselect helpers (starts_with(), ends_width(), …). makes generating documentation, reprexes, tests easier, makes tidyr easier use without also attaching dplyr. functions take ... instrumented functions ellipsis package warn ’ve supplied arguments ignored (typically ’ve misspelled argument name) (#573). complete() now uses full_join() levels preserved even levels specified (@Ryo-N7, #493). crossing() now takes unique values data frame inputs, just vector inputs (#490). gather() throws error column data frame (#553). extract() (hence pivot_longer()) can extract multiple input values single output column (#619). fill() now implemented using dplyr::mutate_at(). radically simplifies implementation considerably improves performance working grouped data (#520). fill() now accepts downup updown fill directions (@coolbutuseless, #505). unite() gains na.rm argument, making easier remove missing values prior uniting values together (#203)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-083","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.3","title":"tidyr 0.8.3","text":"CRAN release: 2019-03-01 crossing() preserves factor levels (#410), now works list-columns (#446, @SamanthaToet). (also help expand() built top crossing()) nest() compatible dplyr 0.8.0. spread() works id variable names (#525). unnest() preserves column unnested input zero-length (#483), using list_of() attribute correctly restore columns, possible. unnest() run named unnamed list-columns length (@hlendway, #460).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-082","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.2","title":"tidyr 0.8.2","text":"CRAN release: 2018-10-28 separate() now accepts NA column name argument denote columns omitted result. (@markdly, #397). Minor updates ensure compatibility dependencies.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-081","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.1","title":"tidyr 0.8.1","text":"CRAN release: 2018-05-18 unnest() weakens test “atomicity” restore previous behaviour unnesting factors dates (#407).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-080","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.0","title":"tidyr 0.8.0","text":"CRAN release: 2018-01-29","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 0.8.0","text":"deliberate breaking changes release. However, number packages failing errors related numbers elements columns, row names. possible accidental API changes new bugs. see error package, sincerely appreciate minimal reprex. separate() now correctly uses -1 refer far right position, instead -2. depended behaviour, ’ll need switch packageVersion(\"tidyr\") > \"0.7.2\"","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-0-8-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 0.8.0","text":"Increased test coverage 84% 99%. uncount() performs inverse operation dplyr::count() (#279)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.8.0","text":"complete(data) now returns data rather throwing error (#390). complete() zero-length completions returns original input (#331). crossing() preserves NAs (#364). expand() empty input gives empty data frame instead NULL (#331). expand(), crossing(), complete() now complete empty factors instead dropping (#270, #285) extract() better error message regex contain expected number groups (#313). drop_na() longer drops columns (@jennybryan, #245), works list-cols (#280). Equivalent NA list column empty (length 0) data structure. nest() now faster, especially long data frame collapsed nested data frame rows. nest() zero-row data frame works expected (#320). replace_na() longer complains try replace missing values variables present data (#356). replace_na() now also works vectors (#342, @flying-sheep), can replace NULL list-columns. throws better error message attempt replace something length 1. separate() longer checks ... empty, allowing methods make use . check added tidyr 0.4.0 (2016-02-02) deprecate previous behaviour ... passed strsplit(). separate() extract() now insert columns correct position drop = TRUE (#394). separate() now works correctly counts RHS using negative integer sep values (@markdly, #315). separate() gets improved warning message pieces aren’t expected (#375). separate_rows() supports list columns (#321), works empty tibbles. spread() now consistently returns 0 row outputs 0 row inputs (#269). spread() now works key column includes NA drop FALSE (#254). spread() longer returns tibbles row names (#322). spread(), separate(), extract() (#255), gather() (#347) now replace existing variables rather creating invalid data frame duplicated variable names (matching semantics mutate). unite() now works (documented) don’t supply variables (#355). unnest() gains preserve argument allows preserve list columns without unnesting (#328). unnest() can unnested list-columns contains lists lists (#278). unnest(df) now works df contains list-cols (#344)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-072","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.2","title":"tidyr 0.7.2","text":"CRAN release: 2017-10-16 SE variants gather_(), spread_() nest_() now treat non-syntactic names way pre tidy eval versions tidyr (#361). Fix tidyr bug revealed R-devel.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-071","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.1","title":"tidyr 0.7.1","text":"CRAN release: 2017-09-01 hotfix release account tidyselect changes unit tests. Note upcoming version tidyselect backtracks changes announced 0.7.0. special evaluation semantics selection changed back old behaviour new rules causing much trouble confusion. now data expressions (symbols calls : c()) can refer registered variables objects context. However semantics context expressions (calls : c()) remain . expressions evaluated context refer registered variables. ’re writing functions refer contextual objects, still good idea avoid data expressions following advice 0.7.0 release notes.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-070","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.0","title":"tidyr 0.7.0","text":"CRAN release: 2017-08-16 release includes important changes tidyr internals. Tidyr now supports new tidy evaluation framework quoting (NSE) functions. also uses new tidyselect package selecting backend.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 0.7.0","text":"see error messages objects functions found, likely selecting functions now stricter arguments example selecting function gather() ... argument. change makes code robust disallowing ambiguous scoping. Consider following code: select first three columns (using x defined global environment), select first two columns (using column named x)? solve ambiguity, now make strict distinction data context expressions. data expression either bare name expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. practice means can longer refer contextual objects like : now explicit find objects. , can use quasiquotation operator !! evaluate argument early inline result: {r} mtcars %>% gather(var, value, !! 1:ncol(mtcars)) mtcars %>% gather(var, value, !! 1:x) mtcars %>% gather(var, value, !! -(1:x)) alternative turn data expression context expression using seq() seq_len() instead :. See section tidyselect information semantics. Following switch tidy evaluation, might see warnings “variable context set”. likely caused supplying helpers like everything() underscored versions tidyr verbs. Helpers always evaluated lazily. fix , just quote helper formula: drop_na(df,   ~everything()). selecting functions now stricter supply integer positions. see error along lines please round positions supplying tidyr. Double vectors fine long rounded.","code":"x <- 3 df <- tibble(w = 1, x = 2, y = 3) gather(df, \"variable\", \"value\", 1:x) mtcars %>% gather(var, value, 1:ncol(mtcars))  x <- 3 mtcars %>% gather(var, value, 1:x) mtcars %>% gather(var, value, -(1:x)) `-0.949999999999999`, `-0.940000000000001`, ... must resolve to integer column positions, not a double vector"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"switch-to-tidy-evaluation-0-7-0","dir":"Changelog","previous_headings":"","what":"Switch to tidy evaluation","title":"tidyr 0.7.0","text":"tidyr now tidy evaluation grammar. See programming vignette dplyr practical information tidy evaluation. tidyr port bit special. philosophy tidy evaluation R code refer real objects (data frame context), make exceptions rule tidyr. reason several functions accept bare symbols specify names new columns create (gather() prime example). tidy symbol represent actual object. workaround capture arguments using rlang::quo_name() (still support quasiquotation can unquote symbols strings). type NSE now discouraged tidyverse: symbols R code represent real objects. Following switch tidy eval underscored variants softly deprecated. However remain around time without warning backward compatibility.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"switch-to-the-tidyselect-backend-0-7-0","dir":"Changelog","previous_headings":"","what":"Switch to the tidyselect backend","title":"tidyr 0.7.0","text":"selecting backend dplyr extracted standalone package tidyselect tidyr now uses selecting variables. used selecting multiple variables (drop_na()) well single variables (col argument extract() separate(), key value arguments spread()). implies following changes: arguments selecting single variable now support features dplyr::pull(). can supply name position, including negative positions. Multiple variables now selected bit differently. now make strict distinction data context expressions. data expression either bare name expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. can still refer contextual objects data expression explicit. One way explicit unquote variable environment tidy eval operator !!: hand, select helpers like start_with() context expressions. therefore easy refer objects never ambiguous data columns: {r} x <- \"d\" drop_na(df, starts_with(x)) special rules contrast dplyr tidyr verbs (data context scope) make sense selecting functions provide robust helpful semantics.","code":"x <- 2 drop_na(df, 2)     # Works fine drop_na(df, x)     # Object 'x' not found drop_na(df, !! x)  # Works as if you had supplied 2"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-063","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.3","title":"tidyr 0.6.3","text":"CRAN release: 2017-05-15 Patch tests compatible dev tibble","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-062","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.2","title":"tidyr 0.6.2","text":"CRAN release: 2017-05-04 Register C functions Added package docs Patch tests compatible dev dplyr.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-061","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.1","title":"tidyr 0.6.1","text":"CRAN release: 2017-01-10 Patch test compatible dev tibble Changed deprecation message extract_numeric() point readr::parse_number() rather readr::parse_numeric()","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-060","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.0","title":"tidyr 0.6.0","text":"CRAN release: 2016-08-12","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"api-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"API changes","title":"tidyr 0.6.0","text":"drop_na() removes observations NA given variables. variables given, variables considered (#194, @janschulz). extract_numeric() deprecated (#213). Renamed table4 table5 table4a table4b make connection clear. key value variables table2 renamed type count.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.6.0","text":"expand(), crossing(), nesting() now silently drop zero-length inputs. crossing_() nesting_() versions crossing() nesting() take list input. full_seq() works correctly dates date/times.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-051","dir":"Changelog","previous_headings":"","what":"tidyr 0.5.1","title":"tidyr 0.5.1","text":"CRAN release: 2016-06-14 Restored compatibility R < 3.3.0 avoiding getS3method(envir = ) (#205, @krlmlr).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-050","dir":"Changelog","previous_headings":"","what":"tidyr 0.5.0","title":"tidyr 0.5.0","text":"CRAN release: 2016-06-12","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-functions-0-5-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tidyr 0.5.0","text":"separate_rows() separates observations multiple delimited values separate rows (#69, @aaronwolen).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.5.0","text":"complete() preserves grouping created dplyr (#168). expand() (hence complete()) preserves ordered attribute factors (#165). full_seq() preserve attributes dates date/times (#156), sequences longer need start 0. gather() can now gather together list columns (#175), gather_.data.frame(na.rm = TRUE) now removes missing values ’re actually present (#173). nest() returns correct output every variable nested (#186). separate() fills right--left (left--right!) fill = “left” (#170, @dgrtwo). separate() unite() now automatically drop removed variables grouping (#159, #177). spread() gains sep argument. -null, name columns “keyvalue”. Additionally, sep NULL missing values converted <NA> (#68). spread() works presence list-columns (#199) unnest() works non-syntactic names (#190). unnest() gains sep argument. non-null, rename columns nested data frames include original column name, nested column name, separated .sep (#184). unnest() gains .id argument works way bind_rows(). useful named list data frames vectors (#125). Moved useful sample datasets DSR package. Made compatible dplyr 0.4 0.5. tidyr functions create new columns aggressive re-encoding column names UTF-8.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-041","dir":"Changelog","previous_headings":"","what":"tidyr 0.4.1","title":"tidyr 0.4.1","text":"CRAN release: 2016-02-05 Fixed bug nest() nested data ending wrong row (#158).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-040","dir":"Changelog","previous_headings":"","what":"tidyr 0.4.0","title":"tidyr 0.4.0","text":"CRAN release: 2016-01-18","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nested-data-frames-0-4-0","dir":"Changelog","previous_headings":"","what":"Nested data frames","title":"tidyr 0.4.0","text":"nest() unnest() overhauled support useful way structuring data frames: nested data frame. grouped data frame, one row per observation, additional metadata define groups. nested data frame, one row per group, individual observations stored column list data frames. useful structure lists objects (like models) one element per group. nest() now produces single list data frames called “data” rather list column variable. Nesting variables included nested data frames. also works grouped data frames made dplyr::group_by(). can override default column name .key. unnest() gains .drop argument controls happens list columns. default, ’re kept output doesn’t require row duplication; otherwise ’re dropped. unnest() now mutate() semantics ... - allows unnest transformed columns easily. (Previously used select semantics).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"expanding-0-4-0","dir":"Changelog","previous_headings":"","what":"Expanding","title":"tidyr 0.4.0","text":"expand() allows evaluate arbitrary expressions like full_seq(year). previously using c() created nested combinations, ’ll now need use nesting() (#85, #121). nesting() crossing() allow create nested crossed data frames individual vectors. crossing() similar base::expand.grid() full_seq(x, period) creates full sequence values min(x) max(x) every period values.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"minor-bug-fixes-and-improvements-0-4-0","dir":"Changelog","previous_headings":"","what":"Minor bug fixes and improvements","title":"tidyr 0.4.0","text":"fill() fills NULLs list-columns. fill() gains direction argument can fill either upwards downwards (#114). gather() now stores key column character, default. revert previous behaviour using factor (allows preserve ordering columns), use key_factor = TRUE (#96). tidyr verbs right thing grouped data frames created group_by() (#122, #129, #81). seq_range() removed. never used announced. spread() creates columns mixed type convert = TRUE (#118, @jennybc). spread() drop = FALSE handles zero-length factors (#56). spread()ing data frame key value columns creates one row output (#41). unite() now removes old columns adding new (#89, @krlmlr). separate() now warns defunct … argument used (#151, @krlmlr).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-031","dir":"Changelog","previous_headings":"","what":"tidyr 0.3.1","title":"tidyr 0.3.1","text":"CRAN release: 2015-09-10 Fixed bug attributes non-gather columns lost (#104)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-030","dir":"Changelog","previous_headings":"","what":"tidyr 0.3.0","title":"tidyr 0.3.0","text":"CRAN release: 2015-09-08","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 0.3.0","text":"New complete() provides wrapper around expand(), left_join() replace_na() common task: completing data frame missing combinations variables. fill() fills missing values column last non-missing value (#4). New replace_na() makes easy replace missing values something meaningful data. nest() complement unnest() (#3). unnest() can now work multiple list-columns time. don’t supply columns names, unlist list-columns (#44). unnest() can also handle columns lists data frames (#58).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.3.0","text":"tidyr longer depends reshape2. fix issues also try load reshape (#88). %>% re-exported magrittr. expand() now supports nesting crossing (see examples details). comes expense creating new variables inline (#46). expand_ SE evaluation correctly can pass character vector columns names (list formulas etc) (#70). extract() 10x faster now uses stringi instead base R regular expressions. also returns NA instead throwing error regular expression doesn’t match (#72). extract() separate() preserve character vectors convert TRUE (#99). internals spread() rewritten, now preserve attributes input value column. means can now spread date (#62) factor (#35) inputs. spread() gives informative error message key value don’t exist input data (#36). separate() displays first 20 failures (#50). finer control happens two matches: can fill missing values either “left” “right” (#49). separate() longer throws error number pieces aren’t expected - instead uses drops extra values fills right gives warning. input NA separate() extract() return silently return NA outputs, rather throwing error. (#77) Experimental unnest() method lists removed.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-020","dir":"Changelog","previous_headings":"","what":"tidyr 0.2.0","title":"tidyr 0.2.0","text":"CRAN release: 2014-12-05","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-functions-0-2-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tidyr 0.2.0","text":"Experimental expand() function (#21). Experiment unnest() function converting named lists data frames. (#3, #22)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.2.0","text":"extract_numeric() preserves negative signs (#20). gather() better defaults key value supplied. ... omitted, gather() selects columns (#28). Performance now comparable reshape2::melt() (#18). separate() gains extra argument lets control happens extra pieces. default throw “error”, can also “merge” “drop”. spread() gains drop argument, allows preserve missing factor levels (#25). converts factor value variables character vectors, instead embedding matrix inside data frame (#35).","code":""}]
+[{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement codeofconduct@posit.co. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tidyr","title":"Contributing to tidyr","text":"outlines propose change tidyr. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to tidyr","text":"Small typos grammatical errors documentation may edited directly using GitHub web interface, long changes made source file. YES: edit roxygen comment .R file R/. : edit .Rd file man/.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"prerequisites","dir":"","previous_headings":"","what":"Prerequisites","title":"Contributing to tidyr","text":"make substantial pull request, always file issue make sure someone team agrees ’s problem. ’ve found bug, create associated issue illustrate bug minimal reprex.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"","what":"Pull request process","title":"Contributing to tidyr","text":"recommend create Git branch pull request (PR). Look Travis AppVeyor build status making changes. README contain badges continuous integration services used package. New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat. Contributions test cases included easier accept. user-facing changes, add bullet top NEWS.md current development version header describing changes made followed GitHub username, links relevant issue(s)/PR(s).","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to tidyr","text":"Please note tidyr project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://tidyr.tidyverse.org/dev/CONTRIBUTING.html","id":"see-tidyverse-development-contributing-guide","dir":"","previous_headings":"","what":"See tidyverse development contributing guide","title":"Contributing to tidyr","text":"details.","code":""},{"path":"https://tidyr.tidyverse.org/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 tidyr authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tidyr.tidyverse.org/dev/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with tidyr","title":"Getting help with tidyr","text":"Thanks using tidyr. filing issue, places explore pieces put together make process smooth possible. Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! section tidyverse site. Armed reprex, next step figure ask. ’s question: start forum.posit.co, /StackOverflow. people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed. right place, need file issue, please review “File issues” paragraph tidyverse contributing guidelines. Thanks help!","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"In packages","text":"vignette serves two distinct, related, purposes: documents general best practices using tidyr package, inspired using ggplot2 packages. describes migration patterns transition tidyr v0.8.3 v1.0.0. release includes breaking changes nest() unnest() order increase consistency within tidyr rest tidyverse. go , ’ll attach packages use, expose version tidyr, make small dataset use examples.","code":"library(tidyr) library(dplyr, warn.conflicts = FALSE) library(purrr)  packageVersion(\"tidyr\") #> [1] '1.3.1.9000'  mini_iris <- as_tibble(iris)[c(1, 2, 51, 52, 101, 102), ] mini_iris #> # A tibble: 6 × 5 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <fct>      #> 1          5.1         3.5          1.4         0.2 setosa     #> 2          4.9         3            1.4         0.2 setosa     #> 3          7           3.2          4.7         1.4 versicolor #> 4          6.4         3.2          4.5         1.5 versicolor #> 5          6.3         3.3          6           2.5 virginica  #> 6          5.8         2.7          5.1         1.9 virginica"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"using-tidyr-in-packages","dir":"Articles","previous_headings":"","what":"Using tidyr in packages","title":"In packages","text":"assume ’re already familiar using tidyr functions, described vignette(\"programming.Rmd\"). two important considerations using tidyr package: avoid R CMD CHECK notes using fixed variable names. alert upcoming changes development version tidyr.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"fixed-column-names","dir":"Articles","previous_headings":"Using tidyr in packages","what":"Fixed column names","title":"In packages","text":"know column names, code works way regardless whether inside outside package: R CMD check warn undefined global variables (Petal.Length, Petal.Width, Sepal.Length, Sepal.Width), doesn’t know nest() looking variables inside mini_iris (.e. Petal.Length friends data-variables, env-variables). easiest way silence note use all_of(). all_of() tidyselect helper (like starts_with(), ends_with(), etc.) takes column names stored strings: Alternatively, may want use any_of() OK specified variables found input data. tidyselect package offers entire family select helpers. probably already familiar using dplyr::select().","code":"mini_iris %>% nest(   petal = c(Petal.Length, Petal.Width),   sepal = c(Sepal.Length, Sepal.Width) ) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]> mini_iris %>% nest(   petal = all_of(c(\"Petal.Length\", \"Petal.Width\")),   sepal = all_of(c(\"Sepal.Length\", \"Sepal.Width\")) ) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"continuous-integration","dir":"Articles","previous_headings":"Using tidyr in packages","what":"Continuous integration","title":"In packages","text":"Hopefully ’ve already adopted continuous integration package, R CMD check (includes tests) run regular basis, e.g. every time push changes package’s source GitHub similar. tidyverse team currently relies heavily GitHub Actions, example. usethis::use_github_action() can help get started. recommend adding workflow targets devel version tidyr. ? Always? package tightly coupled tidyr, consider leaving place time, know changes tidyr affect package. Right tidyr release? everyone else, add (re-activate existing) tidyr-devel workflow period preceding major tidyr release potential breaking changes, especially ’ve contacted reverse dependency checks. Example GitHub Actions workflow tests package development version tidyr: GitHub Actions evolving landscape, can always mine workflows tidyr (tidyverse/tidyr/.github/workflows) main r-lib/actions repo ideas.","code":"on:   push:     branches:       - main   pull_request:     branches:       - main  name: R-CMD-check-tidyr-devel  jobs:   R-CMD-check:     runs-on: macOS-latest     steps:       - uses: actions/checkout@v4       - uses: r-lib/actions/setup-r@v2       - name: Install dependencies         run: |           install.packages(c(\"remotes\", \"rcmdcheck\"))           remotes::install_deps(dependencies = TRUE)           remotes::install_github(\"tidyverse/tidyr\")         shell: Rscript {0}       - name: Check         run: rcmdcheck::rcmdcheck(args = \"--no-manual\", error_on = \"error\")         shell: Rscript {0}"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"tidyr-v0-8-3---v1-0-0","dir":"Articles","previous_headings":"","what":"tidyr v0.8.3 -> v1.0.0","title":"In packages","text":"v1.0.0 makes considerable changes interface nest() unnest() order bring line newer tidyverse conventions. tried make functions backward compatible possible give informative warning messages, cover 100% use cases, may need change package code. guide help minimum pain. Ideally, ’ll tweak package works tidyr 0.8.3 tidyr 1.0.0. makes life considerably easier means ’s need coordinate CRAN submissions - can submit package works tidyr versions, submit tidyr CRAN. section describes recommend practices , drawing general principles described https://design.tidyverse.org/changes-multivers.html. use continuous integration already, strongly recommend adding build tests development version tidyr; see details. section briefly describes run different code different versions tidyr, goes major changes might require workarounds: nest() unnest() get new interfaces. nest() preserves groups. nest_() unnest_() defunct. ’re struggling problem ’s described , please reach via github email can help .","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"conditional-code","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"Conditional code","title":"In packages","text":"Sometimes ’ll able write code works v0.8.3 v1.0.0. often requires code ’s particularly natural either version ’d better (temporarily) separate code paths, containing non-contrived code. get re-use existing code “old” branch, eventually phased , write clean, forward-looking code “new” branch. basic approach looks like . First define function returns TRUE new versions tidyr: highly recommend keeping function provides obvious place jot transition notes package, makes easier remove transitional code later . Another benefit tidyr version determined run time, build time, therefore detect user’s current tidyr version. functions, use statement call different code different versions: new code uses function exists tidyr 1.0.0, get NOTE R CMD check: one notes can explain CRAN submission comments. Just mention ’s forward compatibility tidyr 1.0.0, CRAN let package .","code":"tidyr_new_interface <- function() {   packageVersion(\"tidyr\") > \"0.8.99\" } my_function_inside_a_package <- function(...)   # my code here    if (tidyr_new_interface()) {     # Freshly written code for v1.0.0     out <- tidyr::nest(df, data = any_of(c(\"x\", \"y\", \"z\")))   } else {     # Existing code for v0.8.3     out <- tidyr::nest(df, x, y, z)   }    # more code here }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"new-syntax-for-nest","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"New syntax for nest()","title":"In packages","text":"changed: --nested columns longer accepted “loose parts”. new list-column’s name longer provided via .key argument. Now use construct like : new_col = <something existing cols>. changed: use ... metadata problematic pattern ’re moving away . https://design.tidyverse.org/dots-data.html new_col = <something existing cols> construct lets us create multiple nested list-columns (“multi-nest”). examples: need quick dirty fix without think, just call nest_legacy() instead nest(). ’s nest() v0.8.3:","code":"mini_iris %>%   nest(petal = matches(\"Petal\"), sepal = matches(\"Sepal\")) #> # A tibble: 3 × 3 #>   Species    petal            sepal            #>   <fct>      <list>           <list>           #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]> #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]> #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]> # v0.8.3 mini_iris %>%   nest(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, .key = \"my_data\")  # v1.0.0 mini_iris %>%   nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))  # v1.0.0 avoiding R CMD check NOTE mini_iris %>%   nest(my_data = any_of(c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")))  # or equivalently: mini_iris %>%   nest(my_data = !any_of(\"Species\")) if (tidyr_new_interface()) {   out <- tidyr::nest_legacy(df, x, y, z) } else {   out <- tidyr::nest(df, x, y, z) }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"new-syntax-for-unnest","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"New syntax for unnest()","title":"In packages","text":"changed: --unnested columns must now specified explicitly, instead defaulting list-columns. also deprecates .drop .preserve. .sep deprecated replaced names_sep. unnest() uses emerging tidyverse standard disambiguate duplicated names. Use names_repair = tidyr_legacy request previous approach. .id deprecated can easily replaced creating column names prior unnest(), e.g. upstream call mutate(). changed: use ... metadata problematic pattern ’re moving away . https://design.tidyverse.org/dots-data.html changes details arguments relate features rolling across multiple packages tidyverse. example, ptype exposes prototype support new vctrs package. names_repair specifies duplicated non-syntactic names, consistent tibble readxl. : need quick dirty fix without think, just call unnest_legacy() instead unnest(). ’s unnest() v0.8.3:","code":"# v0.8.3 df %>% unnest(x, .id = \"id\")  # v1.0.0 df %>% mutate(id = names(x)) %>% unnest(x)) nested <- mini_iris %>%   nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))  # v0.8.3 automatically unnests list-cols nested %>% unnest()  # v1.0.0 must be told which columns to unnest nested %>% unnest(any_of(\"my_data\")) if (tidyr_new_interface()) {   out <- tidyr::unnest_legacy(df) } else {   out <- tidyr::unnest(df) }"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"nest-preserves-groups","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"nest() preserves groups","title":"In packages","text":"changed: nest() now preserves groups present input. changed: reflect growing support grouped data frames, especially recent releases dplyr. See, example, dplyr::group_modify(), group_map(), friends. fact nest() now preserves groups problematic downstream, choices: Apply ungroup() result. level pragmatism suggests, however, least consider next two options. never grouped first place. Eliminate group_by() call specify columns nested versus nested directly nest(). Adjust downstream code accommodate grouping. Imagine used group_by() nest() mini_iris, computed list-column outside data frame. now try add back data post hoc: fails df grouped mutate() group-aware, ’s hard add completely external variable. pragmatically ungroup()ing, can ? One option work inside data frame, .e. bring map() inside mutate(), design problem away: , somehow, grouping seems appropriate working inside data frame option, tibble::add_column() group-unaware. lets add external data grouped data frame.","code":"(df <- mini_iris %>%    group_by(Species) %>%    nest()) #> # A tibble: 3 × 2 #> # Groups:   Species [3] #>   Species    data             #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> (external_variable <- map_int(df$data, nrow)) #> [1] 2 2 2 df %>%   mutate(n_rows = external_variable) #> Error in `mutate()`: #> ℹ In argument: `n_rows = external_variable`. #> ℹ In group 1: `Species = setosa`. #> Caused by error: #> ! `n_rows` must be size 1, not 3. df %>%   mutate(n_rows = map_int(data, nrow)) #> # A tibble: 3 × 3 #> # Groups:   Species [3] #>   Species    data             n_rows #>   <fct>      <list>            <int> #> 1 setosa     <tibble [2 × 4]>      2 #> 2 versicolor <tibble [2 × 4]>      2 #> 3 virginica  <tibble [2 × 4]>      2 df %>%   tibble::add_column(n_rows = external_variable) #> # A tibble: 3 × 3 #> # Groups:   Species [3] #>   Species    data             n_rows #>   <fct>      <list>            <int> #> 1 setosa     <tibble [2 × 4]>      2 #> 2 versicolor <tibble [2 × 4]>      2 #> 3 virginica  <tibble [2 × 4]>      2"},{"path":"https://tidyr.tidyverse.org/dev/articles/in-packages.html","id":"nest_-and-unnest_-are-defunct","dir":"Articles","previous_headings":"tidyr v0.8.3 -> v1.0.0","what":"nest_() and unnest_() are defunct","title":"In packages","text":"changed: nest_() unnest_() longer work changed: Specialized standard evaluation versions functions, e.g., foo_() complement foo(). older lazyeval framework. :","code":"# v0.8.3 mini_iris %>%   nest_(     key_col = \"my_data\",     nest_cols = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")   )  nested %>% unnest_(~ my_data)  # v1.0.0 mini_iris %>%   nest(my_data = any_of(c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\")))  nested %>% unnest(any_of(\"my_data\"))"},{"path":"https://tidyr.tidyverse.org/dev/articles/nest.html","id":"basics","dir":"Articles","previous_headings":"","what":"Basics","title":"Nested data","text":"nested data frame data frame one () columns list data frames. can create simple nested data frames hand: (possible create list-columns regular data frames, just tibbles, ’s considerably work default behaviour data.frame() treat lists lists columns.) commonly ’ll create tidyr::nest(): nest() specifies variables nested inside; alternative use dplyr::group_by() describe variables kept outside. think nesting easiest understand connection grouped data: row output corresponds one group input. ’ll see shortly particularly convenient per-group objects. opposite nest() unnest(). give name list-column containing data frames, row-binds data frames together, repeating outer columns right number times line .","code":"df1 <- tibble(   g = c(1, 2, 3),   data = list(     tibble(x = 1, y = 2),     tibble(x = 4:5, y = 6:7),     tibble(x = 10)   ) )  df1 #> # A tibble: 3 × 2 #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 1]> df2 <- tribble(   ~g, ~x, ~y,    1,  1,  2,    2,  4,  6,    2,  5,  7,    3, 10,  NA ) df2 %>% nest(data = c(x, y)) #> # A tibble: 3 × 2 #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]> df2 %>% group_by(g) %>% nest() #> # A tibble: 3 × 2 #> # Groups:   g [3] #>       g data             #>   <dbl> <list>           #> 1     1 <tibble [1 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]> df1 %>% unnest(data) #> # A tibble: 4 × 3 #>       g     x     y #>   <dbl> <dbl> <dbl> #> 1     1     1     2 #> 2     2     4     6 #> 3     2     5     7 #> 4     3    10    NA"},{"path":"https://tidyr.tidyverse.org/dev/articles/nest.html","id":"nested-data-and-models","dir":"Articles","previous_headings":"","what":"Nested data and models","title":"Nested data","text":"Nested data great fit problems one something group. common place arises ’re fitting multiple models. list data frames, ’s natural produce list models: even produce list predictions: workflow works particularly well conjunction broom, makes easy turn models tidy data frames can unnest()ed get back flat data frames. can see bigger example broom dplyr vignette.","code":"mtcars_nested <- mtcars %>%   group_by(cyl) %>%   nest()  mtcars_nested #> # A tibble: 3 × 2 #> # Groups:   cyl [3] #>     cyl data               #>   <dbl> <list>             #> 1     6 <tibble [7 × 10]>  #> 2     4 <tibble [11 × 10]> #> 3     8 <tibble [14 × 10]> mtcars_nested <- mtcars_nested %>%   mutate(model = map(data, function(df) lm(mpg ~ wt, data = df))) mtcars_nested #> # A tibble: 3 × 3 #> # Groups:   cyl [3] #>     cyl data               model  #>   <dbl> <list>             <list> #> 1     6 <tibble [7 × 10]>  <lm>   #> 2     4 <tibble [11 × 10]> <lm>   #> 3     8 <tibble [14 × 10]> <lm> mtcars_nested <- mtcars_nested %>%   mutate(model = map(model, predict)) mtcars_nested #> # A tibble: 3 × 3 #> # Groups:   cyl [3] #>     cyl data               model      #>   <dbl> <list>             <list>     #> 1     6 <tibble [7 × 10]>  <dbl [7]>  #> 2     4 <tibble [11 × 10]> <dbl [11]> #> 3     8 <tibble [14 × 10]> <dbl [14]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Pivoting","text":"vignette describes use new pivot_longer() pivot_wider() functions. goal improve usability gather() spread(), incorporate state---art features found packages. time, ’s obvious something fundamentally wrong design spread() gather(). Many people don’t find names intuitive find hard remember direction corresponds spreading gathering. also seems surprisingly hard remember arguments functions, meaning many people (including !) consult documentation every time. two important new features inspired R packages advancing reshaping R: pivot_longer() can work multiple value variables may different types, inspired enhanced melt() dcast() functions provided data.table package Matt Dowle Arun Srinivasan. pivot_longer() pivot_wider() can take data frame specifies precisely metadata stored column names becomes data variables (vice versa), inspired cdata package John Mount Nina Zumel. vignette, ’ll learn key ideas behind pivot_longer() pivot_wider() see used solve variety data reshaping challenges ranging simple complex. begin ’ll load needed packages. real analysis code, ’d imagine ’d library(tidyverse), can’t since vignette embedded package.","code":"library(tidyr) library(dplyr) library(readr)"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer","dir":"Articles","previous_headings":"","what":"Longer","title":"Pivoting","text":"pivot_longer() makes datasets longer increasing number rows decreasing number columns. don’t believe makes sense describe dataset “long form”. Length relative term, can say (e.g.) dataset longer dataset B. pivot_longer() commonly needed tidy wild-caught datasets often optimise ease data entry ease comparison rather ease analysis. following sections show use pivot_longer() wide range realistic datasets.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"pew","dir":"Articles","previous_headings":"Longer","what":"String data in column names","title":"Pivoting","text":"relig_income dataset stores counts based survey (among things) asked people religion annual income: dataset contains three variables: religion, stored rows, income spread across column names, count stored cell values. tidy use pivot_longer(): first argument dataset reshape, relig_income. cols describes columns need reshaped. case, ’s every column apart religion. names_to gives name variable created data stored column names, .e. income. values_to gives name variable created data stored cell value, .e. count. Neither names_to values_to column exists relig_income, provide strings surrounded quotes.","code":"relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> # ℹ 8 more rows #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(     cols = !religion,     names_to = \"income\",     values_to = \"count\"   ) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"billboard","dir":"Articles","previous_headings":"Longer","what":"Numeric data in column names","title":"Pivoting","text":"billboard dataset records billboard rank songs year 2000. form similar relig_income data, data encoded column names really number, string. can start basic specification relig_income dataset. want names become variable called week, values become variable called rank. also use values_drop_na drop rows correspond missing values. every song stays charts 76 weeks, structure input data force creation unnecessary explicit NAs. nice easily determine long song stayed charts, , ’ll need convert week variable integer. can using two additional arguments: names_prefix strips wk prefix, names_transform converts week integer: Alternatively, single argument using readr::parse_number() automatically strips non-numeric components:","code":"billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 #> # ℹ 5,297 more rows billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_prefix = \"wk\",     names_transform = as.integer,     values_to = \"rank\",     values_drop_na = TRUE,   ) billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_transform = readr::parse_number,     values_to = \"rank\",     values_drop_na = TRUE,   )"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"many-variables-in-column-names","dir":"Articles","previous_headings":"Longer","what":"Many variables in column names","title":"Pivoting","text":"challenging situation occurs multiple variables crammed column names. example, take dataset: country, iso2, iso3, year already variables, can left . columns new_sp_m014 newrel_f65 encode four variables names: new_/new prefix indicates counts new cases. dataset contains new cases, ’ll ignore ’s constant. sp/rel/ep describe case diagnosed. m/f gives gender. 014/1524/2535/3544/4554/65 supplies age range. can break variables specifying multiple column names names_to, either providing names_sep names_pattern. names_pattern natural fit. similar interface extract: give regular expression containing groups (defined ()) puts group column. go one step use readr functions convert gender age factors. think good practice categorical variables known set values. way little efficient mutate fact, pivot_longer() transform one occurence name mutate() need transform many repetitions.","code":"who #> # A tibble: 7,240 × 60 #>    country     iso2  iso3   year new_sp_m014 new_sp_m1524 new_sp_m2534 #>    <chr>       <chr> <chr> <dbl>       <dbl>        <dbl>        <dbl> #>  1 Afghanistan AF    AFG    1980          NA           NA           NA #>  2 Afghanistan AF    AFG    1981          NA           NA           NA #>  3 Afghanistan AF    AFG    1982          NA           NA           NA #>  4 Afghanistan AF    AFG    1983          NA           NA           NA #>  5 Afghanistan AF    AFG    1984          NA           NA           NA #>  6 Afghanistan AF    AFG    1985          NA           NA           NA #>  7 Afghanistan AF    AFG    1986          NA           NA           NA #>  8 Afghanistan AF    AFG    1987          NA           NA           NA #>  9 Afghanistan AF    AFG    1988          NA           NA           NA #> 10 Afghanistan AF    AFG    1989          NA           NA           NA #> # ℹ 7,230 more rows #> # ℹ 53 more variables: new_sp_m3544 <dbl>, new_sp_m4554 <dbl>, #> #   new_sp_m5564 <dbl>, new_sp_m65 <dbl>, new_sp_f014 <dbl>, #> #   new_sp_f1524 <dbl>, new_sp_f2534 <dbl>, new_sp_f3544 <dbl>, #> #   new_sp_f4554 <dbl>, new_sp_f5564 <dbl>, new_sp_f65 <dbl>, #> #   new_sn_m014 <dbl>, new_sn_m1524 <dbl>, new_sn_m2534 <dbl>, #> #   new_sn_m3544 <dbl>, new_sn_m4554 <dbl>, new_sn_m5564 <dbl>, … who %>%   pivot_longer(     cols = new_sp_m014:newrel_f65,     names_to = c(\"diagnosis\", \"gender\", \"age\"),     names_pattern = \"new_?(.*)_(.)(.*)\",     values_to = \"count\"   ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows who %>%   pivot_longer(     cols = new_sp_m014:newrel_f65,     names_to = c(\"diagnosis\", \"gender\", \"age\"),     names_pattern = \"new_?(.*)_(.)(.*)\",     names_transform = list(       gender = ~ readr::parse_factor(.x, levels = c(\"f\", \"m\")),       age = ~ readr::parse_factor(         .x,         levels = c(\"014\", \"1524\", \"2534\", \"3544\", \"4554\", \"5564\", \"65\"),         ordered = TRUE       )     ),     values_to = \"count\", )"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"multiple-observations-per-row","dir":"Articles","previous_headings":"Longer","what":"Multiple observations per row","title":"Pivoting","text":"far, working data frames one observation per row, many important pivoting problems involve multiple observations per row. can usually recognise case name column want appear output part column name input. section, ’ll learn pivot sort data. following example adapted data.table vignette, inspiration tidyr’s solution problem. Note two pieces information (values) child: name dob (date birth). need go separate columns result. supply multiple variables names_to, using names_sep split variable name. Note special name .value: tells pivot_longer() part column name specifies “value” measured (become variable output). Note use values_drop_na = TRUE: input shape forces creation explicit missing variables observations don’t exist. similar problem problem also exists anscombe dataset built base R: dataset contains four pairs variables (x1 y1, x2 y2, etc) underlie Anscombe’s quartet, collection four datasets summary statistics (mean, sd, correlation etc), quite different data. want produce dataset columns set, x y. Setting cols_vary \"slowest\" groups values columns x1 y1 together rows output moving x2 y2. argument often produces intuitively ordered output pivoting every column dataset. similar situation can arise panel data. example, take example dataset provided Thomas Leeper. can tidy using approach anscombe:","code":"household #> # A tibble: 5 × 5 #>   family dob_child1 dob_child2 name_child1 name_child2 #>    <int> <date>     <date>     <chr>       <chr>       #> 1      1 1998-11-26 2000-01-29 Susan       Jose        #> 2      2 1996-06-22 NA         Mark        NA          #> 3      3 2002-07-11 2004-04-05 Sam         Seth        #> 4      4 2004-10-10 2009-08-27 Craig       Khai        #> 5      5 2000-12-05 2005-02-28 Parker      Gracie household %>%   pivot_longer(     cols = !family,     names_to = c(\".value\", \"child\"),     names_sep = \"_\",     values_drop_na = TRUE   ) #> # A tibble: 9 × 4 #>   family child  dob        name   #>    <int> <chr>  <date>     <chr>  #> 1      1 child1 1998-11-26 Susan  #> 2      1 child2 2000-01-29 Jose   #> 3      2 child1 1996-06-22 Mark   #> 4      3 child1 2002-07-11 Sam    #> 5      3 child2 2004-04-05 Seth   #> 6      4 child1 2004-10-10 Craig  #> 7      4 child2 2009-08-27 Khai   #> 8      5 child1 2000-12-05 Parker #> 9      5 child2 2005-02-28 Gracie anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89 anscombe %>%   pivot_longer(     cols = everything(),     cols_vary = \"slowest\",     names_to = c(\".value\", \"set\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 44 × 3 #>    set       x     y #>    <chr> <dbl> <dbl> #>  1 1        10  8.04 #>  2 1         8  6.95 #>  3 1        13  7.58 #>  4 1         9  8.81 #>  5 1        11  8.33 #>  6 1        14  9.96 #>  7 1         6  7.24 #>  8 1         4  4.26 #>  9 1        12 10.8  #> 10 1         7  4.82 #> # ℹ 34 more rows pnl <- tibble(   x = 1:4,   a = c(1, 1,0, 0),   b = c(0, 1, 1, 1),   y1 = rnorm(4),   y2 = rnorm(4),   z1 = rep(3, 4),   z2 = rep(-2, 4), )  pnl %>%   pivot_longer(     cols = !c(x, a, b),     names_to = c(\".value\", \"time\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 8 × 6 #>       x     a     b time         y     z #>   <int> <dbl> <dbl> <chr>    <dbl> <dbl> #> 1     1     1     0 1     -1.40        3 #> 2     1     1     0 2      0.622      -2 #> 3     2     1     1 1      0.255       3 #> 4     2     1     1 2      1.15       -2 #> 5     3     0     1 1     -2.44        3 #> 6     3     0     1 2     -1.82       -2 #> 7     4     0     1 1     -0.00557     3 #> 8     4     0     1 2     -0.247      -2"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"wider","dir":"Articles","previous_headings":"","what":"Wider","title":"Pivoting","text":"pivot_wider() opposite pivot_longer(): makes dataset wider increasing number columns decreasing number rows. ’s relatively rare need pivot_wider() make tidy data, ’s often useful creating summary tables presentation, data format needed tools.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"capture-recapture-data","dir":"Articles","previous_headings":"Wider","what":"Capture-recapture data","title":"Pivoting","text":"fish_encounters dataset, contributed Myfanwy Johnston, describes fish swimming river detected automatic monitoring stations: Many tools used analyse data need form station column: dataset records fish detected station - doesn’t record wasn’t detected (common type data). means output data filled NAs. However, case know absence record means fish seen, can ask pivot_wider() fill missing values zeros:","code":"fish_encounters #> # A tibble: 114 × 3 #>    fish  station  seen #>    <fct> <fct>   <int> #>  1 4842  Release     1 #>  2 4842  I80_1       1 #>  3 4842  Lisbon      1 #>  4 4842  Rstr        1 #>  5 4842  Base_TD     1 #>  6 4842  BCE         1 #>  7 4842  BCW         1 #>  8 4842  BCE2        1 #>  9 4842  BCW2        1 #> 10 4842  MAE         1 #> # ℹ 104 more rows fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen   ) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA #>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA #>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA #>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA #>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA #>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> # ℹ 9 more rows #> # ℹ 1 more variable: MAW <int> fish_encounters %>%   pivot_wider(     names_from = station,     values_from = seen,     values_fill = 0   ) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1     0     0     0     0     0 #>  5 4847        1     1      1     0       0     0     0     0     0     0 #>  6 4848        1     1      1     1       0     0     0     0     0     0 #>  7 4849        1     1      0     0       0     0     0     0     0     0 #>  8 4850        1     1      0     1       1     1     1     0     0     0 #>  9 4851        1     1      0     0       0     0     0     0     0     0 #> 10 4854        1     1      0     0       0     0     0     0     0     0 #> # ℹ 9 more rows #> # ℹ 1 more variable: MAW <int>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"aggregation","dir":"Articles","previous_headings":"Wider","what":"Aggregation","title":"Pivoting","text":"can also use pivot_wider() perform simple aggregation. example, take warpbreaks dataset built base R (converted tibble better print method): designed experiment nine replicates every combination wool (B) tension (L, M, H): happens attempt pivot levels wool columns? get warning cell output corresponds multiple cells input. default behaviour produces list-columns, contain individual values. useful output summary statistics, e.g. mean breaks combination wool tension: complex summary operations, recommend summarising reshaping, simple cases ’s often convenient summarise within pivot_wider().","code":"warpbreaks <- warpbreaks %>%   as_tibble() %>%   select(wool, tension, breaks) warpbreaks #> # A tibble: 54 × 3 #>    wool  tension breaks #>    <fct> <fct>    <dbl> #>  1 A     L           26 #>  2 A     L           30 #>  3 A     L           54 #>  4 A     L           25 #>  5 A     L           70 #>  6 A     L           52 #>  7 A     L           51 #>  8 A     L           26 #>  9 A     L           67 #> 10 A     M           18 #> # ℹ 44 more rows warpbreaks %>%   count(wool, tension) #> # A tibble: 6 × 3 #>   wool  tension     n #>   <fct> <fct>   <int> #> 1 A     L           9 #> 2 A     M           9 #> 3 A     H           9 #> 4 B     L           9 #> 5 B     M           9 #> 6 B     H           9 warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks   ) #> Warning: Values from `breaks` are not uniquely identified; output will contain #> list-cols. #> • Use `values_fn = list` to suppress this warning. #> • Use `values_fn = {summary_fun}` to summarise duplicates. #> • Use the following dplyr code to identify duplicates. #>   {data} |> #>   dplyr::summarise(n = dplyr::n(), .by = c(tension, wool)) |> #>   dplyr::filter(n > 1L) #> # A tibble: 3 × 3 #>   tension A         B         #>   <fct>   <list>    <list>    #> 1 L       <dbl [9]> <dbl [9]> #> 2 M       <dbl [9]> <dbl [9]> #> 3 H       <dbl [9]> <dbl [9]> warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = mean   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        44.6  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"generate-column-name-from-multiple-variables","dir":"Articles","previous_headings":"Wider","what":"Generate column name from multiple variables","title":"Pivoting","text":"Imagine, https://stackoverflow.com/questions/24929954, information containing combination product, country, year. tidy form might look like : want widen data one column combination product country. key specify multiple variables names_from: either names_from values_from select multiple variables, can control column names output constructed names_sep names_prefix, workhorse names_glue:","code":"production <-   expand_grid(     product = c(\"A\", \"B\"),     country = c(\"AI\", \"EI\"),     year = 2000:2014   ) %>%   filter((product == \"A\" & country == \"AI\") | product == \"B\") %>%   mutate(production = rnorm(nrow(.))) production #> # A tibble: 45 × 4 #>    product country  year production #>    <chr>   <chr>   <int>      <dbl> #>  1 A       AI       2000    -0.244  #>  2 A       AI       2001    -0.283  #>  3 A       AI       2002    -0.554  #>  4 A       AI       2003     0.629  #>  5 A       AI       2004     2.07   #>  6 A       AI       2005    -1.63   #>  7 A       AI       2006     0.512  #>  8 A       AI       2007    -1.86   #>  9 A       AI       2008    -0.522  #> 10 A       AI       2009    -0.0526 #> # ℹ 35 more rows production %>%   pivot_wider(     names_from = c(product, country),     values_from = production   ) #> # A tibble: 15 × 4 #>     year    A_AI    B_AI    B_EI #>    <int>   <dbl>   <dbl>   <dbl> #>  1  2000 -0.244   0.738  -0.313  #>  2  2001 -0.283   1.89    1.07   #>  3  2002 -0.554  -0.0974  0.0700 #>  4  2003  0.629  -0.936  -0.639  #>  5  2004  2.07   -0.0160 -0.0500 #>  6  2005 -1.63   -0.827  -0.251  #>  7  2006  0.512  -1.51    0.445  #>  8  2007 -1.86    0.935   2.76   #>  9  2008 -0.522   0.176   0.0465 #> 10  2009 -0.0526  0.244   0.578  #> # ℹ 5 more rows production %>%   pivot_wider(     names_from = c(product, country),     values_from = production,     names_sep = \".\",     names_prefix = \"prod.\"   ) #> # A tibble: 15 × 4 #>     year prod.A.AI prod.B.AI prod.B.EI #>    <int>     <dbl>     <dbl>     <dbl> #>  1  2000   -0.244     0.738    -0.313  #>  2  2001   -0.283     1.89      1.07   #>  3  2002   -0.554    -0.0974    0.0700 #>  4  2003    0.629    -0.936    -0.639  #>  5  2004    2.07     -0.0160   -0.0500 #>  6  2005   -1.63     -0.827    -0.251  #>  7  2006    0.512    -1.51      0.445  #>  8  2007   -1.86      0.935     2.76   #>  9  2008   -0.522     0.176     0.0465 #> 10  2009   -0.0526    0.244     0.578  #> # ℹ 5 more rows  production %>%   pivot_wider(     names_from = c(product, country),     values_from = production,     names_glue = \"prod_{product}_{country}\"   ) #> # A tibble: 15 × 4 #>     year prod_A_AI prod_B_AI prod_B_EI #>    <int>     <dbl>     <dbl>     <dbl> #>  1  2000   -0.244     0.738    -0.313  #>  2  2001   -0.283     1.89      1.07   #>  3  2002   -0.554    -0.0974    0.0700 #>  4  2003    0.629    -0.936    -0.639  #>  5  2004    2.07     -0.0160   -0.0500 #>  6  2005   -1.63     -0.827    -0.251  #>  7  2006    0.512    -1.51      0.445  #>  8  2007   -1.86      0.935     2.76   #>  9  2008   -0.522     0.176     0.0465 #> 10  2009   -0.0526    0.244     0.578  #> # ℹ 5 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"tidy-census","dir":"Articles","previous_headings":"Wider","what":"Tidy census","title":"Pivoting","text":"us_rent_income dataset contains information median income rent state US 2017 (American Community Survey, retrieved tidycensus package). estimate moe values columns, can supply values_from: Note name variable automatically appended output columns.","code":"us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"implicit-missing-values","dir":"Articles","previous_headings":"Wider","what":"Implicit missing values","title":"Pivoting","text":"Occasionally, ’ll come across data names variable encoded factor, data represented. pivot_wider() defaults generating columns values actually represented data, might want include column possible level case data changes future. names_expand argument turn implicit factor levels explicit ones, forcing represented result. also sorts column names using level order, produces intuitive results case. multiple names_from columns provided, names_expand generate Cartesian product possible combinations names_from values. Notice following data omitted rows percentage value 0. names_expand allows us make explicit pivot. related problem can occur implicit missing factor levels combinations id_cols. case, missing rows (rather columns) ’d like explicitly represent. example, ’ll modify daily data type column, pivot instead, keeping day id column. type levels represented columns, missing rows related unrepresented day factor levels. can use id_expand way used names_expand, expand (sort) implicit missing rows id_cols.","code":"weekdays <- c(\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\")  daily <- tibble(   day = factor(c(\"Tue\", \"Thu\", \"Fri\", \"Mon\"), levels = weekdays),   value = c(2, 3, 1, 5) )  daily #> # A tibble: 4 × 2 #>   day   value #>   <fct> <dbl> #> 1 Tue       2 #> 2 Thu       3 #> 3 Fri       1 #> 4 Mon       5 daily %>%   pivot_wider(     names_from = day,     values_from = value   ) #> # A tibble: 1 × 4 #>     Tue   Thu   Fri   Mon #>   <dbl> <dbl> <dbl> <dbl> #> 1     2     3     1     5 daily %>%   pivot_wider(     names_from = day,     values_from = value,     names_expand = TRUE   ) #> # A tibble: 1 × 7 #>     Mon   Tue   Wed   Thu   Fri   Sat   Sun #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1     5     2    NA     3     1    NA    NA percentages <- tibble(   year = c(2018, 2019, 2020, 2020),   type = factor(c(\"A\", \"B\", \"A\", \"B\"), levels = c(\"A\", \"B\")),   percentage = c(100, 100, 40, 60) )  percentages #> # A tibble: 4 × 3 #>    year type  percentage #>   <dbl> <fct>      <dbl> #> 1  2018 A            100 #> 2  2019 B            100 #> 3  2020 A             40 #> 4  2020 B             60  percentages %>%   pivot_wider(     names_from = c(year, type),     values_from = percentage,     names_expand = TRUE,     values_fill = 0   ) #> # A tibble: 1 × 6 #>   `2018_A` `2018_B` `2019_A` `2019_B` `2020_A` `2020_B` #>      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> #> 1      100        0        0      100       40       60 daily <- mutate(daily, type = factor(c(\"A\", \"B\", \"B\", \"A\"))) daily #> # A tibble: 4 × 3 #>   day   value type  #>   <fct> <dbl> <fct> #> 1 Tue       2 A     #> 2 Thu       3 B     #> 3 Fri       1 B     #> 4 Mon       5 A daily %>%   pivot_wider(     names_from = type,     values_from = value,     values_fill = 0   ) #> # A tibble: 4 × 3 #>   day       A     B #>   <fct> <dbl> <dbl> #> 1 Tue       2     0 #> 2 Thu       0     3 #> 3 Fri       0     1 #> 4 Mon       5     0 daily %>%   pivot_wider(     names_from = type,     values_from = value,     values_fill = 0,     id_expand = TRUE   ) #> # A tibble: 7 × 3 #>   day       A     B #>   <fct> <dbl> <dbl> #> 1 Mon       5     0 #> 2 Tue       2     0 #> 3 Wed       0     0 #> 4 Thu       0     3 #> 5 Fri       0     1 #> 6 Sat       0     0 #> 7 Sun       0     0"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"unused-columns","dir":"Articles","previous_headings":"Wider","what":"Unused columns","title":"Pivoting","text":"Imagine ’ve found situation columns data completely unrelated pivoting process, ’d still like retain information somehow. example, updates ’d like pivot system column create one row summaries county’s system updates. typical pivot_wider() call, completely lose information date column. example, ’d like retain recent update date across systems particular county. accomplish can use unused_fn argument, allows us summarize values columns utilized pivoting process. can also retain data delay aggregation entirely using list() summary function.","code":"updates <- tibble(   county = c(\"Wake\", \"Wake\", \"Wake\", \"Guilford\", \"Guilford\"),   date = c(as.Date(\"2020-01-01\") + 0:2, as.Date(\"2020-01-03\") + 0:1),   system = c(\"A\", \"B\", \"C\", \"A\", \"C\"),   value = c(3.2, 4, 5.5, 2, 1.2) )  updates #> # A tibble: 5 × 4 #>   county   date       system value #>   <chr>    <date>     <chr>  <dbl> #> 1 Wake     2020-01-01 A        3.2 #> 2 Wake     2020-01-02 B        4   #> 3 Wake     2020-01-03 C        5.5 #> 4 Guilford 2020-01-03 A        2   #> 5 Guilford 2020-01-04 C        1.2 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value   ) #> # A tibble: 2 × 4 #>   county       A     B     C #>   <chr>    <dbl> <dbl> <dbl> #> 1 Wake       3.2     4   5.5 #> 2 Guilford   2      NA   1.2 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value,     unused_fn = list(date = max)   ) #> # A tibble: 2 × 5 #>   county       A     B     C date       #>   <chr>    <dbl> <dbl> <dbl> <date>     #> 1 Wake       3.2     4   5.5 2020-01-03 #> 2 Guilford   2      NA   1.2 2020-01-04 updates %>%   pivot_wider(     id_cols = county,     names_from = system,     values_from = value,     unused_fn = list(date = list)   ) #> # A tibble: 2 × 5 #>   county       A     B     C date       #>   <chr>    <dbl> <dbl> <dbl> <list>     #> 1 Wake       3.2     4   5.5 <date [3]> #> 2 Guilford   2      NA   1.2 <date [2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"contact-list","dir":"Articles","previous_headings":"Wider","what":"Contact list","title":"Pivoting","text":"final challenge inspired Jiena Gu. Imagine contact list ’ve copied pasted website: challenging ’s variable identifies observations belong together. can fix noting every contact starts name, can create unique id counting every time see “name” field: Now unique identifier person, can pivot field value columns:","code":"contacts <- tribble(   ~field, ~value,   \"name\", \"Jiena McLellan\",   \"company\", \"Toyota\",   \"name\", \"John Smith\",   \"company\", \"google\",   \"email\", \"john@google.com\",   \"name\", \"Huxley Ratcliffe\" ) contacts <- contacts %>%   mutate(     person_id = cumsum(field == \"name\")   ) contacts #> # A tibble: 6 × 3 #>   field   value            person_id #>   <chr>   <chr>                <int> #> 1 name    Jiena McLellan           1 #> 2 company Toyota                   1 #> 3 name    John Smith               2 #> 4 company google                   2 #> 5 email   john@google.com          2 #> 6 name    Huxley Ratcliffe         3 contacts %>%   pivot_wider(     names_from = field,     values_from = value   ) #> # A tibble: 3 × 4 #>   person_id name             company email           #>       <int> <chr>            <chr>   <chr>           #> 1         1 Jiena McLellan   Toyota  NA              #> 2         2 John Smith       google  john@google.com #> 3         3 Huxley Ratcliffe NA      NA"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer-then-wider","dir":"Articles","previous_headings":"","what":"Longer, then wider","title":"Pivoting","text":"problems can’t solved pivoting single direction. examples section show might combine pivot_longer() pivot_wider() solve complex problems.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"world-bank","dir":"Articles","previous_headings":"Longer, then wider","what":"World bank","title":"Pivoting","text":"world_bank_pop contains data World Bank population per country 2000 2018. goal produce tidy dataset variable column. ’s obvious exactly steps needed yet, ’ll start obvious problem: year spread across multiple columns. Next need consider indicator variable: SP.POP.GROW population growth, SP.POP.TOTL total population, SP.URB.* urban areas. Let’s split two variables: area (total urban) actual variable (population growth): Now can complete tidying pivoting variable value make TOTL GROW columns:","code":"world_bank_pop #> # A tibble: 1,064 × 20 #>    country indicator        `2000`  `2001`  `2002`  `2003`  `2004`  `2005` #>    <chr>   <chr>             <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #>  1 ABW     SP.URB.TOTL      4.16e4 4.20e+4 4.22e+4 4.23e+4 4.23e+4 4.24e+4 #>  2 ABW     SP.URB.GROW      1.66e0 9.56e-1 4.01e-1 1.97e-1 9.46e-2 1.94e-1 #>  3 ABW     SP.POP.TOTL      8.91e4 9.07e+4 9.18e+4 9.27e+4 9.35e+4 9.45e+4 #>  4 ABW     SP.POP.GROW      2.54e0 1.77e+0 1.19e+0 9.97e-1 9.01e-1 1.00e+0 #>  5 AFE     SP.URB.TOTL      1.16e8 1.20e+8 1.24e+8 1.29e+8 1.34e+8 1.39e+8 #>  6 AFE     SP.URB.GROW      3.60e0 3.66e+0 3.72e+0 3.71e+0 3.74e+0 3.81e+0 #>  7 AFE     SP.POP.TOTL      4.02e8 4.12e+8 4.23e+8 4.34e+8 4.45e+8 4.57e+8 #>  8 AFE     SP.POP.GROW      2.58e0 2.59e+0 2.61e+0 2.62e+0 2.64e+0 2.67e+0 #>  9 AFG     SP.URB.TOTL      4.31e6 4.36e+6 4.67e+6 5.06e+6 5.30e+6 5.54e+6 #> 10 AFG     SP.URB.GROW      1.86e0 1.15e+0 6.86e+0 7.95e+0 4.59e+0 4.47e+0 #> # ℹ 1,054 more rows #> # ℹ 12 more variables: `2006` <dbl>, `2007` <dbl>, `2008` <dbl>, #> #   `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, #> #   `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl> pop2 <- world_bank_pop %>%   pivot_longer(     cols = `2000`:`2017`,     names_to = \"year\",     values_to = \"value\"   ) pop2 #> # A tibble: 19,152 × 4 #>    country indicator   year  value #>    <chr>   <chr>       <chr> <dbl> #>  1 ABW     SP.URB.TOTL 2000  41625 #>  2 ABW     SP.URB.TOTL 2001  42025 #>  3 ABW     SP.URB.TOTL 2002  42194 #>  4 ABW     SP.URB.TOTL 2003  42277 #>  5 ABW     SP.URB.TOTL 2004  42317 #>  6 ABW     SP.URB.TOTL 2005  42399 #>  7 ABW     SP.URB.TOTL 2006  42555 #>  8 ABW     SP.URB.TOTL 2007  42729 #>  9 ABW     SP.URB.TOTL 2008  42906 #> 10 ABW     SP.URB.TOTL 2009  43079 #> # ℹ 19,142 more rows pop2 %>%   count(indicator) #> # A tibble: 4 × 2 #>   indicator       n #>   <chr>       <int> #> 1 SP.POP.GROW  4788 #> 2 SP.POP.TOTL  4788 #> 3 SP.URB.GROW  4788 #> 4 SP.URB.TOTL  4788 pop3 <- pop2 %>%   separate(indicator, c(NA, \"area\", \"variable\")) pop3 #> # A tibble: 19,152 × 5 #>    country area  variable year  value #>    <chr>   <chr> <chr>    <chr> <dbl> #>  1 ABW     URB   TOTL     2000  41625 #>  2 ABW     URB   TOTL     2001  42025 #>  3 ABW     URB   TOTL     2002  42194 #>  4 ABW     URB   TOTL     2003  42277 #>  5 ABW     URB   TOTL     2004  42317 #>  6 ABW     URB   TOTL     2005  42399 #>  7 ABW     URB   TOTL     2006  42555 #>  8 ABW     URB   TOTL     2007  42729 #>  9 ABW     URB   TOTL     2008  42906 #> 10 ABW     URB   TOTL     2009  43079 #> # ℹ 19,142 more rows pop3 %>%   pivot_wider(     names_from = variable,     values_from = value   ) #> # A tibble: 9,576 × 5 #>    country area  year   TOTL   GROW #>    <chr>   <chr> <chr> <dbl>  <dbl> #>  1 ABW     URB   2000  41625 1.66   #>  2 ABW     URB   2001  42025 0.956  #>  3 ABW     URB   2002  42194 0.401  #>  4 ABW     URB   2003  42277 0.197  #>  5 ABW     URB   2004  42317 0.0946 #>  6 ABW     URB   2005  42399 0.194  #>  7 ABW     URB   2006  42555 0.367  #>  8 ABW     URB   2007  42729 0.408  #>  9 ABW     URB   2008  42906 0.413  #> 10 ABW     URB   2009  43079 0.402  #> # ℹ 9,566 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"multi-choice","dir":"Articles","previous_headings":"Longer, then wider","what":"Multi-choice","title":"Pivoting","text":"Based suggestion Maxime Wack, https://github.com/tidyverse/tidyr/issues/384), final example shows deal common way recording multiple choice data. Often get data follows: actual order isn’t important, ’d prefer individual questions columns. can achieve desired transformation two steps. First, make data longer, eliminating explicit NAs, adding column indicate choice chosen: make data wider, filling missing observations FALSE:","code":"multi <- tribble(   ~id, ~choice1, ~choice2, ~choice3,   1, \"A\", \"B\", \"C\",   2, \"C\", \"B\",  NA,   3, \"D\",  NA,  NA,   4, \"B\", \"D\",  NA ) multi2 <- multi %>%   pivot_longer(     cols = !id,     values_drop_na = TRUE   ) %>%   mutate(checked = TRUE) multi2 #> # A tibble: 8 × 4 #>      id name    value checked #>   <dbl> <chr>   <chr> <lgl>   #> 1     1 choice1 A     TRUE    #> 2     1 choice2 B     TRUE    #> 3     1 choice3 C     TRUE    #> 4     2 choice1 C     TRUE    #> 5     2 choice2 B     TRUE    #> 6     3 choice1 D     TRUE    #> 7     4 choice1 B     TRUE    #> 8     4 choice2 D     TRUE multi2 %>%   pivot_wider(     id_cols = id,     names_from = value,     values_from = checked,     values_fill = FALSE   ) #> # A tibble: 4 × 5 #>      id A     B     C     D     #>   <dbl> <lgl> <lgl> <lgl> <lgl> #> 1     1 TRUE  TRUE  TRUE  FALSE #> 2     2 FALSE TRUE  TRUE  FALSE #> 3     3 FALSE FALSE FALSE TRUE  #> 4     4 FALSE TRUE  FALSE TRUE"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"manual-specs","dir":"Articles","previous_headings":"","what":"Manual specs","title":"Pivoting","text":"arguments pivot_longer() pivot_wider() allow pivot wide range datasets. creativity people apply data structures seemingly endless, ’s quite possible encounter dataset can’t immediately see reshape pivot_longer() pivot_wider(). gain control pivoting, can instead create “spec” data frame describes exactly data stored column names becomes variables (vice versa). section introduces spec data structure, show use pivot_longer() pivot_wider() insufficient.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"longer-1","dir":"Articles","previous_headings":"Manual specs","what":"Longer","title":"Pivoting","text":"see works, lets return simplest case pivoting applied relig_income dataset. Now pivoting happens two steps: first create spec object (using build_longer_spec()) use describe pivoting operation: (gives result , just code. ’s need use , presented simple example using spec.) spec look like? ’s data frame one row column wide format version data present long format, two special columns start .: .name gives name column. .value gives name column values cells go . also one column spec column present long format data present wide format data. corresponds names_to argument pivot_longer() build_longer_spec() names_from argument pivot_wider() build_wider_spec(). example, income column character vector names columns pivoted.","code":"spec <- relig_income %>%   build_longer_spec(     cols = !religion,     names_to = \"income\",     values_to = \"count\"   ) pivot_longer_spec(relig_income, spec) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows spec #> # A tibble: 10 × 3 #>    .name              .value income             #>    <chr>              <chr>  <chr>              #>  1 <$10k              count  <$10k              #>  2 $10-20k            count  $10-20k            #>  3 $20-30k            count  $20-30k            #>  4 $30-40k            count  $30-40k            #>  5 $40-50k            count  $40-50k            #>  6 $50-75k            count  $50-75k            #>  7 $75-100k           count  $75-100k           #>  8 $100-150k          count  $100-150k          #>  9 >150k              count  >150k              #> 10 Don't know/refused count  Don't know/refused"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"wider-1","dir":"Articles","previous_headings":"Manual specs","what":"Wider","title":"Pivoting","text":"widen us_rent_income pivot_wider(). result ok, think improved: think better columns income, rent, income_moe, rent_moe, can achieve manual spec. current spec looks like : case, mutate spec carefully construct column names: Supplying spec pivot_wider() gives us result ’re looking :","code":"us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows spec1 <- us_rent_income %>%   build_wider_spec(     names_from = variable,     values_from = c(estimate, moe)   ) spec1 #> # A tibble: 4 × 3 #>   .name           .value   variable #>   <chr>           <chr>    <chr>    #> 1 estimate_income estimate income   #> 2 estimate_rent   estimate rent     #> 3 moe_income      moe      income   #> 4 moe_rent        moe      rent spec2 <- spec1 %>%   mutate(     .name = paste0(variable, ifelse(.value == \"moe\", \"_moe\", \"\"))   ) spec2 #> # A tibble: 4 × 3 #>   .name      .value   variable #>   <chr>      <chr>    <chr>    #> 1 income     estimate income   #> 2 rent       estimate rent     #> 3 income_moe moe      income   #> 4 rent_moe   moe      rent us_rent_income %>%   pivot_wider_spec(spec2) #> # A tibble: 52 × 6 #>    GEOID NAME                 income  rent income_moe rent_moe #>    <chr> <chr>                 <dbl> <dbl>      <dbl>    <dbl> #>  1 01    Alabama               24476   747        136        3 #>  2 02    Alaska                32940  1200        508       13 #>  3 04    Arizona               27517   972        148        4 #>  4 05    Arkansas              23789   709        165        5 #>  5 06    California            29454  1358        109        3 #>  6 08    Colorado              32401  1125        109        5 #>  7 09    Connecticut           35326  1123        195        5 #>  8 10    Delaware              31560  1076        247       10 #>  9 11    District of Columbia  43198  1424        681       17 #> 10 12    Florida               25952  1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"by-hand","dir":"Articles","previous_headings":"Manual specs","what":"By hand","title":"Pivoting","text":"Sometimes ’s possible (convenient) compute spec, instead ’s convenient construct spec “hand”. example, take construction data, lightly modified Table 5 “completions” found https://www.census.gov/construction/nrc/index.html: sort data uncommon government agencies: column names actually belong different variables, summaries number units (1, 2-4, 5+) regions country (NE, NW, midwest, S, W). can easily describe tibble: yields following longer form: Note overlap units region variables; data really naturally described two independent tables.","code":"construction #> # A tibble: 9 × 9 #>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest #>   <dbl> <chr>     <dbl> <lgl>                      <dbl>     <dbl>   <dbl> #> 1  2018 Janua…      859 NA                           348       114     169 #> 2  2018 Febru…      882 NA                           400       138     160 #> 3  2018 March       862 NA                           356       150     154 #> 4  2018 April       797 NA                           447       144     196 #> 5  2018 May         875 NA                           364        90     169 #> 6  2018 June        867 NA                           342        76     170 #> 7  2018 July        829 NA                           360       108     183 #> 8  2018 August      939 NA                           286        90     205 #> 9  2018 Septe…      835 NA                           304       117     175 #> # ℹ 2 more variables: South <dbl>, West <dbl> spec <- tribble(   ~.name,            ~.value, ~units,  ~region,   \"1 unit\",          \"n\",     \"1\",     NA,   \"2 to 4 units\",    \"n\",     \"2-4\",   NA,   \"5 units or more\", \"n\",     \"5+\",    NA,   \"Northeast\",       \"n\",     NA,      \"Northeast\",   \"Midwest\",         \"n\",     NA,      \"Midwest\",   \"South\",           \"n\",     NA,      \"South\",   \"West\",            \"n\",     NA,      \"West\", ) construction %>% pivot_longer_spec(spec) #> # A tibble: 63 × 5 #>     Year Month    units region        n #>    <dbl> <chr>    <chr> <chr>     <dbl> #>  1  2018 January  1     NA          859 #>  2  2018 January  2-4   NA           NA #>  3  2018 January  5+    NA          348 #>  4  2018 January  NA    Northeast   114 #>  5  2018 January  NA    Midwest     169 #>  6  2018 January  NA    South       596 #>  7  2018 January  NA    West        339 #>  8  2018 February 1     NA          882 #>  9  2018 February 2-4   NA           NA #> 10  2018 February 5+    NA          400 #> # ℹ 53 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/pivot.html","id":"theory","dir":"Articles","previous_headings":"Manual specs","what":"Theory","title":"Pivoting","text":"One neat property spec need spec pivot_longer() pivot_wider(). makes clear two operations symmetric: pivoting spec allows us precise exactly pivot_longer(df, spec = spec) changes shape df: nrow(df) * nrow(spec) rows, ncol(df) - nrow(spec) + ncol(spec) - 2 columns.","code":"construction %>%   pivot_longer_spec(spec) %>%   pivot_wider_spec(spec) #> # A tibble: 9 × 9 #>    Year Month  `1 unit` `2 to 4 units` `5 units or more` Northeast Midwest #>   <dbl> <chr>     <dbl>          <dbl>             <dbl>     <dbl>   <dbl> #> 1  2018 Janua…      859             NA               348       114     169 #> 2  2018 Febru…      882             NA               400       138     160 #> 3  2018 March       862             NA               356       150     154 #> 4  2018 April       797             NA               447       144     196 #> 5  2018 May         875             NA               364        90     169 #> 6  2018 June        867             NA               342        76     170 #> 7  2018 July        829             NA               360       108     183 #> 8  2018 August      939             NA               286        90     205 #> 9  2018 Septe…      835             NA               304       117     175 #> # ℹ 2 more variables: South <dbl>, West <dbl>"},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Programming with tidyr","text":"tidyr verbs use tidy evaluation make interactive data exploration fast fluid. Tidy evaluation special type non-standard evaluation used throughout tidyverse. ’s typical tidyr code: Tidy evaluation can use !Species say “columns except Species”, without quote column name (\"Species\") refer enclosing data frame (iris$Species). Two basic forms tidy evaluation used tidyr: Tidy selection: drop_na(), fill(), pivot_longer()/pivot_wider(), nest()/unnest(), separate()/extract(), unite() let select variables based position, name, type (e.g. 1:3, starts_with(\"x\"), .numeric). Literally, can use techniques dplyr::select(). Data masking: expand(), crossing() nesting() let refer use data variables variables environment (.e. write my_variable df$my_variable). focus tidy selection , since ’s common. can learn data masking equivalent vignette dplyr: https://dplyr.tidyverse.org/dev/articles/programming.html. considerations writing tidyr code packages, please see vignette(\"-packages\"). ’ve pointed tidyr’s tidy evaluation interface optimized interactive exploration. flip side adds challenges indirect use, .e. ’re working inside loop function. vignette shows overcome challenges. ’ll first go basics tidy selection data masking, talk use indirectly, show number recipes solve common problems. go , reveal version tidyr ’re using make small dataset use examples.","code":"library(tidyr)  iris %>%   nest(data = !Species) #> # A tibble: 3 × 2 #>   Species    data              #>   <fct>      <list>            #> 1 setosa     <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica  <tibble [50 × 4]> packageVersion(\"tidyr\") #> [1] '1.3.1.9000'  mini_iris <- as_tibble(iris)[c(1, 2, 51, 52, 101, 102), ] mini_iris #> # A tibble: 6 × 5 #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <fct>      #> 1          5.1         3.5          1.4         0.2 setosa     #> 2          4.9         3            1.4         0.2 setosa     #> 3          7           3.2          4.7         1.4 versicolor #> 4          6.4         3.2          4.5         1.5 versicolor #> 5          6.3         3.3          6           2.5 virginica  #> 6          5.8         2.7          5.1         1.9 virginica"},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"tidy-selection","dir":"Articles","previous_headings":"","what":"Tidy selection","title":"Programming with tidyr","text":"Underneath functions use tidy selection tidyselect package. provides miniature domain specific language makes easy select columns name, position, type. example: select(df, 1) selects first column; select(df, last_col()) selects last column. select(df, c(, b, c)) selects columns , b, c. select(df, starts_with(\"\")) selects columns whose name starts “”; select(df, ends_with(\"z\")) selects columns whose name ends “z”. select(df, (.numeric)) selects numeric columns. can see details ?tidyr_tidy_select.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/programming.html","id":"indirection","dir":"Articles","previous_headings":"Tidy selection","what":"Indirection","title":"Programming with tidyr","text":"Tidy selection makes common task easier cost making less common task harder. want use tidy select indirectly column specification stored intermediate variable, ’ll need learn new tools. three main cases comes : tidy-select specification function argument, must embrace argument surrounding doubled braces. character vector variable names, must use all_of() any_of() depending whether want function error variable found. functions allow write loops function takes variable names character vector. complicated cases, might want use tidyselect directly: Learn vignette(\"tidyselect\"). Note many tidyr functions use ... can easily select many variables, e.g. fill(df, x, y, z). now believe disadvantages approach outweigh benefits, interface better fill(df, c(x, y, z)). new functions select columns, please just use single argument ....","code":"nest_egg <- function(df, cols) {   nest(df, egg = {{ cols }}) }  nest_egg(mini_iris, !Species) #> # A tibble: 3 × 2 #>   Species    egg              #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> nest_egg <- function(df, cols) {   nest(df, egg = all_of(cols)) }  vars <- c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\") nest_egg(mini_iris, vars) #> # A tibble: 3 × 2 #>   Species    egg              #>   <fct>      <list>           #> 1 setosa     <tibble [2 × 4]> #> 2 versicolor <tibble [2 × 4]> #> 3 virginica  <tibble [2 × 4]> sel_vars <- function(df, cols) {   tidyselect::eval_select(rlang::enquo(cols), df) } sel_vars(mini_iris, !Species) #> Sepal.Length  Sepal.Width Petal.Length  Petal.Width  #>            1            2            3            4"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Rectangling","text":"Rectangling art craft taking deeply nested list (often sourced wild caught JSON XML) taming tidy data set rows columns. three functions tidyr particularly useful rectangling: unnest_longer() takes element list-column makes new row. unnest_wider() takes element list-column makes new column. hoist() similar unnest_wider() plucks selected components, can reach multiple levels. (Alternative, complex inputs need rectangle nested list according specification, see tibblify package.) large number data rectangling problems can solved combining jsonlite::read_json() functions splash dplyr (largely eliminating prior approaches combined mutate() multiple purrr::map()s). Note jsonlite another important function called fromJSON(). don’t recommend performs automatic simplification (simplifyVector = TRUE). often works well, particularly simple cases, think ’re better rectangling know exactly ’s happening can easily handle complicated nested structures. illustrate techniques, ’ll use repurrrsive package, provides number deeply nested lists originally mostly captured web APIs.","code":"library(tidyr) library(dplyr) library(repurrrsive)"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"github-users","dir":"Articles","previous_headings":"","what":"GitHub users","title":"Rectangling","text":"’ll start gh_users, list contains information six GitHub users. begin, put gh_users list data frame: seems bit counter-intuitive: first step making list simpler make complicated? data frame big advantage: bundles together multiple vectors everything tracked together single object. user named list, element represents column. two ways turn list components columns. unnest_wider() takes every component makes new column: case, many components don’t need can instead use hoist(). hoist() allows us pull selected components using syntax purrr::pluck(): hoist() removes named components user list-column, can think moving components inner list top-level data frame.","code":"users <- tibble(user = gh_users) names(users$user[[1]]) #>  [1] \"login\"               \"id\"                  \"avatar_url\"          #>  [4] \"gravatar_id\"         \"url\"                 \"html_url\"            #>  [7] \"followers_url\"       \"following_url\"       \"gists_url\"           #> [10] \"starred_url\"         \"subscriptions_url\"   \"organizations_url\"   #> [13] \"repos_url\"           \"events_url\"          \"received_events_url\" #> [16] \"type\"                \"site_admin\"          \"name\"                #> [19] \"company\"             \"blog\"                \"location\"            #> [22] \"email\"               \"hireable\"            \"bio\"                 #> [25] \"public_repos\"        \"public_gists\"        \"followers\"           #> [28] \"following\"           \"created_at\"          \"updated_at\" users %>% unnest_wider(user) #> # A tibble: 6 × 30 #>   login             id avatar_url gravatar_id url   html_url followers_url #>   <chr>          <int> <chr>      <chr>       <chr> <chr>    <chr>         #> 1 gaborcsardi   660288 https://a… \"\"          http… https:/… https://api.… #> 2 jennybc       599454 https://a… \"\"          http… https:/… https://api.… #> 3 jtleek       1571674 https://a… \"\"          http… https:/… https://api.… #> 4 juliasilge  12505835 https://a… \"\"          http… https:/… https://api.… #> 5 leeper       3505428 https://a… \"\"          http… https:/… https://api.… #> 6 masalmon     8360597 https://a… \"\"          http… https:/… https://api.… #> # ℹ 23 more variables: following_url <chr>, gists_url <chr>, #> #   starred_url <chr>, subscriptions_url <chr>, organizations_url <chr>, #> #   repos_url <chr>, events_url <chr>, received_events_url <chr>, #> #   type <chr>, site_admin <lgl>, name <chr>, company <chr>, blog <chr>, #> #   location <chr>, email <chr>, hireable <lgl>, bio <chr>, #> #   public_repos <int>, public_gists <int>, followers <int>, #> #   following <int>, created_at <chr>, updated_at <chr> users %>% hoist(user,   followers = \"followers\",   login = \"login\",   url = \"html_url\" ) #> # A tibble: 6 × 4 #>   followers login       url                            user              #>       <int> <chr>       <chr>                          <list>            #> 1       303 gaborcsardi https://github.com/gaborcsardi <named list [27]> #> 2       780 jennybc     https://github.com/jennybc     <named list [27]> #> 3      3958 jtleek      https://github.com/jtleek      <named list [27]> #> 4       115 juliasilge  https://github.com/juliasilge  <named list [27]> #> 5       213 leeper      https://github.com/leeper      <named list [27]> #> 6        34 masalmon    https://github.com/masalmon    <named list [27]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"github-repos","dir":"Articles","previous_headings":"","what":"GitHub repos","title":"Rectangling","text":"start gh_repos similarly, putting tibble: time elements repos list repositories belong user. observations, become new rows, use unnest_longer() rather unnest_wider(): can use unnest_wider() hoist(): Note use c(\"owner\", \"login\"): allows us reach two levels deep inside list. alternative approach pull just owner put element column:","code":"repos <- tibble(repo = gh_repos) repos #> # A tibble: 6 × 1 #>   repo        #>   <list>      #> 1 <list [30]> #> 2 <list [30]> #> 3 <list [30]> #> 4 <list [26]> #> 5 <list [30]> #> 6 <list [30]> repos <- repos %>% unnest_longer(repo) repos #> # A tibble: 176 × 1 #>    repo              #>    <list>            #>  1 <named list [68]> #>  2 <named list [68]> #>  3 <named list [68]> #>  4 <named list [68]> #>  5 <named list [68]> #>  6 <named list [68]> #>  7 <named list [68]> #>  8 <named list [68]> #>  9 <named list [68]> #> 10 <named list [68]> #> # ℹ 166 more rows repos %>% hoist(repo,   login = c(\"owner\", \"login\"),   name = \"name\",   homepage = \"homepage\",   watchers = \"watchers_count\" ) #> # A tibble: 176 × 5 #>    login       name        homepage watchers repo              #>    <chr>       <chr>       <chr>       <int> <list>            #>  1 gaborcsardi after       NA              5 <named list [65]> #>  2 gaborcsardi argufy      NA             19 <named list [65]> #>  3 gaborcsardi ask         NA              5 <named list [65]> #>  4 gaborcsardi baseimports NA              0 <named list [65]> #>  5 gaborcsardi citest      NA              0 <named list [65]> #>  6 gaborcsardi clisymbols  \"\"             18 <named list [65]> #>  7 gaborcsardi cmaker      NA              0 <named list [65]> #>  8 gaborcsardi cmark       NA              0 <named list [65]> #>  9 gaborcsardi conditions  NA              0 <named list [65]> #> 10 gaborcsardi crayon      NA             52 <named list [65]> #> # ℹ 166 more rows repos %>%   hoist(repo, owner = \"owner\") %>%   unnest_wider(owner) #> # A tibble: 176 × 18 #>    login           id avatar_url  gravatar_id url   html_url followers_url #>    <chr>        <int> <chr>       <chr>       <chr> <chr>    <chr>         #>  1 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  2 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  3 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  4 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  5 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  6 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  7 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  8 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #>  9 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #> 10 gaborcsardi 660288 https://av… \"\"          http… https:/… https://api.… #> # ℹ 166 more rows #> # ℹ 11 more variables: following_url <chr>, gists_url <chr>, #> #   starred_url <chr>, subscriptions_url <chr>, organizations_url <chr>, #> #   repos_url <chr>, events_url <chr>, received_events_url <chr>, #> #   type <chr>, site_admin <lgl>, repo <list>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"game-of-thrones-characters","dir":"Articles","previous_headings":"","what":"Game of Thrones characters","title":"Rectangling","text":"got_chars similar structure gh_users: ’s list named lists, element inner list describes attribute GoT character. start way, first creating data frame unnesting component column: complex gh_users component char list, giving us collection list-columns: next depend purposes analysis. Maybe want row every book TV series character appears : maybe want build table lets match title name: (Note empty titles (\"\") due infelicity input got_chars: ideally people without titles title vector length 0, title vector length 1 containing empty string.)","code":"chars <- tibble(char = got_chars) chars #> # A tibble: 30 × 1 #>    char              #>    <list>            #>  1 <named list [18]> #>  2 <named list [18]> #>  3 <named list [18]> #>  4 <named list [18]> #>  5 <named list [18]> #>  6 <named list [18]> #>  7 <named list [18]> #>  8 <named list [18]> #>  9 <named list [18]> #> 10 <named list [18]> #> # ℹ 20 more rows  chars2 <- chars %>% unnest_wider(char) chars2 #> # A tibble: 30 × 18 #>    url            id name  gender culture born  died  alive titles aliases #>    <chr>       <int> <chr> <chr>  <chr>   <chr> <chr> <lgl> <list> <list>  #>  1 https://ww…  1022 Theo… Male   \"Ironb… \"In … \"\"    TRUE  <chr>  <chr>   #>  2 https://ww…  1052 Tyri… Male   \"\"      \"In … \"\"    TRUE  <chr>  <chr>   #>  3 https://ww…  1074 Vict… Male   \"Ironb… \"In … \"\"    TRUE  <chr>  <chr>   #>  4 https://ww…  1109 Will  Male   \"\"      \"\"    \"In … FALSE <chr>  <chr>   #>  5 https://ww…  1166 Areo… Male   \"Norvo… \"In … \"\"    TRUE  <chr>  <chr>   #>  6 https://ww…  1267 Chett Male   \"\"      \"At … \"In … FALSE <chr>  <chr>   #>  7 https://ww…  1295 Cres… Male   \"\"      \"In … \"In … FALSE <chr>  <chr>   #>  8 https://ww…   130 Aria… Female \"Dorni… \"In … \"\"    TRUE  <chr>  <chr>   #>  9 https://ww…  1303 Daen… Female \"Valyr… \"In … \"\"    TRUE  <chr>  <chr>   #> 10 https://ww…  1319 Davo… Male   \"Weste… \"In … \"\"    TRUE  <chr>  <chr>   #> # ℹ 20 more rows #> # ℹ 8 more variables: father <chr>, mother <chr>, spouse <chr>, #> #   allegiances <list>, books <list>, povBooks <list>, tvSeries <list>, #> #   playedBy <list> chars2 %>% select_if(is.list) #> # A tibble: 30 × 7 #>    titles    aliases    allegiances books     povBooks  tvSeries  playedBy #>    <list>    <list>     <list>      <list>    <list>    <list>    <list>   #>  1 <chr [2]> <chr [4]>  <chr [1]>   <chr [3]> <chr [2]> <chr [6]> <chr>    #>  2 <chr [2]> <chr [11]> <chr [1]>   <chr [2]> <chr [4]> <chr [6]> <chr>    #>  3 <chr [2]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [1]> <chr>    #>  4 <chr [1]> <chr [1]>  <NULL>      <chr [1]> <chr [1]> <chr [1]> <chr>    #>  5 <chr [1]> <chr [1]>  <chr [1]>   <chr [3]> <chr [2]> <chr [2]> <chr>    #>  6 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr>    #>  7 <chr [1]> <chr [1]>  <NULL>      <chr [2]> <chr [1]> <chr [1]> <chr>    #>  8 <chr [1]> <chr [1]>  <chr [1]>   <chr [4]> <chr [1]> <chr [1]> <chr>    #>  9 <chr [5]> <chr [11]> <chr [1]>   <chr [1]> <chr [4]> <chr [6]> <chr>    #> 10 <chr [4]> <chr [5]>  <chr [2]>   <chr [1]> <chr [3]> <chr [5]> <chr>    #> # ℹ 20 more rows chars2 %>%   select(name, books, tvSeries) %>%   pivot_longer(c(books, tvSeries), names_to = \"media\", values_to = \"value\") %>%   unnest_longer(value) #> # A tibble: 179 × 3 #>    name             media    value             #>    <chr>            <chr>    <chr>             #>  1 Theon Greyjoy    books    A Game of Thrones #>  2 Theon Greyjoy    books    A Storm of Swords #>  3 Theon Greyjoy    books    A Feast for Crows #>  4 Theon Greyjoy    tvSeries Season 1          #>  5 Theon Greyjoy    tvSeries Season 2          #>  6 Theon Greyjoy    tvSeries Season 3          #>  7 Theon Greyjoy    tvSeries Season 4          #>  8 Theon Greyjoy    tvSeries Season 5          #>  9 Theon Greyjoy    tvSeries Season 6          #> 10 Tyrion Lannister books    A Feast for Crows #> # ℹ 169 more rows chars2 %>%   select(name, title = titles) %>%   unnest_longer(title) #> # A tibble: 59 × 2 #>    name              title                                                 #>    <chr>             <chr>                                                 #>  1 Theon Greyjoy     \"Prince of Winterfell\"                                #>  2 Theon Greyjoy     \"Lord of the Iron Islands (by law of the green lands… #>  3 Tyrion Lannister  \"Acting Hand of the King (former)\"                    #>  4 Tyrion Lannister  \"Master of Coin (former)\"                             #>  5 Victarion Greyjoy \"Lord Captain of the Iron Fleet\"                      #>  6 Victarion Greyjoy \"Master of the Iron Victory\"                          #>  7 Will              \"\"                                                    #>  8 Areo Hotah        \"Captain of the Guard at Sunspear\"                    #>  9 Chett             \"\"                                                    #> 10 Cressen           \"Maester\"                                             #> # ℹ 49 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"geocoding-with-google","dir":"Articles","previous_headings":"","what":"Geocoding with google","title":"Rectangling","text":"Next ’ll tackle complex form data comes Google’s geocoding service, stored repurssive package json list-column named lists, makes sense start unnest_wider(): Notice results list lists. cities 1 element (representing unique match geocoding API), Washington Arlington two. can pull separate rows unnest_longer(): Now components, revealed unnest_wider(): can find latitude longitude unnesting geometry: location: also just look first address city: use hoist() dive deeply get directly lat lng:","code":"repurrrsive::gmaps_cities #> # A tibble: 5 × 2 #>   city       json             #>   <chr>      <list>           #> 1 Houston    <named list [2]> #> 2 Washington <named list [2]> #> 3 New York   <named list [2]> #> 4 Chicago    <named list [2]> #> 5 Arlington  <named list [2]> repurrrsive::gmaps_cities %>%   unnest_wider(json) #> # A tibble: 5 × 3 #>   city       results    status #>   <chr>      <list>     <chr>  #> 1 Houston    <list [1]> OK     #> 2 Washington <list [2]> OK     #> 3 New York   <list [1]> OK     #> 4 Chicago    <list [1]> OK     #> 5 Arlington  <list [2]> OK repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) #> # A tibble: 7 × 3 #>   city       results          status #>   <chr>      <list>           <chr>  #> 1 Houston    <named list [5]> OK     #> 2 Washington <named list [5]> OK     #> 3 Washington <named list [5]> OK     #> 4 New York   <named list [5]> OK     #> 5 Chicago    <named list [5]> OK     #> 6 Arlington  <named list [5]> OK     #> 7 Arlington  <named list [5]> OK repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) #> # A tibble: 7 × 7 #>   city   address_components formatted_address geometry     place_id types  #>   <chr>  <list>             <chr>             <list>       <chr>    <list> #> 1 Houst… <list [4]>         Houston, TX, USA  <named list> ChIJAYW… <list> #> 2 Washi… <list [2]>         Washington, USA   <named list> ChIJ-bD… <list> #> 3 Washi… <list [4]>         Washington, DC, … <named list> ChIJW-T… <list> #> 4 New Y… <list [3]>         New York, NY, USA <named list> ChIJOwg… <list> #> 5 Chica… <list [4]>         Chicago, IL, USA  <named list> ChIJ7cv… <list> #> 6 Arlin… <list [4]>         Arlington, TX, U… <named list> ChIJ05g… <list> #> 7 Arlin… <list [4]>         Arlington, VA, U… <named list> ChIJD6e… <list> #> # ℹ 1 more variable: status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) %>%   unnest_wider(geometry) #> # A tibble: 7 × 10 #>   city      address_components formatted_address bounds       location     #>   <chr>     <list>             <chr>             <list>       <list>       #> 1 Houston   <list [4]>         Houston, TX, USA  <named list> <named list> #> 2 Washingt… <list [2]>         Washington, USA   <named list> <named list> #> 3 Washingt… <list [4]>         Washington, DC, … <named list> <named list> #> 4 New York  <list [3]>         New York, NY, USA <named list> <named list> #> 5 Chicago   <list [4]>         Chicago, IL, USA  <named list> <named list> #> 6 Arlington <list [4]>         Arlington, TX, U… <named list> <named list> #> 7 Arlington <list [4]>         Arlington, VA, U… <named list> <named list> #> # ℹ 5 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   unnest_longer(results) %>%   unnest_wider(results) %>%   unnest_wider(geometry) %>%   unnest_wider(location) #> # A tibble: 7 × 11 #>   city      address_components formatted_address bounds         lat    lng #>   <chr>     <list>             <chr>             <list>       <dbl>  <dbl> #> 1 Houston   <list [4]>         Houston, TX, USA  <named list>  29.8  -95.4 #> 2 Washingt… <list [2]>         Washington, USA   <named list>  47.8 -121.  #> 3 Washingt… <list [4]>         Washington, DC, … <named list>  38.9  -77.0 #> 4 New York  <list [3]>         New York, NY, USA <named list>  40.7  -74.0 #> 5 Chicago   <list [4]>         Chicago, IL, USA  <named list>  41.9  -87.6 #> 6 Arlington <list [4]>         Arlington, TX, U… <named list>  32.7  -97.1 #> 7 Arlington <list [4]>         Arlington, VA, U… <named list>  38.9  -77.1 #> # ℹ 5 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, status <chr> repurrrsive::gmaps_cities %>%   unnest_wider(json) %>%   hoist(results, first_result = 1) %>%   unnest_wider(first_result) %>%   unnest_wider(geometry) %>%   unnest_wider(location) #> # A tibble: 5 × 12 #>   city      address_components formatted_address bounds         lat    lng #>   <chr>     <list>             <chr>             <list>       <dbl>  <dbl> #> 1 Houston   <list [4]>         Houston, TX, USA  <named list>  29.8  -95.4 #> 2 Washingt… <list [2]>         Washington, USA   <named list>  47.8 -121.  #> 3 New York  <list [3]>         New York, NY, USA <named list>  40.7  -74.0 #> 4 Chicago   <list [4]>         Chicago, IL, USA  <named list>  41.9  -87.6 #> 5 Arlington <list [4]>         Arlington, TX, U… <named list>  32.7  -97.1 #> # ℹ 6 more variables: location_type <chr>, viewport <list>, #> #   place_id <chr>, types <list>, results <list>, status <chr> repurrrsive::gmaps_cities %>%   hoist(json,     lat = list(\"results\", 1, \"geometry\", \"location\", \"lat\"),     lng = list(\"results\", 1, \"geometry\", \"location\", \"lng\")   ) #> # A tibble: 5 × 4 #>   city         lat    lng json             #>   <chr>      <dbl>  <dbl> <list>           #> 1 Houston     29.8  -95.4 <named list [2]> #> 2 Washington  47.8 -121.  <named list [2]> #> 3 New York    40.7  -74.0 <named list [2]> #> 4 Chicago     41.9  -87.6 <named list [2]> #> 5 Arlington   32.7  -97.1 <named list [2]>"},{"path":"https://tidyr.tidyverse.org/dev/articles/rectangle.html","id":"sharla-gelfands-discography","dir":"Articles","previous_headings":"","what":"Sharla Gelfand’s discography","title":"Rectangling","text":"’ll finish complex list, Sharla Gelfand’s discography. ’ll start usual way: putting list single column data frame, widening component column. also parse date_added column real date-time1. level, see information disc added Sharla’s discography, information disc . need widen basic_information column: Unfortunately fails ’s id column inside basic_information. can quickly see ’s going setting names_repair = \"unique\": problem basic_information repeats id column ’s also stored top-level, can just drop : Alternatively, use hoist(): quickly extract name first label artist indexing deeply nested list. systematic approach create separate tables artist label: join back original dataset needed.","code":"discs <- tibble(disc = discog) %>%   unnest_wider(disc) %>%   mutate(date_added = as.POSIXct(strptime(date_added, \"%Y-%m-%dT%H:%M:%S\"))) discs #> # A tibble: 155 × 5 #>    instance_id date_added          basic_information       id rating #>          <int> <dttm>              <list>               <int>  <int> #>  1   354823933 2019-02-16 17:48:59 <named list [11]>  7496378      0 #>  2   354092601 2019-02-13 14:13:11 <named list [11]>  4490852      0 #>  3   354091476 2019-02-13 14:07:23 <named list [11]>  9827276      0 #>  4   351244906 2019-02-02 11:39:58 <named list [11]>  9769203      0 #>  5   351244801 2019-02-02 11:39:37 <named list [11]>  7237138      0 #>  6   351052065 2019-02-01 20:40:53 <named list [11]> 13117042      0 #>  7   350315345 2019-01-29 15:48:37 <named list [11]>  7113575      0 #>  8   350315103 2019-01-29 15:47:22 <named list [11]> 10540713      0 #>  9   350314507 2019-01-29 15:44:08 <named list [11]> 11260950      0 #> 10   350314047 2019-01-29 15:41:35 <named list [11]> 11726853      0 #> # ℹ 145 more rows discs %>% unnest_wider(basic_information) #> Error in `unnest_wider()`: #> ! Can't duplicate names between the affected columns and the #>   original data. #> ✖ These names are duplicated: #>   ℹ `id`, from `basic_information`. #> ℹ Use `names_sep` to disambiguate using the column name. #> ℹ Or use `names_repair` to specify a repair strategy. discs %>% unnest_wider(basic_information, names_repair = \"unique\") #> New names: #> • `id` -> `id...7` #> • `id` -> `id...14` #> # A tibble: 155 × 15 #>    instance_id date_added          labels  year master_url  artists id...7 #>          <int> <dttm>              <list> <int> <chr>       <list>   <int> #>  1   354823933 2019-02-16 17:48:59 <list>  2015 NA          <list>  7.50e6 #>  2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 #>  3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 #>  4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 #>  5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 #>  6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 #>  7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 #>  8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 #>  9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 #> 10   350314047 2019-01-29 15:41:35 <list>  2017 NA          <list>  1.17e7 #> # ℹ 145 more rows #> # ℹ 8 more variables: thumb <chr>, title <chr>, formats <list>, #> #   cover_image <chr>, resource_url <chr>, master_id <int>, #> #   id...14 <int>, rating <int> discs %>%   select(!id) %>%   unnest_wider(basic_information) #> # A tibble: 155 × 14 #>    instance_id date_added          labels  year master_url  artists     id #>          <int> <dttm>              <list> <int> <chr>       <list>   <int> #>  1   354823933 2019-02-16 17:48:59 <list>  2015 NA          <list>  7.50e6 #>  2   354092601 2019-02-13 14:13:11 <list>  2013 https://ap… <list>  4.49e6 #>  3   354091476 2019-02-13 14:07:23 <list>  2017 https://ap… <list>  9.83e6 #>  4   351244906 2019-02-02 11:39:58 <list>  2017 https://ap… <list>  9.77e6 #>  5   351244801 2019-02-02 11:39:37 <list>  2015 https://ap… <list>  7.24e6 #>  6   351052065 2019-02-01 20:40:53 <list>  2019 https://ap… <list>  1.31e7 #>  7   350315345 2019-01-29 15:48:37 <list>  2014 https://ap… <list>  7.11e6 #>  8   350315103 2019-01-29 15:47:22 <list>  2015 https://ap… <list>  1.05e7 #>  9   350314507 2019-01-29 15:44:08 <list>  2017 https://ap… <list>  1.13e7 #> 10   350314047 2019-01-29 15:41:35 <list>  2017 NA          <list>  1.17e7 #> # ℹ 145 more rows #> # ℹ 7 more variables: thumb <chr>, title <chr>, formats <list>, #> #   cover_image <chr>, resource_url <chr>, master_id <int>, rating <int> discs %>%   hoist(basic_information,     title = \"title\",     year = \"year\",     label = list(\"labels\", 1, \"name\"),     artist = list(\"artists\", 1, \"name\")   ) #> # A tibble: 155 × 9 #>    instance_id date_added          title                 year label artist #>          <int> <dttm>              <chr>                <int> <chr> <chr>  #>  1   354823933 2019-02-16 17:48:59 Demo                  2015 Tobi… Mollot #>  2   354092601 2019-02-13 14:13:11 Observant Com El Mo…  2013 La V… Una B… #>  3   354091476 2019-02-13 14:07:23 I                     2017 La V… S.H.I… #>  4   351244906 2019-02-02 11:39:58 Oído Absoluto         2017 La V… Rata … #>  5   351244801 2019-02-02 11:39:37 A Cat's Cause, No D…  2015 Kato… Ivy (… #>  6   351052065 2019-02-01 20:40:53 Tashme                2019 High… Tashme #>  7   350315345 2019-01-29 15:48:37 Demo                  2014 Mind… Desgr… #>  8   350315103 2019-01-29 15:47:22 Let The Miracles Be…  2015 Not … Phant… #>  9   350314507 2019-01-29 15:44:08 Sub Space             2017 Not … Sub S… #> 10   350314047 2019-01-29 15:41:35 Demo                  2017 Pres… Small… #> # ℹ 145 more rows #> # ℹ 3 more variables: basic_information <list>, id <int>, rating <int> discs %>%   hoist(basic_information, artist = \"artists\") %>%   select(disc_id = id, artist) %>%   unnest_longer(artist) %>%   unnest_wider(artist) #> # A tibble: 167 × 8 #>     disc_id join  name              anv   tracks role  resource_url     id #>       <int> <chr> <chr>             <chr> <chr>  <chr> <chr>         <int> #>  1  7496378 \"\"    Mollot            \"\"    \"\"     \"\"    https://api… 4.62e6 #>  2  4490852 \"\"    Una Bèstia Incon… \"\"    \"\"     \"\"    https://api… 3.19e6 #>  3  9827276 \"\"    S.H.I.T. (3)      \"\"    \"\"     \"\"    https://api… 2.77e6 #>  4  9769203 \"\"    Rata Negra        \"\"    \"\"     \"\"    https://api… 4.28e6 #>  5  7237138 \"\"    Ivy (18)          \"\"    \"\"     \"\"    https://api… 3.60e6 #>  6 13117042 \"\"    Tashme            \"\"    \"\"     \"\"    https://api… 5.21e6 #>  7  7113575 \"\"    Desgraciados      \"\"    \"\"     \"\"    https://api… 4.45e6 #>  8 10540713 \"\"    Phantom Head      \"\"    \"\"     \"\"    https://api… 4.27e6 #>  9 11260950 \"\"    Sub Space (2)     \"\"    \"\"     \"\"    https://api… 5.69e6 #> 10 11726853 \"\"    Small Man (2)     \"\"    \"\"     \"\"    https://api… 6.37e6 #> # ℹ 157 more rows  discs %>%   hoist(basic_information, format = \"formats\") %>%   select(disc_id = id, format) %>%   unnest_longer(format) %>%   unnest_wider(format) %>%   unnest_longer(descriptions) #> # A tibble: 258 × 5 #>     disc_id descriptions text  name     qty   #>       <int> <chr>        <chr> <chr>    <chr> #>  1  7496378 \"Numbered\"   Black Cassette 1     #>  2  4490852 \"LP\"         NA    Vinyl    1     #>  3  9827276 \"7\\\"\"        NA    Vinyl    1     #>  4  9827276 \"45 RPM\"     NA    Vinyl    1     #>  5  9827276 \"EP\"         NA    Vinyl    1     #>  6  9769203 \"LP\"         NA    Vinyl    1     #>  7  9769203 \"Album\"      NA    Vinyl    1     #>  8  7237138 \"7\\\"\"        NA    Vinyl    1     #>  9  7237138 \"45 RPM\"     NA    Vinyl    1     #> 10 13117042 \"7\\\"\"        NA    Vinyl    1     #> # ℹ 248 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-tidying","dir":"Articles","previous_headings":"","what":"Data tidying","title":"Tidy data","text":"often said 80% data analysis spent cleaning preparing data. ’s just first step, must repeated many times course analysis new problems come light new data collected. get handle problem, paper focuses small, important, aspect data cleaning call data tidying: structuring datasets facilitate analysis. principles tidy data provide standard way organise data values within dataset. standard makes initial data cleaning easier don’t need start scratch reinvent wheel every time. tidy data standard designed facilitate initial exploration analysis data, simplify development data analysis tools work well together. Current tools often require translation. spend time munging output one tool can input another. Tidy datasets tidy tools work hand hand make data analysis easier, allowing focus interesting domain problem, uninteresting logistics data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"defining","dir":"Articles","previous_headings":"","what":"Defining tidy data","title":"Tidy data","text":"Happy families alike; every unhappy family unhappy way — Leo Tolstoy Like families, tidy datasets alike every messy dataset messy way. Tidy datasets provide standardized way link structure dataset (physical layout) semantics (meaning). section, ’ll provide standard vocabulary describing structure semantics dataset, use definitions define tidy data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-structure","dir":"Articles","previous_headings":"Defining tidy data","what":"Data structure","title":"Tidy data","text":"statistical datasets data frames made rows columns. columns almost always labeled rows sometimes labeled. following code provides data imaginary classroom format commonly seen wild. table three columns four rows, rows columns labeled. many ways structure underlying data. following table shows data , rows columns transposed. data , layout different. vocabulary rows columns simply rich enough describe two tables represent data. addition appearance, need way describe underlying semantics, meaning, values displayed table.","code":"library(tibble) classroom <- tribble(   ~name,    ~quiz1, ~quiz2, ~test1,   \"Billy\",  NA,     \"D\",    \"C\",   \"Suzy\",   \"F\",    NA,     NA,   \"Lionel\", \"B\",    \"C\",    \"B\",   \"Jenny\",  \"A\",    \"A\",    \"B\"   ) classroom #> # A tibble: 4 × 4 #>   name   quiz1 quiz2 test1 #>   <chr>  <chr> <chr> <chr> #> 1 Billy  NA    D     C     #> 2 Suzy   F     NA    NA    #> 3 Lionel B     C     B     #> 4 Jenny  A     A     B tribble(   ~assessment, ~Billy, ~Suzy, ~Lionel, ~Jenny,   \"quiz1\",     NA,     \"F\",   \"B\",     \"A\",   \"quiz2\",     \"D\",    NA,    \"C\",     \"A\",   \"test1\",     \"C\",    NA,    \"B\",     \"B\"   ) #> # A tibble: 3 × 5 #>   assessment Billy Suzy  Lionel Jenny #>   <chr>      <chr> <chr> <chr>  <chr> #> 1 quiz1      NA    F     B      A     #> 2 quiz2      D     NA    C      A     #> 3 test1      C     NA    B      B"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"data-semantics","dir":"Articles","previous_headings":"Defining tidy data","what":"Data semantics","title":"Tidy data","text":"dataset collection values, usually either numbers (quantitative) strings (qualitative). Values organised two ways. Every value belongs variable observation. variable contains values measure underlying attribute (like height, temperature, duration) across units. observation contains values measured unit (like person, day, race) across attributes. tidy version classroom data looks like : (’ll learn functions work little later) makes values, variables, observations clear. dataset contains 36 values representing three variables 12 observations. variables : name, four possible values (Billy, Suzy, Lionel, Jenny). assessment, three possible values (quiz1, quiz2, test1). grade, five six values depending think missing value (, B, C, D, F, NA). tidy data frame explicitly tells us definition observation. classroom, every combination name assessment single measured observation. dataset also informs us missing values, can meaning. Billy absent first quiz, tried salvage grade. Suzy failed first quiz, decided drop class. calculate Billy’s final grade, might replace missing value F (might get second chance take quiz). However, want know class average Test 1, dropping Suzy’s structural missing value appropriate imputing new value. given dataset, ’s usually easy figure observations variables, surprisingly difficult precisely define variables observations general. example, columns classroom data height weight happy call variables. columns height width, less clear cut, might think height width values dimension variable. columns home phone work phone, treat two variables, fraud detection environment might want variables phone number number type use one phone number multiple people might suggest fraud. general rule thumb easier describe functional relationships variables (e.g., z linear combination x y, density ratio weight volume) rows, easier make comparisons groups observations (e.g., average group vs. average group b) groups columns. given analysis, may multiple levels observation. example, trial new allergy medication might three observational types: demographic data collected person (age, sex, race), medical data collected person day (number sneezes, redness eyes), meteorological data collected day (temperature, pollen count). Variables may change course analysis. Often variables raw data fine grained, may add extra modelling complexity little explanatory gain. example, many surveys ask variations question better get underlying trait. early stages analysis, variables correspond questions. later stages, change focus traits, computed averaging together multiple questions. considerably simplifies analysis don’t need hierarchical model, can often pretend data continuous, discrete.","code":"library(tidyr) library(dplyr) classroom2 <- classroom %>%   pivot_longer(quiz1:test1, names_to = \"assessment\", values_to = \"grade\") %>%   arrange(name, assessment) classroom2 #> # A tibble: 12 × 3 #>    name   assessment grade #>    <chr>  <chr>      <chr> #>  1 Billy  quiz1      NA    #>  2 Billy  quiz2      D     #>  3 Billy  test1      C     #>  4 Jenny  quiz1      A     #>  5 Jenny  quiz2      A     #>  6 Jenny  test1      B     #>  7 Lionel quiz1      B     #>  8 Lionel quiz2      C     #>  9 Lionel test1      B     #> 10 Suzy   quiz1      F     #> # ℹ 2 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"tidy-data","dir":"Articles","previous_headings":"Defining tidy data","what":"Tidy data","title":"Tidy data","text":"Tidy data standard way mapping meaning dataset structure. dataset messy tidy depending rows, columns tables matched observations, variables types. tidy data: variable column; column variable. observation row; row observation. value cell; cell single value. Codd’s 3rd normal form, constraints framed statistical language, focus put single dataset rather many connected datasets common relational databases. Messy data arrangement data. Tidy data makes easy analyst computer extract needed variables provides standard way structuring dataset. Compare different versions classroom data: messy version need use different strategies extract different variables. slows analysis invites errors. consider many data analysis operations involve values variable (every aggregation function), can see important extract values simple, standard way. Tidy data particularly well suited vectorised programming languages like R, layout ensures values different variables observation always paired. order variables observations affect analysis, good ordering makes easier scan raw values. One way organising variables role analysis: values fixed design data collection, measured course experiment? Fixed variables describe experimental design known advance. Computer scientists often call fixed variables dimensions, statisticians usually denote subscripts random variables. Measured variables actually measure study. Fixed variables come first, followed measured variables, ordered related variables contiguous. Rows can ordered first variable, breaking ties second subsequent (fixed) variables. convention adopted tabular displays paper.","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"tidying","dir":"Articles","previous_headings":"","what":"Tidying messy datasets","title":"Tidy data","text":"Real datasets can, often , violate three precepts tidy data almost every way imaginable. occasionally get dataset can start analysing immediately, exception, rule. section describes five common problems messy datasets, along remedies: Column headers values, variable names. Multiple variables stored one column. Variables stored rows columns. Multiple types observational units stored table. single observational unit stored multiple tables. Surprisingly, messy datasets, including types messiness explicitly described , can tidied small set tools: pivoting (longer wider) separating. following sections illustrate problem real dataset encountered, show tidy .","code":""},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"column-headers-are-values-not-variable-names","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Column headers are values, not variable names","title":"Tidy data","text":"common type messy dataset tabular data designed presentation, variables form rows columns, column headers values, variable names. call arrangement messy, cases can extremely useful. provides efficient storage completely crossed designs, can lead extremely efficient computation desired operations can expressed matrix operations. following code shows subset typical dataset form. dataset explores relationship income religion US. comes report produced Pew Research Center, American think-tank collects data attitudes topics ranging religion internet, produces many reports contain datasets format. dataset three variables, religion, income frequency. tidy , need pivot non-variable columns two-column key-value pair. action often described making wide dataset longer (taller). pivoting variables, need provide name new key-value columns create. defining columns pivot (every column except religion), need name key column, name variable defined values column headings. case, ’s income. second argument name value column, frequency. form tidy column represents variable row represents observation, case demographic unit corresponding combination religion income. format also used record regularly spaced observations time. example, Billboard dataset shown records date song first entered billboard top 100. variables artist, track, date.entered, rank week. rank week enters top 100 recorded 75 columns, wk1 wk75. form storage tidy, useful data entry. reduces duplication since otherwise song week need row, song metadata like title artist need repeated. discussed depth multiple types. tidy dataset, first use pivot_longer() make dataset longer. transform columns wk1 wk76, making new column names, week, new value values, rank: use values_drop_na = TRUE drop missing values rank column. data, missing values represent weeks song wasn’t charts, can safely dropped. case ’s also nice little cleaning, converting week variable number, figuring date corresponding week charts: Finally, ’s always good idea sort data. artist, track week: date rank:","code":"relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> # ℹ 8 more rows #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(-religion, names_to = \"income\", values_to = \"frequency\") #> # A tibble: 180 × 3 #>    religion income             frequency #>    <chr>    <chr>                  <dbl> #>  1 Agnostic <$10k                     27 #>  2 Agnostic $10-20k                   34 #>  3 Agnostic $20-30k                   60 #>  4 Agnostic $30-40k                   81 #>  5 Agnostic $40-50k                   76 #>  6 Agnostic $50-75k                  137 #>  7 Agnostic $75-100k                 122 #>  8 Agnostic $100-150k                109 #>  9 Agnostic >150k                     84 #> 10 Agnostic Don't know/refused        96 #> # ℹ 170 more rows billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard2 <- billboard %>%   pivot_longer(     wk1:wk76,     names_to = \"week\",     values_to = \"rank\",     values_drop_na = TRUE   ) billboard2 #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk1      87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk2      82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk3      72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk4      77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk5      87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk6      94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   wk7      99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   wk1      91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   wk2      87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   wk3      92 #> # ℹ 5,297 more rows billboard3 <- billboard2 %>%   mutate(     week = as.integer(gsub(\"wk\", \"\", week)),     date = as.Date(date.entered) + 7 * (week - 1),     date.entered = NULL   ) billboard3 #> # A tibble: 5,307 × 5 #>    artist  track                    week  rank date       #>    <chr>   <chr>                   <int> <dbl> <date>     #>  1 2 Pac   Baby Don't Cry (Keep...     1    87 2000-02-26 #>  2 2 Pac   Baby Don't Cry (Keep...     2    82 2000-03-04 #>  3 2 Pac   Baby Don't Cry (Keep...     3    72 2000-03-11 #>  4 2 Pac   Baby Don't Cry (Keep...     4    77 2000-03-18 #>  5 2 Pac   Baby Don't Cry (Keep...     5    87 2000-03-25 #>  6 2 Pac   Baby Don't Cry (Keep...     6    94 2000-04-01 #>  7 2 Pac   Baby Don't Cry (Keep...     7    99 2000-04-08 #>  8 2Ge+her The Hardest Part Of ...     1    91 2000-09-02 #>  9 2Ge+her The Hardest Part Of ...     2    87 2000-09-09 #> 10 2Ge+her The Hardest Part Of ...     3    92 2000-09-16 #> # ℹ 5,297 more rows billboard3 %>% arrange(artist, track, week) #> # A tibble: 5,307 × 5 #>    artist  track                    week  rank date       #>    <chr>   <chr>                   <int> <dbl> <date>     #>  1 2 Pac   Baby Don't Cry (Keep...     1    87 2000-02-26 #>  2 2 Pac   Baby Don't Cry (Keep...     2    82 2000-03-04 #>  3 2 Pac   Baby Don't Cry (Keep...     3    72 2000-03-11 #>  4 2 Pac   Baby Don't Cry (Keep...     4    77 2000-03-18 #>  5 2 Pac   Baby Don't Cry (Keep...     5    87 2000-03-25 #>  6 2 Pac   Baby Don't Cry (Keep...     6    94 2000-04-01 #>  7 2 Pac   Baby Don't Cry (Keep...     7    99 2000-04-08 #>  8 2Ge+her The Hardest Part Of ...     1    91 2000-09-02 #>  9 2Ge+her The Hardest Part Of ...     2    87 2000-09-09 #> 10 2Ge+her The Hardest Part Of ...     3    92 2000-09-16 #> # ℹ 5,297 more rows billboard3 %>% arrange(date, rank) #> # A tibble: 5,307 × 5 #>    artist   track   week  rank date       #>    <chr>    <chr>  <int> <dbl> <date>     #>  1 Lonestar Amazed     1    81 1999-06-05 #>  2 Lonestar Amazed     2    54 1999-06-12 #>  3 Lonestar Amazed     3    44 1999-06-19 #>  4 Lonestar Amazed     4    39 1999-06-26 #>  5 Lonestar Amazed     5    38 1999-07-03 #>  6 Lonestar Amazed     6    33 1999-07-10 #>  7 Lonestar Amazed     7    29 1999-07-17 #>  8 Amber    Sexual     1    99 1999-07-17 #>  9 Lonestar Amazed     8    29 1999-07-24 #> 10 Amber    Sexual     2    99 1999-07-24 #> # ℹ 5,297 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"multiple-variables-stored-in-one-column","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Multiple variables stored in one column","title":"Tidy data","text":"pivoting columns, key column sometimes combination multiple underlying variable names. happens tb (tuberculosis) dataset, shown . dataset comes World Health Organisation, records counts confirmed tuberculosis cases country, year, demographic group. demographic groups broken sex (m, f) age (0-14, 15-25, 25-34, 35-44, 45-54, 55-64, unknown). First use pivot_longer() gather non-variable columns: Column headers format often separated non-alphanumeric character (e.g. ., -, _, :), fixed width format, like dataset. separate() makes easy split compound variables individual variables. can either pass regular expression split (default split non-alphanumeric columns), vector character positions. case want split first character: Storing values form resolves problem original data. want compare rates, counts, means need know population. original format, easy way add population variable. stored separate table, makes hard correctly match populations counts. tidy form, adding variables population rate easy ’re just additional columns. case, also transformation single step supplying multiple column names names_to also supplying grouped regular expression names_pattern:","code":"tb <- as_tibble(read.csv(\"tb.csv\", stringsAsFactors = FALSE)) tb #> # A tibble: 5,769 × 22 #>    iso2   year   m04  m514  m014 m1524 m2534 m3544 m4554 m5564   m65    mu #>    <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> #>  1 AD     1989    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  2 AD     1990    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  3 AD     1991    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  4 AD     1992    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  5 AD     1993    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  6 AD     1994    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #>  7 AD     1996    NA    NA     0     0     0     4     1     0     0    NA #>  8 AD     1997    NA    NA     0     0     1     2     2     1     6    NA #>  9 AD     1998    NA    NA     0     0     0     1     0     0     0    NA #> 10 AD     1999    NA    NA     0     0     0     1     1     0     0    NA #> # ℹ 5,759 more rows #> # ℹ 10 more variables: f04 <int>, f514 <int>, f014 <int>, f1524 <int>, #> #   f2534 <int>, f3544 <int>, f4554 <int>, f5564 <int>, f65 <int>, #> #   fu <int> tb2 <- tb %>%   pivot_longer(     !c(iso2, year),     names_to = \"demo\",     values_to = \"n\",     values_drop_na = TRUE   ) tb2 #> # A tibble: 35,750 × 4 #>    iso2   year demo      n #>    <chr> <int> <chr> <int> #>  1 AD     1996 m014      0 #>  2 AD     1996 m1524     0 #>  3 AD     1996 m2534     0 #>  4 AD     1996 m3544     4 #>  5 AD     1996 m4554     1 #>  6 AD     1996 m5564     0 #>  7 AD     1996 m65       0 #>  8 AD     1996 f014      0 #>  9 AD     1996 f1524     1 #> 10 AD     1996 f2534     1 #> # ℹ 35,740 more rows tb3 <- tb2 %>%   separate(demo, c(\"sex\", \"age\"), 1) tb3 #> # A tibble: 35,750 × 5 #>    iso2   year sex   age       n #>    <chr> <int> <chr> <chr> <int> #>  1 AD     1996 m     014       0 #>  2 AD     1996 m     1524      0 #>  3 AD     1996 m     2534      0 #>  4 AD     1996 m     3544      4 #>  5 AD     1996 m     4554      1 #>  6 AD     1996 m     5564      0 #>  7 AD     1996 m     65        0 #>  8 AD     1996 f     014       0 #>  9 AD     1996 f     1524      1 #> 10 AD     1996 f     2534      1 #> # ℹ 35,740 more rows tb %>% pivot_longer(   !c(iso2, year),   names_to = c(\"sex\", \"age\"),   names_pattern = \"(.)(.+)\",   values_to = \"n\",   values_drop_na = TRUE ) #> # A tibble: 35,750 × 5 #>    iso2   year sex   age       n #>    <chr> <int> <chr> <chr> <int> #>  1 AD     1996 m     014       0 #>  2 AD     1996 m     1524      0 #>  3 AD     1996 m     2534      0 #>  4 AD     1996 m     3544      4 #>  5 AD     1996 m     4554      1 #>  6 AD     1996 m     5564      0 #>  7 AD     1996 m     65        0 #>  8 AD     1996 f     014       0 #>  9 AD     1996 f     1524      1 #> 10 AD     1996 f     2534      1 #> # ℹ 35,740 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"variables-are-stored-in-both-rows-and-columns","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Variables are stored in both rows and columns","title":"Tidy data","text":"complicated form messy data occurs variables stored rows columns. code loads daily weather data Global Historical Climatology Network one weather station (MX17004) Mexico five months 2010. variables individual columns (id, year, month), spread across columns (day, d1-d31) across rows (tmin, tmax) (minimum maximum temperature). Months fewer 31 days structural missing values last day(s) month. tidy dataset first use pivot_longer gather day columns: presentation, ’ve dropped missing values, making implicit rather explicit. ok know many days month can easily reconstruct explicit missing values. ’ll also little cleaning: dataset mostly tidy, element column variable; stores names variables. (shown example meteorological variables prcp (precipitation) snow (snowfall)). Fixing requires widening data: pivot_wider() inverse pivot_longer(), pivoting element value back across multiple columns: form tidy: ’s one variable column, row represents one day.","code":"weather <- as_tibble(read.csv(\"weather.csv\", stringsAsFactors = FALSE)) weather #> # A tibble: 22 × 35 #>    id       year month element    d1    d2    d3    d4    d5    d6    d7 #>    <chr>   <int> <int> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 MX17004  2010     1 tmax       NA  NA    NA      NA  NA      NA    NA #>  2 MX17004  2010     1 tmin       NA  NA    NA      NA  NA      NA    NA #>  3 MX17004  2010     2 tmax       NA  27.3  24.1    NA  NA      NA    NA #>  4 MX17004  2010     2 tmin       NA  14.4  14.4    NA  NA      NA    NA #>  5 MX17004  2010     3 tmax       NA  NA    NA      NA  32.1    NA    NA #>  6 MX17004  2010     3 tmin       NA  NA    NA      NA  14.2    NA    NA #>  7 MX17004  2010     4 tmax       NA  NA    NA      NA  NA      NA    NA #>  8 MX17004  2010     4 tmin       NA  NA    NA      NA  NA      NA    NA #>  9 MX17004  2010     5 tmax       NA  NA    NA      NA  NA      NA    NA #> 10 MX17004  2010     5 tmin       NA  NA    NA      NA  NA      NA    NA #> # ℹ 12 more rows #> # ℹ 24 more variables: d8 <dbl>, d9 <lgl>, d10 <dbl>, d11 <dbl>, #> #   d12 <lgl>, d13 <dbl>, d14 <dbl>, d15 <dbl>, d16 <dbl>, d17 <dbl>, #> #   d18 <lgl>, d19 <lgl>, d20 <lgl>, d21 <lgl>, d22 <lgl>, d23 <dbl>, #> #   d24 <lgl>, d25 <dbl>, d26 <dbl>, d27 <dbl>, d28 <dbl>, d29 <dbl>, #> #   d30 <dbl>, d31 <dbl> weather2 <- weather %>%   pivot_longer(     d1:d31,     names_to = \"day\",     values_to = \"value\",     values_drop_na = TRUE   ) weather2 #> # A tibble: 66 × 6 #>    id       year month element day   value #>    <chr>   <int> <int> <chr>   <chr> <dbl> #>  1 MX17004  2010     1 tmax    d30    27.8 #>  2 MX17004  2010     1 tmin    d30    14.5 #>  3 MX17004  2010     2 tmax    d2     27.3 #>  4 MX17004  2010     2 tmax    d3     24.1 #>  5 MX17004  2010     2 tmax    d11    29.7 #>  6 MX17004  2010     2 tmax    d23    29.9 #>  7 MX17004  2010     2 tmin    d2     14.4 #>  8 MX17004  2010     2 tmin    d3     14.4 #>  9 MX17004  2010     2 tmin    d11    13.4 #> 10 MX17004  2010     2 tmin    d23    10.7 #> # ℹ 56 more rows weather3 <- weather2 %>%   mutate(day = as.integer(gsub(\"d\", \"\", day))) %>%   select(id, year, month, day, element, value) weather3 #> # A tibble: 66 × 6 #>    id       year month   day element value #>    <chr>   <int> <int> <int> <chr>   <dbl> #>  1 MX17004  2010     1    30 tmax     27.8 #>  2 MX17004  2010     1    30 tmin     14.5 #>  3 MX17004  2010     2     2 tmax     27.3 #>  4 MX17004  2010     2     3 tmax     24.1 #>  5 MX17004  2010     2    11 tmax     29.7 #>  6 MX17004  2010     2    23 tmax     29.9 #>  7 MX17004  2010     2     2 tmin     14.4 #>  8 MX17004  2010     2     3 tmin     14.4 #>  9 MX17004  2010     2    11 tmin     13.4 #> 10 MX17004  2010     2    23 tmin     10.7 #> # ℹ 56 more rows weather3 %>%   pivot_wider(names_from = element, values_from = value) #> # A tibble: 33 × 6 #>    id       year month   day  tmax  tmin #>    <chr>   <int> <int> <int> <dbl> <dbl> #>  1 MX17004  2010     1    30  27.8  14.5 #>  2 MX17004  2010     2     2  27.3  14.4 #>  3 MX17004  2010     2     3  24.1  14.4 #>  4 MX17004  2010     2    11  29.7  13.4 #>  5 MX17004  2010     2    23  29.9  10.7 #>  6 MX17004  2010     3     5  32.1  14.2 #>  7 MX17004  2010     3    10  34.5  16.8 #>  8 MX17004  2010     3    16  31.1  17.6 #>  9 MX17004  2010     4    27  36.3  16.7 #> 10 MX17004  2010     5    27  33.2  18.2 #> # ℹ 23 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"multiple-types","dir":"Articles","previous_headings":"Tidying messy datasets","what":"Multiple types in one table","title":"Tidy data","text":"Datasets often involve values collected multiple levels, different types observational units. tidying, type observational unit stored table. closely related idea database normalisation, fact expressed one place. ’s important otherwise inconsistencies can arise. billboard dataset actually contains observations two types observational units: song rank week. manifests duplication facts song: artist repeated many times. dataset needs broken two pieces: song dataset stores artist song name, ranking dataset gives rank song week. first extract song dataset: use make rank dataset replacing repeated song facts pointer song details (unique song id): also imagine week dataset record background information week, maybe total number songs sold similar “demographic” information. Normalisation useful tidying eliminating inconsistencies. However, data analysis tools work directly relational data, analysis usually also requires denormalisation merging datasets back one table.","code":"song <- billboard3 %>%   distinct(artist, track) %>%   mutate(song_id = row_number()) song #> # A tibble: 317 × 3 #>    artist         track                   song_id #>    <chr>          <chr>                     <int> #>  1 2 Pac          Baby Don't Cry (Keep...       1 #>  2 2Ge+her        The Hardest Part Of ...       2 #>  3 3 Doors Down   Kryptonite                    3 #>  4 3 Doors Down   Loser                         4 #>  5 504 Boyz       Wobble Wobble                 5 #>  6 98^0           Give Me Just One Nig...       6 #>  7 A*Teens        Dancing Queen                 7 #>  8 Aaliyah        I Don't Wanna                 8 #>  9 Aaliyah        Try Again                     9 #> 10 Adams, Yolanda Open My Heart                10 #> # ℹ 307 more rows rank <- billboard3 %>%   left_join(song, c(\"artist\", \"track\")) %>%   select(song_id, date, week, rank) rank #> # A tibble: 5,307 × 4 #>    song_id date        week  rank #>      <int> <date>     <int> <dbl> #>  1       1 2000-02-26     1    87 #>  2       1 2000-03-04     2    82 #>  3       1 2000-03-11     3    72 #>  4       1 2000-03-18     4    77 #>  5       1 2000-03-25     5    87 #>  6       1 2000-04-01     6    94 #>  7       1 2000-04-08     7    99 #>  8       2 2000-09-02     1    91 #>  9       2 2000-09-09     2    87 #> 10       2 2000-09-16     3    92 #> # ℹ 5,297 more rows"},{"path":"https://tidyr.tidyverse.org/dev/articles/tidy-data.html","id":"one-type-in-multiple-tables","dir":"Articles","previous_headings":"Tidying messy datasets","what":"One type in multiple tables","title":"Tidy data","text":"’s also common find data values single type observational unit spread multiple tables files. tables files often split another variable, represents single year, person, location. long format individual records consistent, easy problem fix: Read files list tables. table, add new column records original file name (file name often value important variable). Combine tables single table. Purrr makes straightforward R. following code generates vector file names directory (data/) match regular expression (ends .csv). Next name element vector name file. preserve names following step, ensuring row final data frame labeled source. Finally, map_dfr() loops path, reading csv file combining results single data frame. single table, can perform additional tidying needed. example type cleaning can found https://github.com/hadley/data-baby-names takes 129 yearly baby name tables provided US Social Security Administration combines single file. complicated situation occurs dataset structure changes time. example, datasets may contain different variables, variables different names, different file formats, different conventions missing values. may require tidy file individually (, ’re lucky, small groups) combine tidied. example type tidying illustrated https://github.com/hadley/data-fuel-economy, shows tidying epa fuel economy data 50,000 cars 1978 2008. raw data available online, year stored separate file four major formats many minor variations, making tidying dataset considerable challenge.","code":"library(purrr) paths <- dir(\"data\", pattern = \"\\\\.csv$\", full.names = TRUE) names(paths) <- basename(paths) map_dfr(paths, read.csv, stringsAsFactors = FALSE, .id = \"filename\")"},{"path":"https://tidyr.tidyverse.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Hadley Wickham. Author, maintainer. Davis Vaughan. Author. Maximilian Girlich. Author. Kevin Ushey. Contributor. . Copyright holder, funder.","code":""},{"path":"https://tidyr.tidyverse.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Wickham H, Vaughan D, Girlich M (2024). tidyr: Tidy Messy Data. R package version 1.3.1.9000, https://github.com/tidyverse/tidyr, https://tidyr.tidyverse.org.","code":"@Manual{,   title = {tidyr: Tidy Messy Data},   author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich},   year = {2024},   note = {R package version 1.3.1.9000, https://github.com/tidyverse/tidyr},   url = {https://tidyr.tidyverse.org}, }"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Tidy Messy Data","text":"goal tidyr help create tidy data. Tidy data data : variable column; column variable. observation row; row observation. value cell; cell single value. Tidy data describes standard way storing data used wherever possible throughout tidyverse. ensure data tidy, ’ll spend less time fighting tools time working analysis. Learn tidy data vignette(\"tidy-data\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Tidy Messy Data","text":"","code":"# The easiest way to get tidyr is to install the whole tidyverse: install.packages(\"tidyverse\")  # Alternatively, install just tidyr: install.packages(\"tidyr\")  # Or the development version from GitHub: # install.packages(\"pak\") pak::pak(\"tidyverse/tidyr\")"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Tidy Messy Data","text":"tidyr functions fall five main categories: “Pivoting” converts long wide forms. tidyr 1.0.0 introduces pivot_longer() pivot_wider(), replacing older spread() gather() functions. See vignette(\"pivot\") details. “Rectangling”, turns deeply nested lists (JSON) tidy tibbles. See unnest_longer(), unnest_wider(), hoist(), vignette(\"rectangle\") details. Nesting converts grouped data form group becomes single row containing nested data frame, unnesting opposite. See nest(), unnest(), vignette(\"nest\") details. Splitting combining character columns. Use separate_wider_delim(), separate_wider_position(), separate_wider_regex() pull single character column multiple columns; use unite() combine multiple columns single character column. Make implicit missing values explicit complete(); make explicit missing values implicit drop_na(); replace missing values next/previous value fill(), known value replace_na().","code":"library(tidyr)"},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"related-work","dir":"","previous_headings":"","what":"Related work","title":"Tidy Messy Data","text":"tidyr supersedes reshape2 (2010-2014) reshape (2005-2010). Somewhat counterintuitively, iteration package done less. tidyr designed specifically tidying data, general reshaping (reshape2), general aggregation (reshape). data.table provides high-performance implementations melt() dcast() ’d like read data reshaping CS perspective, ’d recommend following three papers: Wrangler: Interactive visual specification data transformation scripts interactive framework data cleaning (Potter’s wheel) efficiently implementing SchemaSQL SQL database system guide reading, ’s translation terminology used different places:","code":""},{"path":"https://tidyr.tidyverse.org/dev/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting help","title":"Tidy Messy Data","text":"encounter clear bug, please file minimal reproducible example github. questions discussion, please use forum.posit.co. Please note tidyr project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":null,"dir":"Reference","previous_headings":"","what":"Song rankings for Billboard top 100 in the year 2000 — billboard","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"Song rankings Billboard top 100 year 2000","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"","code":"billboard"},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"dataset variables: artist Artist name track Song name date.enter Date song entered top 100 wk1 – wk76 Rank song week entered","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/billboard.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Song rankings for Billboard top 100 in the year 2000 — billboard","text":"\"Whitburn\" project, https://waxy.org/2008/05/the_whitburn_project/, (downloaded April 2008)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Check assumptions about a pivot spec — check_pivot_spec","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"check_pivot_spec() developer facing helper function validating pivot spec used pivot_longer_spec() pivot_wider_spec(). useful extending pivot_longer() pivot_wider() new S3 methods. check_pivot_spec() makes following assertions: spec must data frame. spec must character column named .name. spec must character column named .value. .name column must unique. .name .value columns must first two columns data frame, reordered true.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"","code":"check_pivot_spec(spec, call = caller_env())"},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"spec specification data frame. useful complex pivots gives greater control metadata stored columns become column names result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/check_pivot_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check assumptions about a pivot spec — check_pivot_spec","text":"","code":"# A valid spec spec <- tibble(.name = \"a\", .value = \"b\", foo = 1) check_pivot_spec(spec) #> # A tibble: 1 × 3 #>   .name .value   foo #>   <chr> <chr>  <dbl> #> 1 a     b          1  spec <- tibble(.name = \"a\") try(check_pivot_spec(spec)) #> Error in eval(expr, envir, enclos) :  #>   `spec` must have `.name` and `.value` columns.  # `.name` and `.value` are forced to be the first two columns spec <- tibble(foo = 1, .value = \"b\", .name = \"a\") check_pivot_spec(spec) #> # A tibble: 1 × 3 #>   .name .value   foo #>   <chr> <chr>  <dbl> #> 1 a     b          1"},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":null,"dir":"Reference","previous_headings":"","what":"Chop and unchop — chop","title":"Chop and unchop — chop","text":"Chopping unchopping preserve width data frame, changing length. chop() makes df shorter converting rows within group list-columns. unchop() makes df longer expanding list-columns element list-column gets row output. chop() unchop() building blocks complicated functions (like unnest(), unnest_longer(), unnest_wider()) generally suitable programming interactive data analysis.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Chop and unchop — chop","text":"","code":"chop(data, cols, ..., error_call = current_env())  unchop(   data,   cols,   ...,   keep_empty = FALSE,   ptype = NULL,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Chop and unchop — chop","text":"data data frame. cols <tidy-select> Columns chop unchop. unchop(), column list-column containing generalised vectors (e.g. mix NULLs, atomic vector, S3 vectors, lists, data frames). ... dots future extensions must empty. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. ptype Optionally, named list column name-prototype pairs coerce cols , overriding default guessed combining individual values. Alternatively, single empty ptype can supplied, applied cols.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Chop and unchop — chop","text":"Generally, unchopping useful chopping simplifies complex data structure, nest()ing usually appropriate chop()ing since better preserves connections observations. chop() creates list-columns class vctrs::list_of() ensure consistent behaviour chopped data frame emptied. instance helps getting back original column types roundtrip chop unchop. <list_of> keeps tracks type elements, unchop() able reconstitute correct vector type even empty list-columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/chop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Chop and unchop — chop","text":"","code":"# Chop ---------------------------------------------------------------------- df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1) # Note that we get one row of output for each unique combination of # non-chopped variables df %>% chop(c(y, z)) #> # A tibble: 3 × 3 #>       x           y           z #>   <dbl> <list<int>> <list<int>> #> 1     1         [3]         [3] #> 2     2         [2]         [2] #> 3     3         [1]         [1] # cf nest df %>% nest(data = c(y, z)) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Unchop -------------------------------------------------------------------- df <- tibble(x = 1:4, y = list(integer(), 1L, 1:2, 1:3)) df %>% unchop(y) #> # A tibble: 6 × 2 #>       x     y #>   <int> <int> #> 1     2     1 #> 2     3     1 #> 3     3     2 #> 4     4     1 #> 5     4     2 #> 6     4     3 df %>% unchop(y, keep_empty = TRUE) #> # A tibble: 7 × 2 #>       x     y #>   <int> <int> #> 1     1    NA #> 2     2     1 #> 3     3     1 #> 4     3     2 #> 5     4     1 #> 6     4     2 #> 7     4     3  # unchop will error if the types are not compatible: df <- tibble(x = 1:2, y = list(\"1\", 1:3)) try(df %>% unchop(y)) #> Error in list_unchop(col, ptype = col_ptype) :  #>   Can't combine `x[[1]]` <character> and `x[[2]]` <integer>.  # Unchopping a list-col of data frames must generate a df-col because # unchop leaves the column names unchanged df <- tibble(x = 1:3, y = list(NULL, tibble(x = 1), tibble(y = 1:2))) df %>% unchop(y) #> # A tibble: 3 × 2 #>       x   y$x    $y #>   <int> <dbl> <int> #> 1     2     1    NA #> 2     3    NA     1 #> 3     3    NA     2 df %>% unchop(y, keep_empty = TRUE) #> # A tibble: 4 × 2 #>       x   y$x    $y #>   <int> <dbl> <int> #> 1     1    NA    NA #> 2     2     1    NA #> 3     3    NA     1 #> 4     3    NA     2"},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":null,"dir":"Reference","previous_headings":"","what":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"Two datasets public data provided Centers Medicare & Medicaid Services, https://data.cms.gov. cms_patient_experience contains lightly cleaned data \"Hospice - Provider Data\", provides list hospice agencies along data quality patient care, https://data.cms.gov/provider-data/dataset/252m-zfp9. cms_patient_care \"Doctors Clinicians Quality Payment Program PY 2020 Virtual Group Public Reporting\", https://data.cms.gov/provider-data/dataset/8c70-d353","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"","code":"cms_patient_experience  cms_patient_care"},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"cms_patient_experience data frame 500 observations five variables: org_pac_id,org_nm Organisation ID name measure_cd,measure_title Measure code title prf_rate Measure performance rate cms_patient_care data frame 252 observations five variables: ccn,facility_name Facility ID name measure_abbr Abbreviated measurement title, suitable use variable name score Measure score type Whether score refers rating 100 (\"observed\"), maximum possible value raw score (\"denominator\")","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/cms_patient_experience.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data from the Centers for Medicare & Medicaid Services — cms_patient_experience","text":"","code":"cms_patient_experience %>%   dplyr::distinct(measure_cd, measure_title) #> # A tibble: 6 × 2 #>   measure_cd   measure_title                                               #>   <chr>        <chr>                                                       #> 1 CAHPS_GRP_1  CAHPS for MIPS SSM: Getting Timely Care, Appointments, and… #> 2 CAHPS_GRP_2  CAHPS for MIPS SSM: How Well Providers Communicate          #> 3 CAHPS_GRP_3  CAHPS for MIPS SSM: Patient's Rating of Provider            #> 4 CAHPS_GRP_5  CAHPS for MIPS SSM: Health Promotion and Education          #> 5 CAHPS_GRP_8  CAHPS for MIPS SSM: Courteous and Helpful Office Staff      #> 6 CAHPS_GRP_12 CAHPS for MIPS SSM: Stewardship of Patient Resources         cms_patient_experience %>%   pivot_wider(     id_cols = starts_with(\"org\"),     names_from = measure_cd,     values_from = prf_rate  ) #> # A tibble: 95 × 8 #>    org_pac_id org_nm       CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 #>    <chr>      <chr>              <dbl>       <dbl>       <dbl>       <dbl> #>  1 0446157747 USC CARE ME…          63          87          86          57 #>  2 0446162697 ASSOCIATION…          59          85          83          63 #>  3 0547164295 BEAVER MEDI…          49          NA          75          44 #>  4 0749333730 CAPE PHYSIC…          67          84          85          65 #>  5 0840104360 ALLIANCE PH…          66          87          87          64 #>  6 0840109864 REX HOSPITA…          73          87          84          67 #>  7 0840513552 SCL HEALTH …          58          83          76          58 #>  8 0941545784 GRITMAN MED…          46          86          81          54 #>  9 1052612785 COMMUNITY M…          65          84          80          58 #> 10 1254237779 OUR LADY OF…          61          NA          NA          65 #> # ℹ 85 more rows #> # ℹ 2 more variables: CAHPS_GRP_8 <dbl>, CAHPS_GRP_12 <dbl>  cms_patient_care %>%   pivot_wider(     names_from = type,     values_from = score   ) #> # A tibble: 126 × 5 #>    ccn    facility_name                  measure_abbr denominator observed #>    <chr>  <chr>                          <chr>              <dbl>    <dbl> #>  1 011500 BAPTIST HOSPICE                beliefs_add…         202    100   #>  2 011500 BAPTIST HOSPICE                composite_p…         202     88.1 #>  3 011500 BAPTIST HOSPICE                dyspena_tre…         110     99.1 #>  4 011500 BAPTIST HOSPICE                dyspnea_scr…         202    100   #>  5 011500 BAPTIST HOSPICE                opioid_bowel          61    100   #>  6 011500 BAPTIST HOSPICE                pain_assess…         107    100   #>  7 011500 BAPTIST HOSPICE                pain_screen…         202     88.6 #>  8 011500 BAPTIST HOSPICE                treat_pref           202    100   #>  9 011500 BAPTIST HOSPICE                visits_immi…         232     96.1 #> 10 011501 SOUTHERNCARE NEW BEACON N. BI… beliefs_add…         525    100   #> # ℹ 116 more rows  cms_patient_care %>%   pivot_wider(     names_from = measure_abbr,     values_from = score   ) #> # A tibble: 28 × 12 #>    ccn    facility_name          type  beliefs_addressed composite_process #>    <chr>  <chr>                  <chr>             <dbl>             <dbl> #>  1 011500 BAPTIST HOSPICE        deno…             202               202   #>  2 011500 BAPTIST HOSPICE        obse…             100                88.1 #>  3 011501 SOUTHERNCARE NEW BEAC… deno…             525               525   #>  4 011501 SOUTHERNCARE NEW BEAC… obse…             100               100   #>  5 011502 COMFORT CARE COASTAL … deno…             295               295   #>  6 011502 COMFORT CARE COASTAL … obse…             100                99.3 #>  7 011503 SAAD HOSPICE SERVICES  deno…             694               694   #>  8 011503 SAAD HOSPICE SERVICES  obse…              99.9              96   #>  9 011505 HOSPICE FAMILY CARE    deno…             600               600   #> 10 011505 HOSPICE FAMILY CARE    obse…              97.8              92   #> # ℹ 18 more rows #> # ℹ 7 more variables: dyspena_treatment <dbl>, dyspnea_screening <dbl>, #> #   opioid_bowel <dbl>, pain_assessment <dbl>, pain_screening <dbl>, #> #   treat_pref <dbl>, visits_imminent <dbl>  cms_patient_care %>%   pivot_wider(     names_from = c(measure_abbr, type),     values_from = score   ) #> # A tibble: 14 × 20 #>    ccn    facility_name      beliefs_addressed_de…¹ beliefs_addressed_ob…² #>    <chr>  <chr>                               <dbl>                  <dbl> #>  1 011500 BAPTIST HOSPICE                       202                  100   #>  2 011501 SOUTHERNCARE NEW …                    525                  100   #>  3 011502 COMFORT CARE COAS…                    295                  100   #>  4 011503 SAAD HOSPICE SERV…                    694                   99.9 #>  5 011505 HOSPICE FAMILY CA…                    600                   97.8 #>  6 011506 SOUTHERNCARE NEW …                    589                  100   #>  7 011508 SOUTHERNCARE NEW …                    420                  100   #>  8 011510 CULLMAN REGIONAL …                     54                  100   #>  9 011511 HOSPICE OF THE VA…                    179                  100   #> 10 011512 SOUTHERNCARE NEW …                    396                  100   #> 11 011513 SHEPHERD'S COVE H…                    335                   99.1 #> 12 011514 ST VINCENT'S HOSP…                    210                  100   #> 13 011516 HOSPICE OF LIMEST…                    103                  100   #> 14 011517 HOSPICE OF WEST A…                    400                   99.8 #> # ℹ abbreviated names: ¹​beliefs_addressed_denominator, #> #   ²​beliefs_addressed_observed #> # ℹ 16 more variables: composite_process_denominator <dbl>, #> #   composite_process_observed <dbl>, #> #   dyspena_treatment_denominator <dbl>, #> #   dyspena_treatment_observed <dbl>, #> #   dyspnea_screening_denominator <dbl>, …"},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":null,"dir":"Reference","previous_headings":"","what":"Complete a data frame with missing combinations of data — complete","title":"Complete a data frame with missing combinations of data — complete","text":"Turns implicit missing values explicit missing values. wrapper around expand(), dplyr::full_join() replace_na() useful completing missing combinations data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Complete a data frame with missing combinations of data — complete","text":"","code":"complete(data, ..., fill = list(), explicit = TRUE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Complete a data frame with missing combinations of data — complete","text":"data data frame. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). fill named list variable supplies single value use instead NA missing combinations. explicit implicit (newly created) explicit (pre-existing) missing values filled fill? default, TRUE, set FALSE limit fill implicit missing values.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Complete a data frame with missing combinations of data — complete","text":"grouped data frames created dplyr::group_by(), complete() operates within group. , complete grouping column.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/complete.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Complete a data frame with missing combinations of data — complete","text":"","code":"df <- tibble(   group = c(1:2, 1, 2),   item_id = c(1:2, 2, 3),   item_name = c(\"a\", \"a\", \"b\", \"b\"),   value1 = c(1, NA, 3, 4),   value2 = 4:7 ) df #> # A tibble: 4 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     2       2 a             NA      5 #> 3     1       2 b              3      6 #> 4     2       3 b              4      7  # Combinations -------------------------------------------------------------- # Generate all possible combinations of `group`, `item_id`, and `item_name` # (whether or not they appear in the data) df %>% complete(group, item_id, item_name) #> # A tibble: 12 × 5 #>    group item_id item_name value1 value2 #>    <dbl>   <dbl> <chr>      <dbl>  <int> #>  1     1       1 a              1      4 #>  2     1       1 b             NA     NA #>  3     1       2 a             NA     NA #>  4     1       2 b              3      6 #>  5     1       3 a             NA     NA #>  6     1       3 b             NA     NA #>  7     2       1 a             NA     NA #>  8     2       1 b             NA     NA #>  9     2       2 a             NA      5 #> 10     2       2 b             NA     NA #> 11     2       3 a             NA     NA #> 12     2       3 b              4      7  # Cross all possible `group` values with the unique pairs of # `(item_id, item_name)` that already exist in the data df %>% complete(group, nesting(item_id, item_name)) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a             NA     NA #> 3     1       2 b              3      6 #> 4     1       3 b             NA     NA #> 5     2       1 a             NA     NA #> 6     2       2 a             NA      5 #> 7     2       2 b             NA     NA #> 8     2       3 b              4      7  # Within each `group`, generate all possible combinations of # `item_id` and `item_name` that occur in that group df %>%   dplyr::group_by(group) %>%   complete(item_id, item_name) #> # A tibble: 8 × 5 #> # Groups:   group [2] #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       1 b             NA     NA #> 3     1       2 a             NA     NA #> 4     1       2 b              3      6 #> 5     2       2 a             NA      5 #> 6     2       2 b             NA     NA #> 7     2       3 a             NA     NA #> 8     2       3 b              4      7  # Supplying values for new rows --------------------------------------------- # Use `fill` to replace NAs with some value. By default, affects both new # (implicit) and pre-existing (explicit) missing values. df %>%   complete(     group,     nesting(item_id, item_name),     fill = list(value1 = 0, value2 = 99)   ) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a              0     99 #> 3     1       2 b              3      6 #> 4     1       3 b              0     99 #> 5     2       1 a              0     99 #> 6     2       2 a              0      5 #> 7     2       2 b              0     99 #> 8     2       3 b              4      7  # Limit the fill to only the newly created (i.e. previously implicit) # missing values with `explicit = FALSE` df %>%   complete(     group,     nesting(item_id, item_name),     fill = list(value1 = 0, value2 = 99),     explicit = FALSE   ) #> # A tibble: 8 × 5 #>   group item_id item_name value1 value2 #>   <dbl>   <dbl> <chr>      <dbl>  <int> #> 1     1       1 a              1      4 #> 2     1       2 a              0     99 #> 3     1       2 b              3      6 #> 4     1       3 b              0     99 #> 5     2       1 a              0     99 #> 6     2       2 a             NA      5 #> 7     2       2 b              0     99 #> 8     2       3 b              4      7"},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":null,"dir":"Reference","previous_headings":"","what":"Completed construction in the US in 2018 — construction","title":"Completed construction in the US in 2018 — construction","text":"Completed construction US 2018","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Completed construction in the US in 2018 — construction","text":"","code":"construction"},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Completed construction in the US in 2018 — construction","text":"dataset variables: Year,Month Record date 1 unit, 2 4 units, 5 units mote Number completed units size Northeast,Midwest,South,West Number completed units region","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/construction.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Completed construction in the US in 2018 — construction","text":"Completions \"New Residential Construction\" found Table 5 https://www.census.gov/construction/nrc/xls/newresconst.xls (downloaded March 2019)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated SE versions of main verbs — deprecated-se","title":"Deprecated SE versions of main verbs — deprecated-se","text":"tidyr used offer twin versions verb suffixed underscore. versions standard evaluation (SE) semantics: rather taking arguments code, like NSE verbs, took arguments value. purpose make possible program tidyr. However, tidyr now uses tidy evaluation semantics. NSE verbs still capture arguments, can now unquote parts arguments. offers full programmability NSE verbs. Thus, underscored versions now superfluous. Unquoting triggers immediate evaluation operand inlines result within captured expression. result can value expression evaluated later rest argument. See vignette(\"programming\", \"dplyr\") information.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated SE versions of main verbs — deprecated-se","text":"","code":"complete_(data, cols, fill = list(), ...)  drop_na_(data, vars)  expand_(data, dots, ...)  crossing_(x)  nesting_(x)  extract_(   data,   col,   into,   regex = \"([[:alnum:]]+)\",   remove = TRUE,   convert = FALSE,   ... )  fill_(data, fill_cols, .direction = c(\"down\", \"up\"))  gather_(   data,   key_col,   value_col,   gather_cols,   na.rm = FALSE,   convert = FALSE,   factor_key = FALSE )  nest_(...)  separate_rows_(data, cols, sep = \"[^[:alnum:].]+\", convert = FALSE)  separate_(   data,   col,   into,   sep = \"[^[:alnum:]]+\",   remove = TRUE,   convert = FALSE,   extra = \"warn\",   fill = \"warn\",   ... )  spread_(   data,   key_col,   value_col,   fill = NA,   convert = FALSE,   drop = TRUE,   sep = NULL )  unite_(data, col, from, sep = \"_\", remove = TRUE)  unnest_(...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/deprecated-se.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated SE versions of main verbs — deprecated-se","text":"data data frame fill named list variable supplies single value use instead NA missing combinations. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). vars, cols, col Name columns. x nesting_ crossing_ list variables. Names new variables create character vector. Use NA omit variable output. regex string representing regular expression used extract desired values. one group (defined ()) element . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. fill_cols Character vector column names. .direction Direction fill missing values. Currently either \"\" (default), \"\", \"downup\" (.e. first ) \"updown\" (first ). key_col, value_col Strings giving names key value cols. gather_cols Character vector giving column names gathered pair key-value columns. na.rm TRUE, remove rows output value column NA. factor_key FALSE, default, key values stored character vector. TRUE, stored factor, preserves original ordering columns. sep Separator delimiting collapsed values. extra sep character vector, controls happens many pieces. three valid options: \"warn\" (default): emit warning drop extra values. \"drop\": drop extra values without warning. \"merge\": splits length() times drop FALSE, keep factor levels appear data, filling missing combinations fill. Names existing columns character vector","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop rows containing missing values — drop_na","title":"Drop rows containing missing values — drop_na","text":"drop_na() drops rows column specified ... contains missing value.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop rows containing missing values — drop_na","text":"","code":"drop_na(data, ...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop rows containing missing values — drop_na","text":"data data frame. ... <tidy-select> Columns inspect missing values. empty, columns used.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drop rows containing missing values — drop_na","text":"Another way interpret drop_na() keeps \"complete\" rows (rows contain missing values). Internally, completeness computed vctrs::vec_detect_complete().","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/drop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop rows containing missing values — drop_na","text":"","code":"df <- tibble(x = c(1, 2, NA), y = c(\"a\", NA, \"b\")) df %>% drop_na() #> # A tibble: 1 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     df %>% drop_na(x) #> # A tibble: 2 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     #> 2     2 NA     vars <- \"y\" df %>% drop_na(x, any_of(vars)) #> # A tibble: 1 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand data frame to include all possible combinations of values — expand","title":"Expand data frame to include all possible combinations of values — expand","text":"expand() generates combination variables found dataset. paired nesting() crossing() helpers. crossing() wrapper around expand_grid() de-duplicates sorts inputs; nesting() helper finds combinations already present data. expand() often useful conjunction joins: use right_join() convert implicit missing values explicit missing values (e.g., fill gaps data frame). use anti_join() figure combinations missing (e.g., identify gaps data frame).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand data frame to include all possible combinations of values — expand","text":"","code":"expand(data, ..., .name_repair = \"check_unique\")  crossing(..., .name_repair = \"check_unique\")  nesting(..., .name_repair = \"check_unique\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand data frame to include all possible combinations of values — expand","text":"data data frame. ... <data-masking> Specification columns expand complete. Columns can atomic vectors lists. find unique combinations x, y z, including present data, supply variable separate argument: expand(df, x, y, z) complete(df, x, y, z). find combinations occur data, use nesting: expand(df, nesting(x, y, z)). can combine two forms. example, expand(df, nesting(school_id, student_id), date) produce row present school-student combination possible dates. used factors, expand() complete() use full set levels, just appear data. want use values seen data, use forcats::fct_drop(). used continuous variables, may need fill values appear data: use expressions like year = 2010:2020 year = full_seq(year,1). .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Expand data frame to include all possible combinations of values — expand","text":"grouped data frames created dplyr::group_by(), expand() operates within group. , expand grouping column.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/expand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand data frame to include all possible combinations of values — expand","text":"","code":"# Finding combinations ------------------------------------------------------ fruits <- tibble(   type = c(\"apple\", \"orange\", \"apple\", \"orange\", \"orange\", \"orange\"),   year = c(2010, 2010, 2012, 2010, 2011, 2012),   size = factor(     c(\"XS\", \"S\", \"M\", \"S\", \"S\", \"M\"),     levels = c(\"XS\", \"S\", \"M\", \"L\")   ),   weights = rnorm(6, as.numeric(size) + 2) )  # All combinations, including factor levels that are not used fruits %>% expand(type) #> # A tibble: 2 × 1 #>   type   #>   <chr>  #> 1 apple  #> 2 orange fruits %>% expand(size) #> # A tibble: 4 × 1 #>   size  #>   <fct> #> 1 XS    #> 2 S     #> 3 M     #> 4 L     fruits %>% expand(type, size) #> # A tibble: 8 × 2 #>   type   size  #>   <chr>  <fct> #> 1 apple  XS    #> 2 apple  S     #> 3 apple  M     #> 4 apple  L     #> 5 orange XS    #> 6 orange S     #> 7 orange M     #> 8 orange L     fruits %>% expand(type, size, year) #> # A tibble: 24 × 3 #>    type  size   year #>    <chr> <fct> <dbl> #>  1 apple XS     2010 #>  2 apple XS     2011 #>  3 apple XS     2012 #>  4 apple S      2010 #>  5 apple S      2011 #>  6 apple S      2012 #>  7 apple M      2010 #>  8 apple M      2011 #>  9 apple M      2012 #> 10 apple L      2010 #> # ℹ 14 more rows  # Only combinations that already appear in the data fruits %>% expand(nesting(type)) #> # A tibble: 2 × 1 #>   type   #>   <chr>  #> 1 apple  #> 2 orange fruits %>% expand(nesting(size)) #> # A tibble: 3 × 1 #>   size  #>   <fct> #> 1 XS    #> 2 S     #> 3 M     fruits %>% expand(nesting(type, size)) #> # A tibble: 4 × 2 #>   type   size  #>   <chr>  <fct> #> 1 apple  XS    #> 2 apple  M     #> 3 orange S     #> 4 orange M     fruits %>% expand(nesting(type, size, year)) #> # A tibble: 5 × 3 #>   type   size   year #>   <chr>  <fct> <dbl> #> 1 apple  XS     2010 #> 2 apple  M      2012 #> 3 orange S      2010 #> 4 orange S      2011 #> 5 orange M      2012  # Other uses ---------------------------------------------------------------- # Use with `full_seq()` to fill in values of continuous variables fruits %>% expand(type, size, full_seq(year, 1)) #> # A tibble: 24 × 3 #>    type  size  `full_seq(year, 1)` #>    <chr> <fct>               <dbl> #>  1 apple XS                   2010 #>  2 apple XS                   2011 #>  3 apple XS                   2012 #>  4 apple S                    2010 #>  5 apple S                    2011 #>  6 apple S                    2012 #>  7 apple M                    2010 #>  8 apple M                    2011 #>  9 apple M                    2012 #> 10 apple L                    2010 #> # ℹ 14 more rows fruits %>% expand(type, size, 2010:2013) #> # A tibble: 32 × 3 #>    type  size  `2010:2013` #>    <chr> <fct>       <int> #>  1 apple XS           2010 #>  2 apple XS           2011 #>  3 apple XS           2012 #>  4 apple XS           2013 #>  5 apple S            2010 #>  6 apple S            2011 #>  7 apple S            2012 #>  8 apple S            2013 #>  9 apple M            2010 #> 10 apple M            2011 #> # ℹ 22 more rows  # Use `anti_join()` to determine which observations are missing all <- fruits %>% expand(type, size, year) all #> # A tibble: 24 × 3 #>    type  size   year #>    <chr> <fct> <dbl> #>  1 apple XS     2010 #>  2 apple XS     2011 #>  3 apple XS     2012 #>  4 apple S      2010 #>  5 apple S      2011 #>  6 apple S      2012 #>  7 apple M      2010 #>  8 apple M      2011 #>  9 apple M      2012 #> 10 apple L      2010 #> # ℹ 14 more rows all %>% dplyr::anti_join(fruits) #> Joining with `by = join_by(type, size, year)` #> # A tibble: 19 × 3 #>    type   size   year #>    <chr>  <fct> <dbl> #>  1 apple  XS     2011 #>  2 apple  XS     2012 #>  3 apple  S      2010 #>  4 apple  S      2011 #>  5 apple  S      2012 #>  6 apple  M      2010 #>  7 apple  M      2011 #>  8 apple  L      2010 #>  9 apple  L      2011 #> 10 apple  L      2012 #> 11 orange XS     2010 #> 12 orange XS     2011 #> 13 orange XS     2012 #> 14 orange S      2012 #> 15 orange M      2010 #> 16 orange M      2011 #> 17 orange L      2010 #> 18 orange L      2011 #> 19 orange L      2012  # Use with `right_join()` to fill in missing rows (like `complete()`) fruits %>% dplyr::right_join(all) #> Joining with `by = join_by(type, year, size)` #> # A tibble: 25 × 4 #>    type    year size  weights #>    <chr>  <dbl> <fct>   <dbl> #>  1 apple   2010 XS       1.60 #>  2 orange  2010 S        4.26 #>  3 apple   2012 M        2.56 #>  4 orange  2010 S        3.99 #>  5 orange  2011 S        4.62 #>  6 orange  2012 M        6.15 #>  7 apple   2011 XS      NA    #>  8 apple   2012 XS      NA    #>  9 apple   2010 S       NA    #> 10 apple   2011 S       NA    #> # ℹ 15 more rows  # Use with `group_by()` to expand within each group fruits %>%   dplyr::group_by(type) %>%   expand(year, size) #> # A tibble: 20 × 3 #> # Groups:   type [2] #>    type    year size  #>    <chr>  <dbl> <fct> #>  1 apple   2010 XS    #>  2 apple   2010 S     #>  3 apple   2010 M     #>  4 apple   2010 L     #>  5 apple   2012 XS    #>  6 apple   2012 S     #>  7 apple   2012 M     #>  8 apple   2012 L     #>  9 orange  2010 XS    #> 10 orange  2010 S     #> 11 orange  2010 M     #> 12 orange  2010 L     #> 13 orange  2011 XS    #> 14 orange  2011 S     #> 15 orange  2011 M     #> 16 orange  2011 L     #> 17 orange  2012 XS    #> 18 orange  2012 S     #> 19 orange  2012 M     #> 20 orange  2012 L"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a tibble from all combinations of inputs — expand_grid","title":"Create a tibble from all combinations of inputs — expand_grid","text":"expand_grid() heavily motivated expand.grid(). Compared expand.grid(), : Produces sorted output (varying first column slowest, rather fastest). Returns tibble, data frame. Never converts strings factors. add additional attributes. Can expand generalised vector, including data frames.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a tibble from all combinations of inputs — expand_grid","text":"","code":"expand_grid(..., .name_repair = \"check_unique\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a tibble from all combinations of inputs — expand_grid","text":"... Name-value pairs. name become column name output. .name_repair Treatment problematic column names: \"minimal\": name repair checks, beyond basic existence, \"unique\": Make sure names unique empty, \"check_unique\": (default value), name repair, check unique, \"universal\": Make names unique syntactic function: apply custom name repair (e.g., .name_repair = make.names names style base R). purrr-style anonymous function, see rlang::as_function() argument passed repair vctrs::vec_as_names(). See details terms strategies used enforce .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a tibble from all combinations of inputs — expand_grid","text":"tibble one column input .... output one row combination inputs, .e. size equal product sizes inputs. implies input length 0, output zero rows.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/expand_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a tibble from all combinations of inputs — expand_grid","text":"","code":"expand_grid(x = 1:3, y = 1:2) #> # A tibble: 6 × 2 #>       x     y #>   <int> <int> #> 1     1     1 #> 2     1     2 #> 3     2     1 #> 4     2     2 #> 5     3     1 #> 6     3     2 expand_grid(l1 = letters, l2 = LETTERS) #> # A tibble: 676 × 2 #>    l1    l2    #>    <chr> <chr> #>  1 a     A     #>  2 a     B     #>  3 a     C     #>  4 a     D     #>  5 a     E     #>  6 a     F     #>  7 a     G     #>  8 a     H     #>  9 a     I     #> 10 a     J     #> # ℹ 666 more rows  # Can also expand data frames expand_grid(df = tibble(x = 1:2, y = c(2, 1)), z = 1:3) #> # A tibble: 6 × 2 #>    df$x    $y     z #>   <int> <dbl> <int> #> 1     1     2     1 #> 2     1     2     2 #> 3     1     2     3 #> 4     2     1     1 #> 5     2     1     2 #> 6     2     1     3 # And matrices expand_grid(x1 = matrix(1:4, nrow = 2), x2 = matrix(5:8, nrow = 2)) #> # A tibble: 4 × 2 #>   x1[,1]  [,2] x2[,1]  [,2] #>    <int> <int>  <int> <int> #> 1      1     3      5     7 #> 2      1     3      6     8 #> 3      2     4      5     7 #> 4      2     4      6     8"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract a character column into multiple columns using regular expression groups — extract","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"extract() superseded favour separate_wider_regex() polished API better handling problems. Superseded functions go away, receive critical bug fixes. Given regular expression capturing groups, extract() turns group new column. groups match, input NA, output NA.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"","code":"extract(   data,   col,   into,   regex = \"([[:alnum:]]+)\",   remove = TRUE,   convert = FALSE,   ... )"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"data data frame. col <tidy-select> Column expand. Names new variables create character vector. Use NA omit variable output. regex string representing regular expression used extract desired values. one group (defined ()) element . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. ... Additional arguments passed methods.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/extract.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract a character column into multiple columns using regular expression groups — extract","text":"","code":"df <- tibble(x = c(NA, \"a-b\", \"a-d\", \"b-c\", \"d-e\")) df %>% extract(x, \"A\") #> # A tibble: 5 × 1 #>   A     #>   <chr> #> 1 NA    #> 2 a     #> 3 a     #> 4 b     #> 5 d     df %>% extract(x, c(\"A\", \"B\"), \"([[:alnum:]]+)-([[:alnum:]]+)\") #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 d     e      # Now recommended df %>%   separate_wider_regex(     x,     patterns = c(A = \"[[:alnum:]]+\", \"-\", B = \"[[:alnum:]]+\")   ) #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 d     e      # If no match, NA: df %>% extract(x, c(\"A\", \"B\"), \"([a-d]+)-([a-d]+)\") #> # A tibble: 5 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 a     b     #> 3 a     d     #> 4 b     c     #> 5 NA    NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract numeric component of variable. — extract_numeric","title":"Extract numeric component of variable. — extract_numeric","text":"DEPRECATED: please use readr::parse_number() instead.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract numeric component of variable. — extract_numeric","text":"","code":"extract_numeric(x)"},{"path":"https://tidyr.tidyverse.org/dev/reference/extract_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract numeric component of variable. — extract_numeric","text":"x character vector (factor).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":null,"dir":"Reference","previous_headings":"","what":"Fill in missing values with previous or next value — fill","title":"Fill in missing values with previous or next value — fill","text":"Fills missing values selected columns using next previous entry. useful common output format values repeated, recorded change.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fill in missing values with previous or next value — fill","text":"","code":"fill(data, ..., .direction = c(\"down\", \"up\", \"downup\", \"updown\"))"},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fill in missing values with previous or next value — fill","text":"data data frame. ... <tidy-select> Columns fill. .direction Direction fill missing values. Currently either \"\" (default), \"\", \"downup\" (.e. first ) \"updown\" (first ).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fill in missing values with previous or next value — fill","text":"Missing values replaced atomic vectors; NULLs replaced lists.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Fill in missing values with previous or next value — fill","text":"grouped data frames created dplyr::group_by(), fill() applied within group, meaning fill across group boundaries.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fill.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fill in missing values with previous or next value — fill","text":"","code":"# direction = \"down\" -------------------------------------------------------- # Value (year) is recorded only when it changes sales <- tibble::tribble(   ~quarter, ~year, ~sales,   \"Q1\",    2000,    66013,   \"Q2\",      NA,    69182,   \"Q3\",      NA,    53175,   \"Q4\",      NA,    21001,   \"Q1\",    2001,    46036,   \"Q2\",      NA,    58842,   \"Q3\",      NA,    44568,   \"Q4\",      NA,    50197,   \"Q1\",    2002,    39113,   \"Q2\",      NA,    41668,   \"Q3\",      NA,    30144,   \"Q4\",      NA,    52897,   \"Q1\",    2004,    32129,   \"Q2\",      NA,    67686,   \"Q3\",      NA,    31768,   \"Q4\",      NA,    49094 ) # `fill()` defaults to replacing missing data from top to bottom sales %>% fill(year) #> # A tibble: 16 × 3 #>    quarter  year sales #>    <chr>   <dbl> <dbl> #>  1 Q1       2000 66013 #>  2 Q2       2000 69182 #>  3 Q3       2000 53175 #>  4 Q4       2000 21001 #>  5 Q1       2001 46036 #>  6 Q2       2001 58842 #>  7 Q3       2001 44568 #>  8 Q4       2001 50197 #>  9 Q1       2002 39113 #> 10 Q2       2002 41668 #> 11 Q3       2002 30144 #> 12 Q4       2002 52897 #> 13 Q1       2004 32129 #> 14 Q2       2004 67686 #> 15 Q3       2004 31768 #> 16 Q4       2004 49094  # direction = \"up\" ---------------------------------------------------------- # Value (pet_type) is missing above tidy_pets <- tibble::tribble(   ~rank, ~pet_type, ~breed,   1L,        NA,    \"Boston Terrier\",   2L,        NA,    \"Retrievers (Labrador)\",   3L,        NA,    \"Retrievers (Golden)\",   4L,        NA,    \"French Bulldogs\",   5L,        NA,    \"Bulldogs\",   6L,     \"Dog\",    \"Beagles\",   1L,        NA,    \"Persian\",   2L,        NA,    \"Maine Coon\",   3L,        NA,    \"Ragdoll\",   4L,        NA,    \"Exotic\",   5L,        NA,    \"Siamese\",   6L,     \"Cat\",    \"American Short\" )  # For values that are missing above you can use `.direction = \"up\"` tidy_pets %>%   fill(pet_type, .direction = \"up\") #> # A tibble: 12 × 3 #>     rank pet_type breed                 #>    <int> <chr>    <chr>                 #>  1     1 Dog      Boston Terrier        #>  2     2 Dog      Retrievers (Labrador) #>  3     3 Dog      Retrievers (Golden)   #>  4     4 Dog      French Bulldogs       #>  5     5 Dog      Bulldogs              #>  6     6 Dog      Beagles               #>  7     1 Cat      Persian               #>  8     2 Cat      Maine Coon            #>  9     3 Cat      Ragdoll               #> 10     4 Cat      Exotic                #> 11     5 Cat      Siamese               #> 12     6 Cat      American Short         # direction = \"downup\" ------------------------------------------------------ # Value (n_squirrels) is missing above and below within a group squirrels <- tibble::tribble(   ~group,    ~name,     ~role,     ~n_squirrels,   1,      \"Sam\",    \"Observer\",   NA,   1,     \"Mara\", \"Scorekeeper\",    8,   1,    \"Jesse\",    \"Observer\",   NA,   1,      \"Tom\",    \"Observer\",   NA,   2,     \"Mike\",    \"Observer\",   NA,   2,  \"Rachael\",    \"Observer\",   NA,   2,  \"Sydekea\", \"Scorekeeper\",   14,   2, \"Gabriela\",    \"Observer\",   NA,   3,  \"Derrick\",    \"Observer\",   NA,   3,     \"Kara\", \"Scorekeeper\",    9,   3,    \"Emily\",    \"Observer\",   NA,   3, \"Danielle\",    \"Observer\",   NA )  # The values are inconsistently missing by position within the group # Use .direction = \"downup\" to fill missing values in both directions squirrels %>%   dplyr::group_by(group) %>%   fill(n_squirrels, .direction = \"downup\") %>%   dplyr::ungroup() #> # A tibble: 12 × 4 #>    group name     role        n_squirrels #>    <dbl> <chr>    <chr>             <dbl> #>  1     1 Sam      Observer              8 #>  2     1 Mara     Scorekeeper           8 #>  3     1 Jesse    Observer              8 #>  4     1 Tom      Observer              8 #>  5     2 Mike     Observer             14 #>  6     2 Rachael  Observer             14 #>  7     2 Sydekea  Scorekeeper          14 #>  8     2 Gabriela Observer             14 #>  9     3 Derrick  Observer              9 #> 10     3 Kara     Scorekeeper           9 #> 11     3 Emily    Observer              9 #> 12     3 Danielle Observer              9  # Using `.direction = \"updown\"` accomplishes the same goal in this example"},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":null,"dir":"Reference","previous_headings":"","what":"Fish encounters — fish_encounters","title":"Fish encounters — fish_encounters","text":"Information fish swimming river: station represents autonomous monitor records tagged fish seen location. Fish travel one direction (migrating downstream). Information misses just important hits, directly recorded form data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fish encounters — fish_encounters","text":"","code":"fish_encounters"},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Fish encounters — fish_encounters","text":"dataset variables: fish Fish identifier station Measurement station seen fish seen? (1 yes, true rows)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/fish_encounters.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Fish encounters — fish_encounters","text":"Dataset provided Myfanwy Johnston; details https://fishsciences.github.io/post/visualizing-fish-encounter-histories/","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":null,"dir":"Reference","previous_headings":"","what":"Create the full sequence of values in a vector — full_seq","title":"Create the full sequence of values in a vector — full_seq","text":"useful want fill missing values observed . example, full_seq(c(1, 2, 4, 6), 1) return 1:6.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create the full sequence of values in a vector — full_seq","text":"","code":"full_seq(x, period, tol = 1e-06)"},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create the full sequence of values in a vector — full_seq","text":"x numeric vector. period Gap observation. existing data checked ensure actually periodicity. tol Numerical tolerance checking periodicity.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/full_seq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create the full sequence of values in a vector — full_seq","text":"","code":"full_seq(c(1, 2, 4, 5, 10), 1) #>  [1]  1  2  3  4  5  6  7  8  9 10"},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":null,"dir":"Reference","previous_headings":"","what":"Gather columns into key-value pairs — gather","title":"Gather columns into key-value pairs — gather","text":"Development gather() complete, new code recommend switching pivot_longer(), easier use, featureful, still active development. df %>% gather(\"key\", \"value\", x, y, z) equivalent df %>% pivot_longer(c(x, y, z), names_to = \"key\", values_to = \"value\") See details vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gather columns into key-value pairs — gather","text":"","code":"gather(   data,   key = \"key\",   value = \"value\",   ...,   na.rm = FALSE,   convert = FALSE,   factor_key = FALSE )"},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gather columns into key-value pairs — gather","text":"data data frame. key, value Names new key value columns, strings symbols. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). ... selection columns. empty, variables selected. can supply bare variable names, select variables x z x:z, exclude y -y. options, see dplyr::select() documentation. See also section selection rules . na.rm TRUE, remove rows output value column NA. convert TRUE automatically run type.convert() key column. useful column types actually numeric, integer, logical. factor_key FALSE, default, key values stored character vector. TRUE, stored factor, preserves original ordering columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"rules-for-selection","dir":"Reference","previous_headings":"","what":"Rules for selection","title":"Gather columns into key-value pairs — gather","text":"Arguments selecting columns passed tidyselect::vars_select() treated specially. Unlike verbs, selecting functions make strict distinction data expressions context expressions. data expression either bare name like x expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. instance, col1:col3 data expression refers data columns, seq(start, end) context expression refers objects contexts. need refer contextual objects data expression, can use all_of() any_of(). functions used select data-variables whose names stored env-variable. instance, all_of() selects variables listed character vector . details, see tidyselect::select_helpers() documentation.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/gather.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Gather columns into key-value pairs — gather","text":"","code":"# From https://stackoverflow.com/questions/1181060 stocks <- tibble(   time = as.Date(\"2009-01-01\") + 0:9,   X = rnorm(10, 0, 1),   Y = rnorm(10, 0, 2),   Z = rnorm(10, 0, 4) )  gather(stocks, \"stock\", \"price\", -time) #> # A tibble: 30 × 3 #>    time       stock  price #>    <date>     <chr>  <dbl> #>  1 2009-01-01 X     -1.82  #>  2 2009-01-02 X     -0.247 #>  3 2009-01-03 X     -0.244 #>  4 2009-01-04 X     -0.283 #>  5 2009-01-05 X     -0.554 #>  6 2009-01-06 X      0.629 #>  7 2009-01-07 X      2.07  #>  8 2009-01-08 X     -1.63  #>  9 2009-01-09 X      0.512 #> 10 2009-01-10 X     -1.86  #> # ℹ 20 more rows stocks %>% gather(\"stock\", \"price\", -time) #> # A tibble: 30 × 3 #>    time       stock  price #>    <date>     <chr>  <dbl> #>  1 2009-01-01 X     -1.82  #>  2 2009-01-02 X     -0.247 #>  3 2009-01-03 X     -0.244 #>  4 2009-01-04 X     -0.283 #>  5 2009-01-05 X     -0.554 #>  6 2009-01-06 X      0.629 #>  7 2009-01-07 X      2.07  #>  8 2009-01-08 X     -1.63  #>  9 2009-01-09 X      0.512 #> 10 2009-01-10 X     -1.86  #> # ℹ 20 more rows  # get first observation for each Species in iris data -- base R mini_iris <- iris[c(1, 51, 101), ] # gather Sepal.Length, Sepal.Width, Petal.Length, Petal.Width gather(mini_iris, key = \"flower_att\", value = \"measurement\",        Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) #>       Species   flower_att measurement #> 1      setosa Sepal.Length         5.1 #> 2  versicolor Sepal.Length         7.0 #> 3   virginica Sepal.Length         6.3 #> 4      setosa  Sepal.Width         3.5 #> 5  versicolor  Sepal.Width         3.2 #> 6   virginica  Sepal.Width         3.3 #> 7      setosa Petal.Length         1.4 #> 8  versicolor Petal.Length         4.7 #> 9   virginica Petal.Length         6.0 #> 10     setosa  Petal.Width         0.2 #> 11 versicolor  Petal.Width         1.4 #> 12  virginica  Petal.Width         2.5 # same result but less verbose gather(mini_iris, key = \"flower_att\", value = \"measurement\", -Species) #>       Species   flower_att measurement #> 1      setosa Sepal.Length         5.1 #> 2  versicolor Sepal.Length         7.0 #> 3   virginica Sepal.Length         6.3 #> 4      setosa  Sepal.Width         3.5 #> 5  versicolor  Sepal.Width         3.2 #> 6   virginica  Sepal.Width         3.3 #> 7      setosa Petal.Length         1.4 #> 8  versicolor Petal.Length         4.7 #> 9   virginica Petal.Length         6.0 #> 10     setosa  Petal.Width         0.2 #> 11 versicolor  Petal.Width         1.4 #> 12  virginica  Petal.Width         2.5"},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":null,"dir":"Reference","previous_headings":"","what":"Hoist values out of list-columns — hoist","title":"Hoist values out of list-columns — hoist","text":"hoist() allows selectively pull components list-column top-level columns, using syntax purrr::pluck(). Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hoist values out of list-columns — hoist","text":"","code":"hoist(   .data,   .col,   ...,   .remove = TRUE,   .simplify = TRUE,   .ptype = NULL,   .transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hoist values out of list-columns — hoist","text":".data data frame. .col <tidy-select> List-column extract components . ... <dynamic-dots> Components .col turn columns form col_name = \"pluck_specification\". can pluck name character vector, position integer vector, combination two list. See purrr::pluck() details. column names must unique call hoist(), although existing columns name overwritten. plucking single string can choose omit name, .e. hoist(df, col, \"x\") short-hand hoist(df, col, x = \"x\"). .remove TRUE, default, remove extracted components .col. ensures value lives one place. components removed .col, .col removed result entirely. .simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. .ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. .transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/hoist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hoist values out of list-columns — hoist","text":"","code":"df <- tibble(   character = c(\"Toothless\", \"Dory\"),   metadata = list(     list(       species = \"dragon\",       color = \"black\",       films = c(         \"How to Train Your Dragon\",         \"How to Train Your Dragon 2\",         \"How to Train Your Dragon: The Hidden World\"       )     ),     list(       species = \"blue tang\",       color = \"blue\",       films = c(\"Finding Nemo\", \"Finding Dory\")     )   ) ) df #> # A tibble: 2 × 2 #>   character metadata         #>   <chr>     <list>           #> 1 Toothless <named list [3]> #> 2 Dory      <named list [3]>  # Extract only specified components df %>% hoist(metadata,   \"species\",   first_film = list(\"films\", 1L),   third_film = list(\"films\", 3L) ) #> # A tibble: 2 × 5 #>   character species   first_film               third_film     metadata     #>   <chr>     <chr>     <chr>                    <chr>          <list>       #> 1 Toothless dragon    How to Train Your Dragon How to Train … <named list> #> 2 Dory      blue tang Finding Nemo             NA             <named list>"},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":null,"dir":"Reference","previous_headings":"","what":"Household data — household","title":"Household data — household","text":"dataset based example vignette(\"datatable-reshape\", package = \"data.table\")","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Household data — household","text":"","code":"household"},{"path":"https://tidyr.tidyverse.org/dev/reference/household.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Household data — household","text":"data frame 5 rows 5 columns: family Family identifier dob_child1 Date birth first child dob_child2 Date birth second child name_child1 Name first child name_child2 Name second child","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":null,"dir":"Reference","previous_headings":"","what":"Nest rows into a list-column of data frames — nest","title":"Nest rows into a list-column of data frames — nest","text":"Nesting creates list-column data frames; unnesting flattens back regular columns. Nesting implicitly summarising operation: get one row group defined non-nested columns. useful conjunction summaries work whole datasets, notably models. Learn vignette(\"nest\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nest rows into a list-column of data frames — nest","text":"","code":"nest(.data, ..., .by = NULL, .key = NULL, .names_sep = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Nest rows into a list-column of data frames — nest","text":".data data frame. ... <tidy-select> Columns nest; appear inner data frames. Specified using name-variable pairs form new_col = c(col1, col2, col3). right hand side can valid tidyselect expression. supplied, ... derived columns selected ., use column name .key. : previously write df %>% nest(x, y, z). Convert df %>% nest(data = c(x, y, z)). .<tidy-select> Columns nest ; remain outer data frame. .can used place conjunction columns supplied .... supplied, .derived columns selected .... .key name resulting nested column. applicable ... specified, .e. case df %>% nest(.= x). NULL, \"data\" used default. .names_sep NULL, default, inner names come former outer names. string,  new inner names use outer names names_sep automatically stripped. makes names_sep roughly symmetric nesting unnesting.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Nest rows into a list-column of data frames — nest","text":"neither ... .supplied, nest() nest variables, use column name supplied .key.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"new-syntax","dir":"Reference","previous_headings":"","what":"New syntax","title":"Nest rows into a list-column of data frames — nest","text":"tidyr 1.0.0 introduced new syntax nest() unnest() designed similar functions. Converting new syntax straightforward (guided message receive) just need run old analysis, can easily revert previous behaviour using nest_legacy() unnest_legacy() follows:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"grouped-data-frames","dir":"Reference","previous_headings":"","what":"Grouped data frames","title":"Nest rows into a list-column of data frames — nest","text":"df %>% nest(data = c(x, y)) specifies columns nested; .e. columns appear inner data frame. df %>% nest(.= c(x, y)) specifies columns nest ; .e. columns remain outer data frame. alternative way achieve latter nest() grouped data frame created dplyr::group_by(). grouping variables remain outer data frame others nested. result preserves grouping input. Variables supplied nest() override grouping variables df %>% group_by(x, y) %>% nest(data = !z) equivalent df %>% nest(data = !z). supply .grouped data frame, groups already represent nesting .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nest rows into a list-column of data frames — nest","text":"","code":"df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)  # Specify variables to nest using name-variable pairs. # Note that we get one row of output for each unique combination of # non-nested variables. df %>% nest(data = c(y, z)) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Specify variables to nest by (rather than variables to nest) using `.by` df %>% nest(.by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # In this case, since `...` isn't used you can specify the resulting column # name with `.key` df %>% nest(.by = x, .key = \"cols\") #> # A tibble: 3 × 2 #>       x cols             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # Use tidyselect syntax and helpers, just like in `dplyr::select()` df %>% nest(data = any_of(c(\"y\", \"z\"))) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 2]> #> 2     2 <tibble [2 × 2]> #> 3     3 <tibble [1 × 2]>  # `...` and `.by` can be used together to drop columns you no longer need, # or to include the columns you are nesting by in the inner data frame too. # This drops `z`: df %>% nest(data = y, .by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 1]> #> 2     2 <tibble [2 × 1]> #> 3     3 <tibble [1 × 1]> # This includes `x` in the inner data frame: df %>% nest(data = everything(), .by = x) #> # A tibble: 3 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [3 × 3]> #> 2     2 <tibble [2 × 3]> #> 3     3 <tibble [1 × 3]>  # Multiple nesting structures can be specified at once iris %>%   nest(petal = starts_with(\"Petal\"), sepal = starts_with(\"Sepal\")) #> # A tibble: 3 × 3 #>   Species    petal             sepal             #>   <fct>      <list>            <list>            #> 1 setosa     <tibble [50 × 2]> <tibble [50 × 2]> #> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]> #> 3 virginica  <tibble [50 × 2]> <tibble [50 × 2]> iris %>%   nest(width = contains(\"Width\"), length = contains(\"Length\")) #> # A tibble: 3 × 3 #>   Species    width             length            #>   <fct>      <list>            <list>            #> 1 setosa     <tibble [50 × 2]> <tibble [50 × 2]> #> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]> #> 3 virginica  <tibble [50 × 2]> <tibble [50 × 2]>  # Nesting a grouped data frame nests all variables apart from the group vars fish_encounters %>%   dplyr::group_by(fish) %>%   nest() #> # A tibble: 19 × 2 #> # Groups:   fish [19] #>    fish  data              #>    <fct> <list>            #>  1 4842  <tibble [11 × 2]> #>  2 4843  <tibble [11 × 2]> #>  3 4844  <tibble [11 × 2]> #>  4 4845  <tibble [5 × 2]>  #>  5 4847  <tibble [3 × 2]>  #>  6 4848  <tibble [4 × 2]>  #>  7 4849  <tibble [2 × 2]>  #>  8 4850  <tibble [6 × 2]>  #>  9 4851  <tibble [2 × 2]>  #> 10 4854  <tibble [2 × 2]>  #> 11 4855  <tibble [5 × 2]>  #> 12 4857  <tibble [9 × 2]>  #> 13 4858  <tibble [11 × 2]> #> 14 4859  <tibble [5 × 2]>  #> 15 4861  <tibble [11 × 2]> #> 16 4862  <tibble [9 × 2]>  #> 17 4863  <tibble [2 × 2]>  #> 18 4864  <tibble [2 × 2]>  #> 19 4865  <tibble [3 × 2]>   # That is similar to `nest(.by = )`, except here the result isn't grouped fish_encounters %>%   nest(.by = fish) #> # A tibble: 19 × 2 #>    fish  data              #>    <fct> <list>            #>  1 4842  <tibble [11 × 2]> #>  2 4843  <tibble [11 × 2]> #>  3 4844  <tibble [11 × 2]> #>  4 4845  <tibble [5 × 2]>  #>  5 4847  <tibble [3 × 2]>  #>  6 4848  <tibble [4 × 2]>  #>  7 4849  <tibble [2 × 2]>  #>  8 4850  <tibble [6 × 2]>  #>  9 4851  <tibble [2 × 2]>  #> 10 4854  <tibble [2 × 2]>  #> 11 4855  <tibble [5 × 2]>  #> 12 4857  <tibble [9 × 2]>  #> 13 4858  <tibble [11 × 2]> #> 14 4859  <tibble [5 × 2]>  #> 15 4861  <tibble [11 × 2]> #> 16 4862  <tibble [9 × 2]>  #> 17 4863  <tibble [2 × 2]>  #> 18 4864  <tibble [2 × 2]>  #> 19 4865  <tibble [3 × 2]>   # Nesting is often useful for creating per group models mtcars %>%   nest(.by = cyl) %>%   dplyr::mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df))) #> # A tibble: 3 × 3 #>     cyl data               models #>   <dbl> <list>             <list> #> 1     6 <tibble [7 × 10]>  <lm>   #> 2     4 <tibble [11 × 10]> <lm>   #> 3     8 <tibble [14 × 10]> <lm>"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy versions of nest() and unnest() — nest_legacy","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"tidyr 1.0.0 introduced new syntax nest() unnest(). majority existing usage automatically translated new syntax warning. However, need quickly roll back previous behaviour, functions provide previous interface. make old code work , add following code top script:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"","code":"nest_legacy(data, ..., .key = \"data\")  unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"data data frame. ... Specification columns unnest. Use bare variable names functions variables. omitted, defaults list-cols. .key name new column, string symbol. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). .drop additional list columns dropped? default, unnest() drop unnesting specified columns requires rows duplicated. .id Data frame identifier - supplied, create new column name .id, giving unique identifier. useful list column named. .sep non-NULL, names unnested data frame columns combine name original list-col names nested data frame, separated .sep. .preserve Optionally, list-columns preserve output. duplicated way atomic vectors. dplyr::select() semantics can preserve multiple variables .preserve = c(x, y) .preserve = starts_with(\"list\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/nest_legacy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy versions of nest() and unnest() — nest_legacy","text":"","code":"# Nest and unnest are inverses df <- tibble(x = c(1, 1, 2), y = 3:1) df %>% nest_legacy(y) #> # A tibble: 2 × 2 #>       x data             #>   <dbl> <list>           #> 1     1 <tibble [2 × 1]> #> 2     2 <tibble [1 × 1]> df %>% nest_legacy(y) %>% unnest_legacy() #> # A tibble: 3 × 2 #>       x     y #>   <dbl> <int> #> 1     1     3 #> 2     1     2 #> 3     2     1  # nesting ------------------------------------------------------------------- as_tibble(iris) %>% nest_legacy(!Species) #> # A tibble: 3 × 2 #>   Species    data              #>   <fct>      <list>            #> 1 setosa     <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica  <tibble [50 × 4]> as_tibble(chickwts) %>% nest_legacy(weight) #> # A tibble: 6 × 2 #>   feed      data              #>   <fct>     <list>            #> 1 horsebean <tibble [10 × 1]> #> 2 linseed   <tibble [12 × 1]> #> 3 soybean   <tibble [14 × 1]> #> 4 sunflower <tibble [12 × 1]> #> 5 meatmeal  <tibble [11 × 1]> #> 6 casein    <tibble [12 × 1]>  # unnesting ----------------------------------------------------------------- df <- tibble(   x = 1:2,   y = list(     tibble(z = 1),     tibble(z = 3:4)   ) ) df %>% unnest_legacy(y) #> # A tibble: 3 × 2 #>       x     z #>   <int> <dbl> #> 1     1     1 #> 2     2     3 #> 3     2     4  # You can also unnest multiple columns simultaneously df <- tibble(   a = list(c(\"a\", \"b\"), \"c\"),   b = list(1:2, 3),   c = c(11, 22) ) df %>% unnest_legacy(a, b) #> # A tibble: 3 × 3 #>       c a         b #>   <dbl> <chr> <dbl> #> 1    11 a         1 #> 2    11 b         2 #> 3    22 c         3 # If you omit the column names, it'll unnest all list-cols df %>% unnest_legacy() #> # A tibble: 3 × 3 #>       c a         b #>   <dbl> <chr> <dbl> #> 1    11 a         1 #> 2    11 b         2 #> 3    22 c         3"},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":null,"dir":"Reference","previous_headings":"","what":"Pack and unpack — pack","title":"Pack and unpack — pack","text":"Packing unpacking preserve length data frame, changing width. pack() makes df narrow collapsing set columns single df-column. unpack() makes data wider expanding df-columns back individual columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pack and unpack — pack","text":"","code":"pack(.data, ..., .names_sep = NULL, .error_call = current_env())  unpack(   data,   cols,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pack and unpack — pack","text":"... pack(), <tidy-select> columns pack, specified using name-variable pairs form new_col = c(col1, col2, col3). right hand side can valid tidy select expression. unpack(), dots future extensions must empty. data, .data data frame. cols <tidy-select> Columns unpack. names_sep, .names_sep NULL, default, names left . pack(), inner names come former outer names; unpack(), new outer names come inner names. string, inner outer names used together. unpack(), names new outer columns formed pasting together outer inner column names, separated names_sep. pack(), new inner names outer names + names_sep automatically stripped. makes names_sep roughly symmetric packing unpacking. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . error_call, .error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pack and unpack — pack","text":"Generally, unpacking useful packing simplifies complex data structure. Currently, functions work df-cols, mostly curiosity, seem worth exploring mimic nested column headers popular Excel.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pack.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pack and unpack — pack","text":"","code":"# Packing ------------------------------------------------------------------- # It's not currently clear why you would ever want to pack columns # since few functions work with this sort of data. df <- tibble(x1 = 1:3, x2 = 4:6, x3 = 7:9, y = 1:3) df #> # A tibble: 3 × 4 #>      x1    x2    x3     y #>   <int> <int> <int> <int> #> 1     1     4     7     1 #> 2     2     5     8     2 #> 3     3     6     9     3 df %>% pack(x = starts_with(\"x\")) #> # A tibble: 3 × 2 #>       y  x$x1   $x2   $x3 #>   <int> <int> <int> <int> #> 1     1     1     4     7 #> 2     2     2     5     8 #> 3     3     3     6     9 df %>% pack(x = c(x1, x2, x3), y = y) #> # A tibble: 3 × 2 #>    x$x1   $x2   $x3   y$y #>   <int> <int> <int> <int> #> 1     1     4     7     1 #> 2     2     5     8     2 #> 3     3     6     9     3  # .names_sep allows you to strip off common prefixes; this # acts as a natural inverse to name_sep in unpack() iris %>%   as_tibble() %>%   pack(     Sepal = starts_with(\"Sepal\"),     Petal = starts_with(\"Petal\"),     .names_sep = \".\"   ) #> # A tibble: 150 × 3 #>    Species Sepal$Length $Width Petal$Length $Width #>    <fct>          <dbl>  <dbl>        <dbl>  <dbl> #>  1 setosa           5.1    3.5          1.4    0.2 #>  2 setosa           4.9    3            1.4    0.2 #>  3 setosa           4.7    3.2          1.3    0.2 #>  4 setosa           4.6    3.1          1.5    0.2 #>  5 setosa           5      3.6          1.4    0.2 #>  6 setosa           5.4    3.9          1.7    0.4 #>  7 setosa           4.6    3.4          1.4    0.3 #>  8 setosa           5      3.4          1.5    0.2 #>  9 setosa           4.4    2.9          1.4    0.2 #> 10 setosa           4.9    3.1          1.5    0.1 #> # ℹ 140 more rows  # Unpacking ----------------------------------------------------------------- df <- tibble(   x = 1:3,   y = tibble(a = 1:3, b = 3:1),   z = tibble(X = c(\"a\", \"b\", \"c\"), Y = runif(3), Z = c(TRUE, FALSE, NA)) ) df #> # A tibble: 3 × 3 #>       x   y$a    $b z$X       $Y $Z    #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(y) #> # A tibble: 3 × 4 #>       x     a     b z$X       $Y $Z    #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(c(y, z)) #> # A tibble: 3 × 6 #>       x     a     b X          Y Z     #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA    df %>% unpack(c(y, z), names_sep = \"_\") #> # A tibble: 3 × 6 #>       x   y_a   y_b z_X      z_Y z_Z   #>   <int> <int> <int> <chr>  <dbl> <lgl> #> 1     1     1     3 a     0.0281 TRUE  #> 2     2     2     2 b     0.466  FALSE #> 3     3     3     1 c     0.390  NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See %>% details.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from wide to long — pivot_longer","title":"Pivot data from wide to long — pivot_longer","text":"pivot_longer() \"lengthens\" data, increasing number rows decreasing number columns. inverse transformation pivot_wider() Learn vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from wide to long — pivot_longer","text":"","code":"pivot_longer(   data,   cols,   ...,   cols_vary = \"fastest\",   names_to = \"name\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   names_ptypes = NULL,   names_transform = NULL,   names_repair = \"check_unique\",   values_to = \"value\",   values_drop_na = FALSE,   values_ptypes = NULL,   values_transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from wide to long — pivot_longer","text":"data data frame pivot. cols <tidy-select> Columns pivot longer format. ... Additional arguments passed methods. cols_vary pivoting cols longer format, output rows arranged relative original row number? \"fastest\", default, keeps individual rows cols close together output. often produces intuitively ordered output least one key column data involved pivoting process. \"slowest\" keeps individual columns cols close together output. often produces intuitively ordered output utilize columns data pivoting process. names_to character vector specifying new column columns create information stored column names data specified cols. length 0, NULL supplied, columns created. length 1, single column created contain column names specified cols. length >1, multiple columns created. case, one names_sep names_pattern must supplied specify column names split. also two additional character values can take advantage : NA discard corresponding component column name. \".value\" indicates corresponding component column name defines name output column containing cell values, overriding values_to entirely. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, arguments control column name broken . names_sep takes specification separate(), can either numeric vector (specifying positions break ), single string (specifying regular expression split ). names_pattern takes specification extract(), regular expression containing matching groups (()). arguments give enough control, use pivot_longer_spec() create spec object process manually needed. names_ptypes, values_ptypes Optionally, list column name-prototype pairs. Alternatively, single empty prototype can supplied, applied columns. prototype (ptype short) zero-length vector (like integer() numeric()) defines type, class, attributes vector. Use arguments want confirm created columns types expect. Note want change (instead confirm) types specific columns, use names_transform values_transform instead. names_transform, values_transform Optionally, list column name-function pairs. Alternatively, single function can supplied, applied columns. Use arguments need change types specific columns. example, names_transform = list(week = .integer) convert character variable called week integer. specified, type columns generated names_to character, type variables generated values_to common type input columns used generate . names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_to string specifying name column create data stored cell values. names_to character containing special .value sentinel, value ignored, name value column derived part existing column names. values_drop_na TRUE, drop rows contain NAs value_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pivot data from wide to long — pivot_longer","text":"pivot_longer() updated approach gather(), designed simpler use handle use cases. recommend use pivot_longer() new code; gather() going away longer active development.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from wide to long — pivot_longer","text":"","code":"# See vignette(\"pivot\") for examples and explanation  # Simplest case where column names are character data relig_income #> # A tibble: 18 × 11 #>    religion      `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` #>    <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl> #>  1 Agnostic           27        34        60        81        76       137 #>  2 Atheist            12        27        37        52        35        70 #>  3 Buddhist           27        21        30        34        33        58 #>  4 Catholic          418       617       732       670       638      1116 #>  5 Don’t know/r…      15        14        15        11        10        35 #>  6 Evangelical …     575       869      1064       982       881      1486 #>  7 Hindu               1         9         7         9        11        34 #>  8 Historically…     228       244       236       238       197       223 #>  9 Jehovah's Wi…      20        27        24        24        21        30 #> 10 Jewish             19        19        25        25        30        95 #> 11 Mainline Prot     289       495       619       655       651      1107 #> 12 Mormon             29        40        48        51        56       112 #> 13 Muslim              6         7         9        10         9        23 #> 14 Orthodox           13        17        23        32        32        47 #> 15 Other Christ…       9         7        11        13        13        14 #> 16 Other Faiths       20        33        40        46        49        63 #> 17 Other World …       5         2         3         4         2         7 #> 18 Unaffiliated      217       299       374       365       341       528 #> # ℹ 4 more variables: `$75-100k` <dbl>, `$100-150k` <dbl>, `>150k` <dbl>, #> #   `Don't know/refused` <dbl> relig_income %>%   pivot_longer(!religion, names_to = \"income\", values_to = \"count\") #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows  # Slightly more complex case where columns have common prefix, # and missing missings are structural so should be dropped. billboard #> # A tibble: 317 × 79 #>    artist     track date.entered   wk1   wk2   wk3   wk4   wk5   wk6   wk7 #>    <chr>      <chr> <date>       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 2 Pac      Baby… 2000-02-26      87    82    72    77    87    94    99 #>  2 2Ge+her    The … 2000-09-02      91    87    92    NA    NA    NA    NA #>  3 3 Doors D… Kryp… 2000-04-08      81    70    68    67    66    57    54 #>  4 3 Doors D… Loser 2000-10-21      76    76    72    69    67    65    55 #>  5 504 Boyz   Wobb… 2000-04-15      57    34    25    17    17    31    36 #>  6 98^0       Give… 2000-08-19      51    39    34    26    26    19     2 #>  7 A*Teens    Danc… 2000-07-08      97    97    96    95   100    NA    NA #>  8 Aaliyah    I Do… 2000-01-29      84    62    51    41    38    35    35 #>  9 Aaliyah    Try … 2000-03-18      59    53    38    28    21    18    16 #> 10 Adams, Yo… Open… 2000-08-26      76    76    74    69    68    67    61 #> # ℹ 307 more rows #> # ℹ 69 more variables: wk8 <dbl>, wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, #> #   wk12 <dbl>, wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, #> #   wk17 <dbl>, wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, #> #   wk22 <dbl>, wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, #> #   wk27 <dbl>, wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, #> #   wk32 <dbl>, wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, … billboard %>%   pivot_longer(     cols = starts_with(\"wk\"),     names_to = \"week\",     names_prefix = \"wk\",     values_to = \"rank\",     values_drop_na = TRUE   ) #> # A tibble: 5,307 × 5 #>    artist  track                   date.entered week   rank #>    <chr>   <chr>                   <date>       <chr> <dbl> #>  1 2 Pac   Baby Don't Cry (Keep... 2000-02-26   1        87 #>  2 2 Pac   Baby Don't Cry (Keep... 2000-02-26   2        82 #>  3 2 Pac   Baby Don't Cry (Keep... 2000-02-26   3        72 #>  4 2 Pac   Baby Don't Cry (Keep... 2000-02-26   4        77 #>  5 2 Pac   Baby Don't Cry (Keep... 2000-02-26   5        87 #>  6 2 Pac   Baby Don't Cry (Keep... 2000-02-26   6        94 #>  7 2 Pac   Baby Don't Cry (Keep... 2000-02-26   7        99 #>  8 2Ge+her The Hardest Part Of ... 2000-09-02   1        91 #>  9 2Ge+her The Hardest Part Of ... 2000-09-02   2        87 #> 10 2Ge+her The Hardest Part Of ... 2000-09-02   3        92 #> # ℹ 5,297 more rows  # Multiple variables stored in column names who %>% pivot_longer(   cols = new_sp_m014:newrel_f65,   names_to = c(\"diagnosis\", \"gender\", \"age\"),   names_pattern = \"new_?(.*)_(.)(.*)\",   values_to = \"count\" ) #> # A tibble: 405,440 × 8 #>    country     iso2  iso3   year diagnosis gender age   count #>    <chr>       <chr> <chr> <dbl> <chr>     <chr>  <chr> <dbl> #>  1 Afghanistan AF    AFG    1980 sp        m      014      NA #>  2 Afghanistan AF    AFG    1980 sp        m      1524     NA #>  3 Afghanistan AF    AFG    1980 sp        m      2534     NA #>  4 Afghanistan AF    AFG    1980 sp        m      3544     NA #>  5 Afghanistan AF    AFG    1980 sp        m      4554     NA #>  6 Afghanistan AF    AFG    1980 sp        m      5564     NA #>  7 Afghanistan AF    AFG    1980 sp        m      65       NA #>  8 Afghanistan AF    AFG    1980 sp        f      014      NA #>  9 Afghanistan AF    AFG    1980 sp        f      1524     NA #> 10 Afghanistan AF    AFG    1980 sp        f      2534     NA #> # ℹ 405,430 more rows  # Multiple observations per row. Since all columns are used in the pivoting # process, we'll use `cols_vary` to keep values from the original columns # close together in the output. anscombe #>    x1 x2 x3 x4    y1   y2    y3    y4 #> 1  10 10 10  8  8.04 9.14  7.46  6.58 #> 2   8  8  8  8  6.95 8.14  6.77  5.76 #> 3  13 13 13  8  7.58 8.74 12.74  7.71 #> 4   9  9  9  8  8.81 8.77  7.11  8.84 #> 5  11 11 11  8  8.33 9.26  7.81  8.47 #> 6  14 14 14  8  9.96 8.10  8.84  7.04 #> 7   6  6  6  8  7.24 6.13  6.08  5.25 #> 8   4  4  4 19  4.26 3.10  5.39 12.50 #> 9  12 12 12  8 10.84 9.13  8.15  5.56 #> 10  7  7  7  8  4.82 7.26  6.42  7.91 #> 11  5  5  5  8  5.68 4.74  5.73  6.89 anscombe %>%   pivot_longer(     everything(),     cols_vary = \"slowest\",     names_to = c(\".value\", \"set\"),     names_pattern = \"(.)(.)\"   ) #> # A tibble: 44 × 3 #>    set       x     y #>    <chr> <dbl> <dbl> #>  1 1        10  8.04 #>  2 1         8  6.95 #>  3 1        13  7.58 #>  4 1         9  8.81 #>  5 1        11  8.33 #>  6 1        14  9.96 #>  7 1         6  7.24 #>  8 1         4  4.26 #>  9 1        12 10.8  #> 10 1         7  4.82 #> # ℹ 34 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from wide to long using a spec — pivot_longer_spec","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"low level interface pivoting, inspired cdata package, allows describe pivoting data frame.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"","code":"pivot_longer_spec(   data,   spec,   ...,   cols_vary = \"fastest\",   names_repair = \"check_unique\",   values_drop_na = FALSE,   values_ptypes = NULL,   values_transform = NULL,   error_call = current_env() )  build_longer_spec(   data,   cols,   ...,   names_to = \"name\",   values_to = \"value\",   names_prefix = NULL,   names_sep = NULL,   names_pattern = NULL,   names_ptypes = NULL,   names_transform = NULL,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"data data frame pivot. spec specification data frame. useful complex pivots gives greater control metadata stored column names turns columns result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting. ... dots future extensions must empty. cols_vary pivoting cols longer format, output rows arranged relative original row number? \"fastest\", default, keeps individual rows cols close together output. often produces intuitively ordered output least one key column data involved pivoting process. \"slowest\" keeps individual columns cols close together output. often produces intuitively ordered output utilize columns data pivoting process. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_drop_na TRUE, drop rows contain NAs value_to column. effectively converts explicit missing values implicit missing values, generally used missing values data created structure. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. cols <tidy-select> Columns pivot longer format. names_to character vector specifying new column columns create information stored column names data specified cols. length 0, NULL supplied, columns created. length 1, single column created contain column names specified cols. length >1, multiple columns created. case, one names_sep names_pattern must supplied specify column names split. also two additional character values can take advantage : NA discard corresponding component column name. \".value\" indicates corresponding component column name defines name output column containing cell values, overriding values_to entirely. values_to string specifying name column create data stored cell values. names_to character containing special .value sentinel, value ignored, name value column derived part existing column names. names_prefix regular expression used remove matching text start variable name. names_sep, names_pattern names_to contains multiple values, arguments control column name broken . names_sep takes specification separate(), can either numeric vector (specifying positions break ), single string (specifying regular expression split ). names_pattern takes specification extract(), regular expression containing matching groups (()). arguments give enough control, use pivot_longer_spec() create spec object process manually needed. names_ptypes, values_ptypes Optionally, list column name-prototype pairs. Alternatively, single empty prototype can supplied, applied columns. prototype (ptype short) zero-length vector (like integer() numeric()) defines type, class, attributes vector. Use arguments want confirm created columns types expect. Note want change (instead confirm) types specific columns, use names_transform values_transform instead. names_transform, values_transform Optionally, list column name-function pairs. Alternatively, single function can supplied, applied columns. Use arguments need change types specific columns. example, names_transform = list(week = .integer) convert character variable called week integer. specified, type columns generated names_to character, type variables generated values_to common type input columns used generate .","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_longer_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from wide to long using a spec — pivot_longer_spec","text":"","code":"# See vignette(\"pivot\") for examples and explanation  # Use `build_longer_spec()` to build `spec` using similar syntax to `pivot_longer()` # and run `pivot_longer_spec()` based on `spec`. spec <- relig_income %>% build_longer_spec(   cols = !religion,   names_to = \"income\",   values_to = \"count\" ) spec #> # A tibble: 10 × 3 #>    .name              .value income             #>    <chr>              <chr>  <chr>              #>  1 <$10k              count  <$10k              #>  2 $10-20k            count  $10-20k            #>  3 $20-30k            count  $20-30k            #>  4 $30-40k            count  $30-40k            #>  5 $40-50k            count  $40-50k            #>  6 $50-75k            count  $50-75k            #>  7 $75-100k           count  $75-100k           #>  8 $100-150k          count  $100-150k          #>  9 >150k              count  >150k              #> 10 Don't know/refused count  Don't know/refused  pivot_longer_spec(relig_income, spec) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows  # Is equivalent to: relig_income %>% pivot_longer(   cols = !religion,   names_to = \"income\",   values_to = \"count\" ) #> # A tibble: 180 × 3 #>    religion income             count #>    <chr>    <chr>              <dbl> #>  1 Agnostic <$10k                 27 #>  2 Agnostic $10-20k               34 #>  3 Agnostic $20-30k               60 #>  4 Agnostic $30-40k               81 #>  5 Agnostic $40-50k               76 #>  6 Agnostic $50-75k              137 #>  7 Agnostic $75-100k             122 #>  8 Agnostic $100-150k            109 #>  9 Agnostic >150k                 84 #> 10 Agnostic Don't know/refused    96 #> # ℹ 170 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from long to wide — pivot_wider","title":"Pivot data from long to wide — pivot_wider","text":"pivot_wider() \"widens\" data, increasing number columns decreasing number rows. inverse transformation pivot_longer(). Learn vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from long to wide — pivot_wider","text":"","code":"pivot_wider(   data,   ...,   id_cols = NULL,   id_expand = FALSE,   names_from = name,   names_prefix = \"\",   names_sep = \"_\",   names_glue = NULL,   names_sort = FALSE,   names_vary = \"fastest\",   names_expand = FALSE,   names_repair = \"check_unique\",   values_from = value,   values_fill = NULL,   values_fn = NULL,   unused_fn = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from long to wide — pivot_wider","text":"data data frame pivot. ... Additional arguments passed methods. id_cols <tidy-select> set columns uniquely identify observation. Typically used redundant variables, .e. variables whose values perfectly correlated existing variables. Defaults columns data except columns specified names_from values_from. tidyselect expression supplied, evaluated data removing columns specified names_from values_from. id_expand values id_cols columns expanded expand() pivoting? results rows, output contain complete expansion possible values id_cols. Implicit factor levels represented data become explicit. Additionally, row values corresponding expanded id_cols sorted. names_from, values_from <tidy-select> pair arguments describing column (columns) get name output column (names_from), column (columns) get cell values (values_from). values_from contains multiple values, value added front output column. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns (special .value) create custom column names. names_sort column names sorted? FALSE, default, column names ordered first appearance. names_vary names_from identifies column (columns) multiple unique values, multiple values_from columns provided, order resulting column names combined? \"fastest\" varies names_from values fastest, resulting column naming scheme form: value1_name1, value1_name2, value2_name1, value2_name2. default. \"slowest\" varies names_from values slowest, resulting column naming scheme form: value1_name1, value2_name1, value1_name2, value2_name2. names_expand values names_from columns expanded expand() pivoting? results columns, output contain column names corresponding complete expansion possible values names_from. Implicit factor levels represented data become explicit. Additionally, column names sorted, identical names_sort produce. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. values_fill Optionally, (scalar) value specifies value filled missing. can named list want apply different fill values different value columns. values_fn Optionally, function applied value cell output. typically use combination id_cols names_from columns uniquely identify observation. can named list want apply different aggregations different values_from columns. unused_fn Optionally, function applied summarize values unused columns (.e. columns identified id_cols, names_from, values_from). default drops unused columns result. can named list want apply different aggregations different unused columns. id_cols must supplied unused_fn useful, since otherwise unspecified columns considered id_cols. similar grouping id_cols summarizing unused columns using unused_fn.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pivot data from long to wide — pivot_wider","text":"pivot_wider() updated approach spread(), designed simpler use handle use cases. recommend use pivot_wider() new code; spread() going away longer active development.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from long to wide — pivot_wider","text":"","code":"# See vignette(\"pivot\") for examples and explanation  fish_encounters #> # A tibble: 114 × 3 #>    fish  station  seen #>    <fct> <fct>   <int> #>  1 4842  Release     1 #>  2 4842  I80_1       1 #>  3 4842  Lisbon      1 #>  4 4842  Rstr        1 #>  5 4842  Base_TD     1 #>  6 4842  BCE         1 #>  7 4842  BCW         1 #>  8 4842  BCE2        1 #>  9 4842  BCW2        1 #> 10 4842  MAE         1 #> # ℹ 104 more rows fish_encounters %>%   pivot_wider(names_from = station, values_from = seen) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1    NA    NA    NA    NA    NA #>  5 4847        1     1      1    NA      NA    NA    NA    NA    NA    NA #>  6 4848        1     1      1     1      NA    NA    NA    NA    NA    NA #>  7 4849        1     1     NA    NA      NA    NA    NA    NA    NA    NA #>  8 4850        1     1     NA     1       1     1     1    NA    NA    NA #>  9 4851        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 10 4854        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 11 4855        1     1      1     1       1    NA    NA    NA    NA    NA #> 12 4857        1     1      1     1       1     1     1     1     1    NA #> 13 4858        1     1      1     1       1     1     1     1     1     1 #> 14 4859        1     1      1     1       1    NA    NA    NA    NA    NA #> 15 4861        1     1      1     1       1     1     1     1     1     1 #> 16 4862        1     1      1     1       1     1     1     1     1    NA #> 17 4863        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 18 4864        1     1     NA    NA      NA    NA    NA    NA    NA    NA #> 19 4865        1     1      1    NA      NA    NA    NA    NA    NA    NA #> # ℹ 1 more variable: MAW <int> # Fill in missing values fish_encounters %>%   pivot_wider(names_from = station, values_from = seen, values_fill = 0) #> # A tibble: 19 × 12 #>    fish  Release I80_1 Lisbon  Rstr Base_TD   BCE   BCW  BCE2  BCW2   MAE #>    <fct>   <int> <int>  <int> <int>   <int> <int> <int> <int> <int> <int> #>  1 4842        1     1      1     1       1     1     1     1     1     1 #>  2 4843        1     1      1     1       1     1     1     1     1     1 #>  3 4844        1     1      1     1       1     1     1     1     1     1 #>  4 4845        1     1      1     1       1     0     0     0     0     0 #>  5 4847        1     1      1     0       0     0     0     0     0     0 #>  6 4848        1     1      1     1       0     0     0     0     0     0 #>  7 4849        1     1      0     0       0     0     0     0     0     0 #>  8 4850        1     1      0     1       1     1     1     0     0     0 #>  9 4851        1     1      0     0       0     0     0     0     0     0 #> 10 4854        1     1      0     0       0     0     0     0     0     0 #> 11 4855        1     1      1     1       1     0     0     0     0     0 #> 12 4857        1     1      1     1       1     1     1     1     1     0 #> 13 4858        1     1      1     1       1     1     1     1     1     1 #> 14 4859        1     1      1     1       1     0     0     0     0     0 #> 15 4861        1     1      1     1       1     1     1     1     1     1 #> 16 4862        1     1      1     1       1     1     1     1     1     0 #> 17 4863        1     1      0     0       0     0     0     0     0     0 #> 18 4864        1     1      0     0       0     0     0     0     0     0 #> 19 4865        1     1      1     0       0     0     0     0     0     0 #> # ℹ 1 more variable: MAW <int>  # Generate column names from multiple variables us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # You can control whether `names_from` values vary fastest or slowest # relative to the `values_from` column names using `names_vary`. us_rent_income %>%   pivot_wider(     names_from = variable,     values_from = c(estimate, moe),     names_vary = \"slowest\"   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income moe_income estimate_rent moe_rent #>    <chr> <chr>                     <dbl>      <dbl>         <dbl>    <dbl> #>  1 01    Alabama                   24476        136           747        3 #>  2 02    Alaska                    32940        508          1200       13 #>  3 04    Arizona                   27517        148           972        4 #>  4 05    Arkansas                  23789        165           709        5 #>  5 06    California                29454        109          1358        3 #>  6 08    Colorado                  32401        109          1125        5 #>  7 09    Connecticut               35326        195          1123        5 #>  8 10    Delaware                  31560        247          1076       10 #>  9 11    District of Co…           43198        681          1424       17 #> 10 12    Florida                   25952         70          1077        3 #> # ℹ 42 more rows  # When there are multiple `names_from` or `values_from`, you can use # use `names_sep` or `names_glue` to control the output variable names us_rent_income %>%   pivot_wider(     names_from = variable,     names_sep = \".\",     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate.income estimate.rent moe.income moe.rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows us_rent_income %>%   pivot_wider(     names_from = variable,     names_glue = \"{variable}_{.value}\",     values_from = c(estimate, moe)   ) #> # A tibble: 52 × 6 #>    GEOID NAME            income_estimate rent_estimate income_moe rent_moe #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # Can perform aggregation with `values_fn` warpbreaks <- as_tibble(warpbreaks[c(\"wool\", \"tension\", \"breaks\")]) warpbreaks #> # A tibble: 54 × 3 #>    wool  tension breaks #>    <fct> <fct>    <dbl> #>  1 A     L           26 #>  2 A     L           30 #>  3 A     L           54 #>  4 A     L           25 #>  5 A     L           70 #>  6 A     L           52 #>  7 A     L           51 #>  8 A     L           26 #>  9 A     L           67 #> 10 A     M           18 #> # ℹ 44 more rows warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = mean   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        44.6  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8  # Can pass an anonymous function to `values_fn` when you # need to supply additional arguments warpbreaks$breaks[1] <- NA warpbreaks %>%   pivot_wider(     names_from = wool,     values_from = breaks,     values_fn = ~ mean(.x, na.rm = TRUE)   ) #> # A tibble: 3 × 3 #>   tension     A     B #>   <fct>   <dbl> <dbl> #> 1 L        46.9  28.2 #> 2 M        24    28.8 #> 3 H        24.6  18.8"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":null,"dir":"Reference","previous_headings":"","what":"Pivot data from long to wide using a spec — pivot_wider_spec","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"low level interface pivoting, inspired cdata package, allows describe pivoting data frame.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"","code":"pivot_wider_spec(   data,   spec,   ...,   names_repair = \"check_unique\",   id_cols = NULL,   id_expand = FALSE,   values_fill = NULL,   values_fn = NULL,   unused_fn = NULL,   error_call = current_env() )  build_wider_spec(   data,   ...,   names_from = name,   values_from = value,   names_prefix = \"\",   names_sep = \"_\",   names_glue = NULL,   names_sort = FALSE,   names_vary = \"fastest\",   names_expand = FALSE,   error_call = current_env() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"data data frame pivot. spec specification data frame. useful complex pivots gives greater control metadata stored columns become column names result. Must data frame containing character .name .value columns. Additional columns spec named match columns long format dataset contain values corresponding columns pivoted wide format. special .seq variable used disambiguate rows internally; automatically removed pivoting. ... dots future extensions must empty. names_repair happens output invalid column names? default, \"check_unique\" error columns duplicated. Use \"minimal\" allow duplicates output, \"unique\" de-duplicated adding numeric suffixes. See vctrs::vec_as_names() options. id_cols <tidy-select> set columns uniquely identifies observation. Defaults columns data except columns specified spec$.value columns spec named .name .value. Typically used redundant variables, .e. variables whose values perfectly correlated existing variables. id_expand values id_cols columns expanded expand() pivoting? results rows, output contain complete expansion possible values id_cols. Implicit factor levels represented data become explicit. Additionally, row values corresponding expanded id_cols sorted. values_fill Optionally, (scalar) value specifies value filled missing. can named list want apply different fill values different value columns. values_fn Optionally, function applied value cell output. typically use combination id_cols names_from columns uniquely identify observation. can named list want apply different aggregations different values_from columns. unused_fn Optionally, function applied summarize values unused columns (.e. columns identified id_cols, names_from, values_from). default drops unused columns result. can named list want apply different aggregations different unused columns. id_cols must supplied unused_fn useful, since otherwise unspecified columns considered id_cols. similar grouping id_cols summarizing unused columns using unused_fn. error_call execution environment currently running function, e.g. caller_env(). function mentioned error messages source error. See call argument abort() information. names_from, values_from <tidy-select> pair arguments describing column (columns) get name output column (names_from), column (columns) get cell values (values_from). values_from contains multiple values, value added front output column. names_prefix String added start every variable name. particularly useful names_from numeric vector want create syntactic variable names. names_sep names_from values_from contains multiple variables, used join values together single string use column name. names_glue Instead names_sep names_prefix, can supply glue specification uses names_from columns (special .value) create custom column names. names_sort column names sorted? FALSE, default, column names ordered first appearance. names_vary names_from identifies column (columns) multiple unique values, multiple values_from columns provided, order resulting column names combined? \"fastest\" varies names_from values fastest, resulting column naming scheme form: value1_name1, value1_name2, value2_name1, value2_name2. default. \"slowest\" varies names_from values slowest, resulting column naming scheme form: value1_name1, value2_name1, value1_name2, value2_name2. names_expand values names_from columns expanded expand() pivoting? results columns, output contain column names corresponding complete expansion possible values names_from. Implicit factor levels represented data become explicit. Additionally, column names sorted, identical names_sort produce.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/pivot_wider_spec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pivot data from long to wide using a spec — pivot_wider_spec","text":"","code":"# See vignette(\"pivot\") for examples and explanation  us_rent_income #> # A tibble: 104 × 5 #>    GEOID NAME       variable estimate   moe #>    <chr> <chr>      <chr>       <dbl> <dbl> #>  1 01    Alabama    income      24476   136 #>  2 01    Alabama    rent          747     3 #>  3 02    Alaska     income      32940   508 #>  4 02    Alaska     rent         1200    13 #>  5 04    Arizona    income      27517   148 #>  6 04    Arizona    rent          972     4 #>  7 05    Arkansas   income      23789   165 #>  8 05    Arkansas   rent          709     5 #>  9 06    California income      29454   109 #> 10 06    California rent         1358     3 #> # ℹ 94 more rows spec1 <- us_rent_income %>%   build_wider_spec(names_from = variable, values_from = c(estimate, moe)) spec1 #> # A tibble: 4 × 3 #>   .name           .value   variable #>   <chr>           <chr>    <chr>    #> 1 estimate_income estimate income   #> 2 estimate_rent   estimate rent     #> 3 moe_income      moe      income   #> 4 moe_rent        moe      rent      us_rent_income %>%   pivot_wider_spec(spec1) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # Is equivalent to us_rent_income %>%   pivot_wider(names_from = variable, values_from = c(estimate, moe)) #> # A tibble: 52 × 6 #>    GEOID NAME            estimate_income estimate_rent moe_income moe_rent #>    <chr> <chr>                     <dbl>         <dbl>      <dbl>    <dbl> #>  1 01    Alabama                   24476           747        136        3 #>  2 02    Alaska                    32940          1200        508       13 #>  3 04    Arizona                   27517           972        148        4 #>  4 05    Arkansas                  23789           709        165        5 #>  5 06    California                29454          1358        109        3 #>  6 08    Colorado                  32401          1125        109        5 #>  7 09    Connecticut               35326          1123        195        5 #>  8 10    Delaware                  31560          1076        247       10 #>  9 11    District of Co…           43198          1424        681       17 #> 10 12    Florida                   25952          1077         70        3 #> # ℹ 42 more rows  # `pivot_wider_spec()` provides more control over column names and output format # instead of creating columns with estimate_ and moe_ prefixes, # keep original variable name for estimates and attach _moe as suffix spec2 <- tibble(   .name = c(\"income\", \"rent\", \"income_moe\", \"rent_moe\"),   .value = c(\"estimate\", \"estimate\", \"moe\", \"moe\"),   variable = c(\"income\", \"rent\", \"income\", \"rent\") )  us_rent_income %>%   pivot_wider_spec(spec2) #> # A tibble: 52 × 6 #>    GEOID NAME                 income  rent income_moe rent_moe #>    <chr> <chr>                 <dbl> <dbl>      <dbl>    <dbl> #>  1 01    Alabama               24476   747        136        3 #>  2 02    Alaska                32940  1200        508       13 #>  3 04    Arizona               27517   972        148        4 #>  4 05    Arkansas              23789   709        165        5 #>  5 06    California            29454  1358        109        3 #>  6 08    Colorado              32401  1125        109        5 #>  7 09    Connecticut           35326  1123        195        5 #>  8 10    Delaware              31560  1076        247       10 #>  9 11    District of Columbia  43198  1424        681       17 #> 10 12    Florida               25952  1077         70        3 #> # ℹ 42 more rows"},{"path":"https://tidyr.tidyverse.org/dev/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. tibble as_tibble, tibble, tribble tidyselect all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, one_of, starts_with","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":null,"dir":"Reference","previous_headings":"","what":"Pew religion and income survey — relig_income","title":"Pew religion and income survey — relig_income","text":"Pew religion income survey","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pew religion and income survey — relig_income","text":"","code":"relig_income"},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pew religion and income survey — relig_income","text":"dataset variables: religion Name religion <$10k-Don\\'t know/refused Number respondees income range column name","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/relig_income.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Pew religion and income survey — relig_income","text":"Downloaded https://www.pewresearch.org/religious-landscape-study/database/ (downloaded November 2009)","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace NAs with specified values — replace_na","title":"Replace NAs with specified values — replace_na","text":"Replace NAs specified values","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace NAs with specified values — replace_na","text":"","code":"replace_na(data, replace, ...)"},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace NAs with specified values — replace_na","text":"data data frame vector. replace data data frame, replace takes named list values, one value column missing values replaced. value replace cast type column data used replacement . data vector, replace takes single value. single value replaces missing values vector. replace cast type data. ... Additional arguments methods. Currently unused.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace NAs with specified values — replace_na","text":"replace_na() returns object type data.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/replace_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Replace NAs with specified values — replace_na","text":"","code":"# Replace NAs in a data frame df <- tibble(x = c(1, 2, NA), y = c(\"a\", NA, \"b\")) df %>% replace_na(list(x = 0, y = \"unknown\")) #> # A tibble: 3 × 2 #>       x y       #>   <dbl> <chr>   #> 1     1 a       #> 2     2 unknown #> 3     0 b        # Replace NAs in a vector df %>% dplyr::mutate(x = replace_na(x, 0)) #> # A tibble: 3 × 2 #>       x y     #>   <dbl> <chr> #> 1     1 a     #> 2     2 NA    #> 3     0 b     # OR df$x %>% replace_na(0) #> [1] 1 2 0 df$y %>% replace_na(\"unknown\") #> [1] \"a\"       \"unknown\" \"b\"        # Replace NULLs in a list: NULLs are the list-col equivalent of NAs df_list <- tibble(z = list(1:5, NULL, 10:20)) df_list %>% replace_na(list(z = list(5))) #> # A tibble: 3 × 1 #>   z          #>   <list>     #> 1 <int [5]>  #> 2 <dbl [1]>  #> 3 <int [11]>"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"separate() superseded favour separate_wider_position() separate_wider_delim() two functions make two uses obvious, API polished, handling problems better. Superseded functions go away, receive critical bug fixes. Given either regular expression vector character positions, separate() turns single character column multiple columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"","code":"separate(   data,   col,   into,   sep = \"[^[:alnum:]]+\",   remove = TRUE,   convert = FALSE,   extra = \"warn\",   fill = \"warn\",   ... )"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"data data frame. col <tidy-select> Column expand. Names new variables create character vector. Use NA omit variable output. sep Separator columns. character, sep interpreted regular expression. default value regular expression matches sequence non-alphanumeric values. numeric, sep interpreted character positions split . Positive values start 1 far-left string; negative value start -1 far-right string. length sep one less . remove TRUE, remove input column output data frame. convert TRUE, run type.convert() .= TRUE new columns. useful component columns integer, numeric logical. NB: cause string \"NA\"s converted NAs. extra sep character vector, controls happens many pieces. three valid options: \"warn\" (default): emit warning drop extra values. \"drop\": drop extra values without warning. \"merge\": splits length() times fill sep character vector, controls happens enough pieces. three valid options: \"warn\" (default): emit warning fill right \"right\": fill missing values right \"left\": fill missing values left ... Additional arguments passed methods.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/separate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate a character column into multiple columns with a regular expression or numeric locations — separate","text":"","code":"# If you want to split by any non-alphanumeric value (the default): df <- tibble(x = c(NA, \"x.y\", \"x.z\", \"y.z\")) df %>% separate(x, c(\"A\", \"B\")) #> # A tibble: 4 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 x     y     #> 3 x     z     #> 4 y     z      # If you just want the second variable: df %>% separate(x, c(NA, \"B\")) #> # A tibble: 4 × 1 #>   B     #>   <chr> #> 1 NA    #> 2 y     #> 3 z     #> 4 z      # We now recommend separate_wider_delim() instead: df %>% separate_wider_delim(x, \".\", names = c(\"A\", \"B\")) #> # A tibble: 4 × 2 #>   A     B     #>   <chr> <chr> #> 1 NA    NA    #> 2 x     y     #> 3 x     z     #> 4 y     z     df %>% separate_wider_delim(x, \".\", names = c(NA, \"B\")) #> # A tibble: 4 × 1 #>   B     #>   <chr> #> 1 NA    #> 2 y     #> 3 z     #> 4 z      # Controlling uneven splits ------------------------------------------------- # If every row doesn't split into the same number of pieces, use # the extra and fill arguments to control what happens: df <- tibble(x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate(x, c(\"a\", \"b\")) #> Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [3]. #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [1]. #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 x     NA    #> 2 x     y     #> 3 x     y     #> 4 NA    NA    # The same behaviour as previous, but drops the c without warnings: df %>% separate(x, c(\"a\", \"b\"), extra = \"drop\", fill = \"right\") #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 x     NA    #> 2 x     y     #> 3 x     y     #> 4 NA    NA    # Opposite of previous, keeping the c and filling left: df %>% separate(x, c(\"a\", \"b\"), extra = \"merge\", fill = \"left\") #> # A tibble: 4 × 2 #>   a     b     #>   <chr> <chr> #> 1 NA    x     #> 2 x     y     #> 3 x     y z   #> 4 NA    NA    # Or you can keep all three: df %>% separate(x, c(\"a\", \"b\", \"c\")) #> Warning: Expected 3 pieces. Missing pieces filled with `NA` in 2 rows [1, 2]. #> # A tibble: 4 × 3 #>   a     b     c     #>   <chr> <chr> <chr> #> 1 x     NA    NA    #> 2 x     y     NA    #> 3 x     y     z     #> 4 NA    NA    NA     # To only split a specified number of times use extra = \"merge\": df <- tibble(x = c(\"x: 123\", \"y: error: 7\")) df %>% separate(x, c(\"key\", \"value\"), \": \", extra = \"merge\") #> # A tibble: 2 × 2 #>   key   value    #>   <chr> <chr>    #> 1 x     123      #> 2 y     error: 7  # Controlling column types -------------------------------------------------- # convert = TRUE detects column classes: df <- tibble(x = c(\"x:1\", \"x:2\", \"y:4\", \"z\", NA)) df %>% separate(x, c(\"key\", \"value\"), \":\") %>% str() #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4]. #> tibble [5 × 2] (S3: tbl_df/tbl/data.frame) #>  $ key  : chr [1:5] \"x\" \"x\" \"y\" \"z\" ... #>  $ value: chr [1:5] \"1\" \"2\" \"4\" NA ... df %>% separate(x, c(\"key\", \"value\"), \":\", convert = TRUE) %>% str() #> Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1 rows [4]. #> tibble [5 × 2] (S3: tbl_df/tbl/data.frame) #>  $ key  : chr [1:5] \"x\" \"x\" \"y\" \"z\" ... #>  $ value: int [1:5] 1 2 4 NA NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a string into rows — separate_longer_delim","title":"Split a string into rows — separate_longer_delim","text":"functions takes string splits multiple rows: separate_longer_delim() splits delimiter. separate_longer_position() splits fixed width.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a string into rows — separate_longer_delim","text":"","code":"separate_longer_delim(data, cols, delim, ...)  separate_longer_position(data, cols, width, ..., keep_empty = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a string into rows — separate_longer_delim","text":"data data frame. cols <tidy-select> Columns separate. delim separate_longer_delim(), string giving delimiter values. default, interpreted fixed string; use stringr::regex() friends split ways. ... dots future extensions must empty. width separate_longer_position(), integer giving number characters split . keep_empty default, get ceiling(nchar(x) / width) rows observation. nchar(x) zero, means entire input row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements missing value.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a string into rows — separate_longer_delim","text":"data frame based data. columns, different rows.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_longer_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a string into rows — separate_longer_delim","text":"","code":"df <- tibble(id = 1:4, x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate_longer_delim(x, delim = \" \") #> # A tibble: 7 × 2 #>      id x     #>   <int> <chr> #> 1     1 x     #> 2     2 x     #> 3     2 y     #> 4     3 x     #> 5     3 y     #> 6     3 z     #> 7     4 NA     # You can separate multiple columns at once if they have the same structure df <- tibble(id = 1:3, x = c(\"x\", \"x y\", \"x y z\"), y = c(\"a\", \"a b\", \"a b c\")) df %>% separate_longer_delim(c(x, y), delim = \" \") #> # A tibble: 6 × 3 #>      id x     y     #>   <int> <chr> <chr> #> 1     1 x     a     #> 2     2 x     a     #> 3     2 y     b     #> 4     3 x     a     #> 5     3 y     b     #> 6     3 z     c      # Or instead split by a fixed length df <- tibble(id = 1:3, x = c(\"ab\", \"def\", \"\")) df %>% separate_longer_position(x, 1) #> # A tibble: 5 × 2 #>      id x     #>   <int> <chr> #> 1     1 a     #> 2     1 b     #> 3     2 d     #> 4     2 e     #> 5     2 f     df %>% separate_longer_position(x, 2) #> # A tibble: 3 × 2 #>      id x     #>   <int> <chr> #> 1     1 ab    #> 2     2 de    #> 3     2 f     df %>% separate_longer_position(x, 2, keep_empty = TRUE) #> # A tibble: 4 × 2 #>      id x     #>   <int> <chr> #> 1     1 ab    #> 2     2 de    #> 3     2 f     #> 4     3 NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Separate a collapsed column into multiple rows — separate_rows","title":"Separate a collapsed column into multiple rows — separate_rows","text":"separate_rows() superseded favour separate_longer_delim() consistent API separate functions. Superseded functions go away, receive critical bug fixes. variable contains observations multiple delimited values, separate_rows() separates values places one row.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Separate a collapsed column into multiple rows — separate_rows","text":"","code":"separate_rows(data, ..., sep = \"[^[:alnum:].]+\", convert = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Separate a collapsed column into multiple rows — separate_rows","text":"data data frame. ... <tidy-select> Columns separate across multiple rows sep Separator delimiting collapsed values. convert TRUE automatically run type.convert() key column. useful column types actually numeric, integer, logical.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Separate a collapsed column into multiple rows — separate_rows","text":"","code":"df <- tibble(   x = 1:3,   y = c(\"a\", \"d,e,f\", \"g,h\"),   z = c(\"1\", \"2,3,4\", \"5,6\") ) separate_rows(df, y, z, convert = TRUE) #> # A tibble: 6 × 3 #>       x y         z #>   <int> <chr> <int> #> 1     1 a         1 #> 2     2 d         2 #> 3     2 e         3 #> 4     2 f         4 #> 5     3 g         5 #> 6     3 h         6  # Now recommended df %>%   separate_longer_delim(c(y, z), delim = \",\") #> # A tibble: 6 × 3 #>       x y     z     #>   <int> <chr> <chr> #> 1     1 a     1     #> 2     2 d     2     #> 3     2 e     3     #> 4     2 f     4     #> 5     3 g     5     #> 6     3 h     6"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a string into columns — separate_wider_delim","title":"Split a string into columns — separate_wider_delim","text":"functions takes string column splits multiple new columns: separate_wider_delim() splits delimiter. separate_wider_position() splits fixed widths. separate_wider_regex() splits regular expression matches. functions equivalent separate() extract(), use stringr underlying string manipulation engine, interfaces reflect learned unnest_wider() unnest_longer().","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a string into columns — separate_wider_delim","text":"","code":"separate_wider_delim(   data,   cols,   delim,   ...,   names = NULL,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\", \"align_end\"),   too_many = c(\"error\", \"debug\", \"drop\", \"merge\"),   cols_remove = TRUE )  separate_wider_position(   data,   cols,   widths,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\"),   too_many = c(\"error\", \"debug\", \"drop\"),   cols_remove = TRUE )  separate_wider_regex(   data,   cols,   patterns,   ...,   names_sep = NULL,   names_repair = \"check_unique\",   too_few = c(\"error\", \"debug\", \"align_start\"),   cols_remove = TRUE )"},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a string into columns — separate_wider_delim","text":"data data frame. cols <tidy-select> Columns separate. delim separate_wider_delim(), string giving delimiter values. default, interpreted fixed string; use stringr::regex() friends split ways. ... dots future extensions must empty. names separate_wider_delim(), character vector output column names. Use NA components want appear output; number non-NA elements determines number new columns result. names_sep supplied, output names composed input column name followed separator followed new column name. Required cols selects multiple columns. separate_wider_delim() can specify instead names, case names generated source column name, names_sep, numeric suffix. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . too_few happen value separates pieces? \"error\", default, throw error. \"debug\" adds additional columns output help locate resolve underlying problem. option intended help debug issue address generally remain final code. \"align_start\" aligns starts short matches, adding NA end pad correct length. \"align_end\" (separate_wider_delim() ) aligns ends short matches, adding NA start pad correct length. too_many happen value separates many pieces? \"error\", default, throw error. \"debug\" add additional columns output help locate resolve underlying problem. \"drop\" silently drop extra pieces. \"merge\" (separate_wider_delim() ) merge together additional pieces. cols_remove input cols removed output? Always FALSE too_few too_many set \"debug\". widths named numeric vector names become column names, values specify column width. Unnamed components match, included output. patterns named character vector names become column names values regular expressions match contents vector. Unnamed components match, included output.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split a string into columns — separate_wider_delim","text":"data frame based data. rows, different columns: primary purpose functions create new columns components string. separate_wider_delim() names new columns come names. separate_wider_position() names come names widths. separate_wider_regex() names come names patterns. too_few too_many \"debug\", output contain additional columns useful debugging: {col}_ok: logical vector tells input ok . Use quickly find problematic rows. {col}_remainder: text remaining separation. {col}_pieces, {col}_width, {col}_matches: number pieces, number characters, number matches separate_wider_delim(), separate_wider_position() separate_regexp_wider() respectively. cols_remove = TRUE (default), input cols removed output.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/separate_wider_delim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split a string into columns — separate_wider_delim","text":"","code":"df <- tibble(id = 1:3, x = c(\"m-123\", \"f-455\", \"f-123\")) # There are three basic ways to split up a string into pieces: # 1. with a delimiter df %>% separate_wider_delim(x, delim = \"-\", names = c(\"gender\", \"unit\")) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123   # 2. by length df %>% separate_wider_position(x, c(gender = 1, 1, unit = 3)) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123   # 3. defining each component with a regular expression df %>% separate_wider_regex(x, c(gender = \".\", \".\", unit = \"\\\\d+\")) #> # A tibble: 3 × 3 #>      id gender unit  #>   <int> <chr>  <chr> #> 1     1 m      123   #> 2     2 f      455   #> 3     3 f      123    # Sometimes you split on the \"last\" delimiter df <- tibble(var = c(\"race_1\", \"race_2\", \"age_bucket_1\", \"age_bucket_2\")) # _delim won't help because it always splits on the first delimiter try(df %>% separate_wider_delim(var, \"_\", names = c(\"var1\", \"var2\"))) #> Error in separate_wider_delim(., var, \"_\", names = c(\"var1\", \"var2\")) :  #>   Expected 2 pieces in each element of `var`. #> ! 2 values were too long. #> ℹ Use `too_many = \"debug\"` to diagnose the problem. #> ℹ Use `too_many = \"drop\"/\"merge\"` to silence this message. df %>% separate_wider_delim(var, \"_\", names = c(\"var1\", \"var2\"), too_many = \"merge\") #> # A tibble: 4 × 2 #>   var1  var2     #>   <chr> <chr>    #> 1 race  1        #> 2 race  2        #> 3 age   bucket_1 #> 4 age   bucket_2 # Instead, you can use _regex df %>% separate_wider_regex(var, c(var1 = \".*\", \"_\", var2 = \".*\")) #> # A tibble: 4 × 2 #>   var1       var2  #>   <chr>      <chr> #> 1 race       1     #> 2 race       2     #> 3 age_bucket 1     #> 4 age_bucket 2     # this works because * is greedy; you can mimic the _delim behaviour with .*? df %>% separate_wider_regex(var, c(var1 = \".*?\", \"_\", var2 = \".*\")) #> # A tibble: 4 × 2 #>   var1  var2     #>   <chr> <chr>    #> 1 race  1        #> 2 race  2        #> 3 age   bucket_1 #> 4 age   bucket_2  # If the number of components varies, it's most natural to split into rows df <- tibble(id = 1:4, x = c(\"x\", \"x y\", \"x y z\", NA)) df %>% separate_longer_delim(x, delim = \" \") #> # A tibble: 7 × 2 #>      id x     #>   <int> <chr> #> 1     1 x     #> 2     2 x     #> 3     2 y     #> 4     3 x     #> 5     3 y     #> 6     3 z     #> 7     4 NA    # But separate_wider_delim() provides some tools to deal with the problem # The default behaviour tells you that there's a problem try(df %>% separate_wider_delim(x, delim = \" \", names = c(\"a\", \"b\"))) #> Error in separate_wider_delim(., x, delim = \" \", names = c(\"a\", \"b\")) :  #>   Expected 2 pieces in each element of `x`. #> ! 1 value was too short. #> ℹ Use `too_few = \"debug\"` to diagnose the problem. #> ℹ Use `too_few = \"align_start\"/\"align_end\"` to silence this message. #> ! 1 value was too long. #> ℹ Use `too_many = \"debug\"` to diagnose the problem. #> ℹ Use `too_many = \"drop\"/\"merge\"` to silence this message. # You can get additional insight by using the debug options df %>%   separate_wider_delim(     x,     delim = \" \",     names = c(\"a\", \"b\"),     too_few = \"debug\",     too_many = \"debug\"   ) #> Warning: Debug mode activated: adding variables `x_ok`, `x_pieces`, and #> `x_remainder`. #> # A tibble: 4 × 7 #>      id a     b     x     x_ok  x_pieces x_remainder #>   <int> <chr> <chr> <chr> <lgl>    <int> <chr>       #> 1     1 x     NA    x     FALSE        1 \"\"          #> 2     2 x     y     x y   TRUE         2 \"\"          #> 3     3 x     y     x y z FALSE        3 \" z\"        #> 4     4 NA    NA    NA    TRUE        NA  NA          # But you can suppress the warnings df %>%   separate_wider_delim(     x,     delim = \" \",     names = c(\"a\", \"b\"),     too_few = \"align_start\",     too_many = \"merge\"   ) #> # A tibble: 4 × 3 #>      id a     b     #>   <int> <chr> <chr> #> 1     1 x     NA    #> 2     2 x     y     #> 3     3 x     y z   #> 4     4 NA    NA     # Or choose to automatically name the columns, producing as many as needed df %>% separate_wider_delim(x, delim = \" \", names_sep = \"\", too_few = \"align_start\") #> # A tibble: 4 × 4 #>      id x1    x2    x3    #>   <int> <chr> <chr> <chr> #> 1     1 x     NA    NA    #> 2     2 x     y     NA    #> 3     3 x     y     z     #> 4     4 NA    NA    NA"},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":null,"dir":"Reference","previous_headings":"","what":"Some data about the Smith family — smiths","title":"Some data about the Smith family — smiths","text":"small demo dataset describing John Mary Smith.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Some data about the Smith family — smiths","text":"","code":"smiths"},{"path":"https://tidyr.tidyverse.org/dev/reference/smiths.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Some data about the Smith family — smiths","text":"data frame 2 rows 5 columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":null,"dir":"Reference","previous_headings":"","what":"Spread a key-value pair across multiple columns — spread","title":"Spread a key-value pair across multiple columns — spread","text":"Development spread() complete, new code recommend switching pivot_wider(), easier use, featureful, still active development. df %>% spread(key, value) equivalent df %>% pivot_wider(names_from = key, values_from = value) See details vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spread a key-value pair across multiple columns — spread","text":"","code":"spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spread a key-value pair across multiple columns — spread","text":"data data frame. key, value <tidy-select> Columns use key value. fill set, missing values replaced value. Note two types missingness input: explicit missing values (.e. NA), implicit missings, rows simply present. types missing value replaced fill. convert TRUE, type.convert() asis =   TRUE run new columns. useful value column mix variables coerced string. class value column factor date, note true new columns produced, coerced character type conversion. drop FALSE, keep factor levels appear data, filling missing combinations fill. sep NULL, column names taken values key variable. non-NULL, column names given \"<key_name><sep><key_value>\".","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/spread.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spread a key-value pair across multiple columns — spread","text":"","code":"stocks <- tibble(   time = as.Date(\"2009-01-01\") + 0:9,   X = rnorm(10, 0, 1),   Y = rnorm(10, 0, 2),   Z = rnorm(10, 0, 4) ) stocksm <- stocks %>% gather(stock, price, -time) stocksm %>% spread(stock, price) #> # A tibble: 10 × 4 #>    time            X      Y        Z #>    <date>      <dbl>  <dbl>    <dbl> #>  1 2009-01-01 -2.05  -1.40   0.00192 #>  2 2009-01-02  0.151  1.95   3.02    #>  3 2009-01-03 -0.293 -0.154  1.37    #>  4 2009-01-04  0.255  1.79   0.674   #>  5 2009-01-05 -0.553 -1.56   5.59    #>  6 2009-01-06  1.41   0.874 -2.72    #>  7 2009-01-07 -0.795  0.827  2.95    #>  8 2009-01-08 -1.57   1.95  -3.44    #>  9 2009-01-09 -1.04   2.29   1.68    #> 10 2009-01-10  1.02   2.43   5.80    stocksm %>% spread(time, price) #> # A tibble: 3 × 11 #>   stock `2009-01-01` `2009-01-02` `2009-01-03` `2009-01-04` `2009-01-05` #>   <chr>        <dbl>        <dbl>        <dbl>        <dbl>        <dbl> #> 1 X         -2.05           0.151       -0.293        0.255       -0.553 #> 2 Y         -1.40           1.95        -0.154        1.79        -1.56  #> 3 Z          0.00192        3.02         1.37         0.674        5.59  #> # ℹ 5 more variables: `2009-01-06` <dbl>, `2009-01-07` <dbl>, #> #   `2009-01-08` <dbl>, `2009-01-09` <dbl>, `2009-01-10` <dbl>  # Spread and gather are complements df <- tibble(x = c(\"a\", \"b\"), y = c(3, 4), z = c(5, 6)) df %>%   spread(x, y) %>%   gather(\"x\", \"y\", a:b, na.rm = TRUE) #> # A tibble: 2 × 3 #>       z x         y #>   <dbl> <chr> <dbl> #> 1     5 a         3 #> 2     6 b         4  # Use 'convert = TRUE' to produce variables of mixed type df <- tibble(   row = rep(c(1, 51), each = 3),   var = rep(c(\"Sepal.Length\", \"Species\", \"Species_num\"), 2),   value = c(5.1, \"setosa\", 1, 7.0, \"versicolor\", 2) ) df %>% spread(var, value) %>% str() #> tibble [2 × 4] (S3: tbl_df/tbl/data.frame) #>  $ row         : num [1:2] 1 51 #>  $ Sepal.Length: chr [1:2] \"5.1\" \"7\" #>  $ Species     : chr [1:2] \"setosa\" \"versicolor\" #>  $ Species_num : chr [1:2] \"1\" \"2\" df %>% spread(var, value, convert = TRUE) %>% str() #> tibble [2 × 4] (S3: tbl_df/tbl/data.frame) #>  $ row         : num [1:2] 1 51 #>  $ Sepal.Length: num [1:2] 5.1 7 #>  $ Species     : chr [1:2] \"setosa\" \"versicolor\" #>  $ Species_num : int [1:2] 1 2"},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":null,"dir":"Reference","previous_headings":"","what":"Example tabular representations — table1","title":"Example tabular representations — table1","text":"Data sets demonstrate multiple ways layout tabular data.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example tabular representations — table1","text":"","code":"table1  table2  table3  table4a  table4b  table5"},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example tabular representations — table1","text":"https://www..int/teams/global-tuberculosis-programme/data","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/table1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Example tabular representations — table1","text":"table1, table2, table3, table4a, table4b, table5 display number TB cases documented World Health Organization Afghanistan, Brazil, China 1999 2000. data contains values associated four variables (country, year, cases, population), table organizes values different layout. data subset data contained World Health Organization Global Tuberculosis Report","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tidyr: Tidy Messy Data — tidyr-package","title":"tidyr: Tidy Messy Data — tidyr-package","text":"Tools help create tidy data, column variable, row observation, cell contains single value. 'tidyr' contains tools changing shape (pivoting) hierarchy (nesting 'unnesting') dataset, turning deeply nested lists rectangular data frames ('rectangling'), extracting values string columns. also includes tools working missing values (implicit explicit).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tidyr: Tidy Messy Data — tidyr-package","text":"Maintainer: Hadley Wickham hadley@posit.co Authors: Davis Vaughan davis@posit.co Maximilian Girlich contributors: Kevin Ushey kevin@posit.co [contributor] Posit Software, PBC [copyright holder, funder]","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":null,"dir":"Reference","previous_headings":"","what":"Argument type: data-masking — tidyr_data_masking","title":"Argument type: data-masking — tidyr_data_masking","text":"page describes <data-masking> argument modifier indicates argument uses data masking, sub-type tidy evaluation. never heard tidy evaluation , start practical introduction https://r4ds.hadley.nz/functions.html#data-frame-functions read underlying theory https://rlang.r-lib.org/reference/topic-data-mask.html.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":"key-techniques","dir":"Reference","previous_headings":"","what":"Key techniques","title":"Argument type: data-masking — tidyr_data_masking","text":"allow user supply column name function argument, embrace argument, e.g. filter(df, {{ var }}).   work column name recorded string, use .data pronoun, e.g. summarise(df, mean = mean(.data[[var]])).   suppress R CMD check NOTEs unknown variables use .data$var instead var:   also need import .data rlang (e.g.) @importFrom rlang .data.","code":"dist_summary <- function(df, var) {   df %>%     summarise(n = n(), min = min({{ var }}), max = max({{ var }})) } mtcars %>% dist_summary(mpg) mtcars %>% group_by(cyl) %>% dist_summary(mpg) for (var in names(mtcars)) {   mtcars %>% count(.data[[var]]) %>% print() }  lapply(names(mtcars), function(var) mtcars %>% count(.data[[var]])) # has NOTE df %>% mutate(z = x + y)  # no NOTE df %>% mutate(z = .data$x + .data$y)"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_data_masking.html","id":"dot-dot-dot-","dir":"Reference","previous_headings":"","what":"Dot-dot-dot (...)","title":"Argument type: data-masking — tidyr_data_masking","text":"... automatically provides indirection, can use (.e. without embracing) inside function:   can also use := instead = enable glue-like syntax creating variables user supplied data:   Learn https://rlang.r-lib.org/reference/topic-data-mask-programming.html.","code":"grouped_mean <- function(df, var, ...) {   df %>%     group_by(...) %>%     summarise(mean = mean({{ var }})) } var_name <- \"l100km\" mtcars %>% mutate(\"{var_name}\" := 235 / mpg)  summarise_mean <- function(df, var) {   df %>%     summarise(\"mean_of_{{var}}\" := mean({{ var }})) } mtcars %>% group_by(cyl) %>% summarise_mean(mpg)"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy name repair — tidyr_legacy","title":"Legacy name repair — tidyr_legacy","text":"Ensures column names unique using approach found tidyr 0.8.3 earlier. use function want preserve naming strategy, otherwise better adopting new tidyverse standard name_repair = \"universal\"","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy name repair — tidyr_legacy","text":"","code":"tidyr_legacy(nms, prefix = \"V\", sep = \"\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy name repair — tidyr_legacy","text":"nms Character vector names prefix prefix Prefix use unnamed column sep Separator use name unique suffix","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_legacy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy name repair — tidyr_legacy","text":"","code":"df <- tibble(x = 1:2, y = list(tibble(x = 3:5), tibble(x = 4:7)))  # Doesn't work because it would produce a data frame with two # columns called x if (FALSE) { # \\dontrun{ unnest(df, y) } # }  # The new tidyverse standard: unnest(df, y, names_repair = \"universal\") #> New names: #> • `x` -> `x...1` #> • `x` -> `x...2` #> # A tibble: 7 × 2 #>   x...1 x...2 #>   <int> <int> #> 1     1     3 #> 2     1     4 #> 3     1     5 #> 4     2     4 #> 5     2     5 #> 6     2     6 #> 7     2     7  # The old tidyr approach unnest(df, y, names_repair = tidyr_legacy) #> # A tibble: 7 × 2 #>       x    x1 #>   <int> <int> #> 1     1     3 #> 2     1     4 #> 3     1     5 #> 4     2     4 #> 5     2     5 #> 6     2     6 #> 7     2     7"},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":null,"dir":"Reference","previous_headings":"","what":"Argument type: tidy-select — tidyr_tidy_select","title":"Argument type: tidy-select — tidyr_tidy_select","text":"page describes <tidy-select> argument modifier indicates argument uses tidy selection, sub-type tidy evaluation. never heard tidy evaluation , start practical introduction https://r4ds.hadley.nz/functions.html#data-frame-functions read underlying theory https://rlang.r-lib.org/reference/topic-data-mask.html.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":"overview-of-selection-features","dir":"Reference","previous_headings":"","what":"Overview of selection features","title":"Argument type: tidy-select — tidyr_tidy_select","text":"tidyselect implements DSL selecting variables. provides helpers selecting variables: var1:var10: variables lying var1 left var10 right. starts_with(\"\"): names start \"\". ends_with(\"z\"): names end \"z\". contains(\"b\"): names contain \"b\". matches(\"x.y\"): names match regular expression x.y. num_range(x, 1:4): names following pattern, x1, x2, ..., x4. all_of(vars)/any_of(vars): matches names stored character vector vars. all_of(vars) error variables present; any_of(var) match just variables exist. everything(): variables. last_col(): furthest column right. (.numeric): variables .numeric() returns TRUE. well operators combining selections: !selection: variables match selection. selection1 & selection2: variables included selection1 selection2. selection1 | selection2: variables match either selection1 selection2.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/tidyr_tidy_select.html","id":"key-techniques","dir":"Reference","previous_headings":"","what":"Key techniques","title":"Argument type: tidy-select — tidyr_tidy_select","text":"want user supply tidyselect specification function argument, need tunnel selection function argument. done embracing function argument {{ }}, e.g unnest(df, {{ vars }}). character vector column names, use all_of() any_of(), depending whether want unknown variable names cause error, e.g unnest(df, all_of(vars)), unnest(df, !any_of(vars)). suppress R CMD check NOTEs unknown variables use \"var\" instead var:","code":"# has NOTE df %>% select(x, y, z)  # no NOTE df %>% select(\"x\", \"y\", \"z\")"},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":null,"dir":"Reference","previous_headings":"","what":"","title":"","text":"Performs opposite operation dplyr::count(), duplicating rows according weighting variable (expression).","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"","text":"","code":"uncount(data, weights, ..., .remove = TRUE, .id = NULL)"},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"","text":"data data frame, tibble, grouped tibble. weights vector weights. Evaluated context data; supports quasiquotation. ... Additional arguments passed methods. .remove TRUE, weights name column data, column removed. .id Supply string create new variable gives unique identifier created row.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/uncount.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"","text":"","code":"df <- tibble(x = c(\"a\", \"b\"), n = c(1, 2)) uncount(df, n) #> # A tibble: 3 × 1 #>   x     #>   <chr> #> 1 a     #> 2 b     #> 3 b     uncount(df, n, .id = \"id\") #> # A tibble: 3 × 2 #>   x        id #>   <chr> <int> #> 1 a         1 #> 2 b         1 #> 3 b         2  # You can also use constants uncount(df, 2) #> # A tibble: 4 × 2 #>   x         n #>   <chr> <dbl> #> 1 a         1 #> 2 a         1 #> 3 b         2 #> 4 b         2  # Or expressions uncount(df, 2 / n) #> # A tibble: 3 × 2 #>   x         n #>   <chr> <dbl> #> 1 a         1 #> 2 a         1 #> 3 b         2"},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":null,"dir":"Reference","previous_headings":"","what":"Unite multiple columns into one by pasting strings together — unite","title":"Unite multiple columns into one by pasting strings together — unite","text":"Convenience function paste together multiple columns one.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unite multiple columns into one by pasting strings together — unite","text":"","code":"unite(data, col, ..., sep = \"_\", remove = TRUE, na.rm = FALSE)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unite multiple columns into one by pasting strings together — unite","text":"data data frame. col name new column, string symbol. argument passed expression supports quasiquotation (can unquote strings symbols). name captured expression rlang::ensym() (note kind interface symbols represent actual objects now discouraged tidyverse; support backward compatibility). ... <tidy-select> Columns unite sep Separator use values. remove TRUE, remove input columns output data frame. na.rm TRUE, missing values removed prior uniting value.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unite.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unite multiple columns into one by pasting strings together — unite","text":"","code":"df <- expand_grid(x = c(\"a\", NA), y = c(\"b\", NA)) df #> # A tibble: 4 × 2 #>   x     y     #>   <chr> <chr> #> 1 a     b     #> 2 a     NA    #> 3 NA    b     #> 4 NA    NA     df %>% unite(\"z\", x:y, remove = FALSE) #> # A tibble: 4 × 3 #>   z     x     y     #>   <chr> <chr> <chr> #> 1 a_b   a     b     #> 2 a_NA  a     NA    #> 3 NA_b  NA    b     #> 4 NA_NA NA    NA    # To remove missing values: df %>% unite(\"z\", x:y, na.rm = TRUE, remove = FALSE) #> # A tibble: 4 × 3 #>   z     x     y     #>   <chr> <chr> <chr> #> 1 \"a_b\" a     b     #> 2 \"a\"   a     NA    #> 3 \"b\"   NA    b     #> 4 \"\"    NA    NA     # Separate is almost the complement of unite df %>%   unite(\"xy\", x:y) %>%   separate(xy, c(\"x\", \"y\")) #> # A tibble: 4 × 2 #>   x     y     #>   <chr> <chr> #> 1 a     b     #> 2 a     NA    #> 3 NA    b     #> 4 NA    NA    # (but note `x` and `y` contain now \"NA\" not NA)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column of data frames into rows and columns — unnest","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"Unnest expands list-column containing data frames rows columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"","code":"unnest(   data,   cols,   ...,   keep_empty = FALSE,   ptype = NULL,   names_sep = NULL,   names_repair = \"check_unique\",   .drop = deprecated(),   .id = deprecated(),   .sep = deprecated(),   .preserve = deprecated() )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"data data frame. cols <tidy-select> List-columns unnest. selecting multiple columns, values row recycled common size. ... : previously write df %>% unnest(x, y, z). Convert df %>% unnest(c(x, y, z)). previously created new variable unnest() now need explicitly mutate(). Convert df %>% unnest(y = fun(x, y, z)) df %>% mutate(y = fun(x, y, z)) %>% unnest(y). keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. ptype Optionally, named list column name-prototype pairs coerce cols , overriding default guessed combining individual values. Alternatively, single empty ptype can supplied, applied cols. names_sep NULL, default, outer names come inner names. string, outer names formed pasting together outer inner column names, separated names_sep. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . .drop, .preserve : list-columns now preserved; want output use select() remove prior unnesting. .id : convert df %>% unnest(x, .id = \"id\") df %>% mutate(id = names(x)) %>% unnest(x)). .sep : use names_sep instead.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"new-syntax","dir":"Reference","previous_headings":"","what":"New syntax","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"tidyr 1.0.0 introduced new syntax nest() unnest() designed similar functions. Converting new syntax straightforward (guided message receive) just need run old analysis, can easily revert previous behaviour using nest_legacy() unnest_legacy() follows:","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column of data frames into rows and columns — unnest","text":"","code":"# unnest() is designed to work with lists of data frames df <- tibble(   x = 1:3,   y = list(     NULL,     tibble(a = 1, b = 2),     tibble(a = 1:3, b = 3:1, c = 4)   ) ) # unnest() recycles input rows for each row of the list-column # and adds a column for each column df %>% unnest(y) #> # A tibble: 4 × 4 #>       x     a     b     c #>   <int> <dbl> <dbl> <dbl> #> 1     2     1     2    NA #> 2     3     1     3     4 #> 3     3     2     2     4 #> 4     3     3     1     4  # input rows with 0 rows in the list-column will usually disappear, # but you can keep them (generating NAs) with keep_empty = TRUE: df %>% unnest(y, keep_empty = TRUE) #> # A tibble: 5 × 4 #>       x     a     b     c #>   <int> <dbl> <dbl> <dbl> #> 1     1    NA    NA    NA #> 2     2     1     2    NA #> 3     3     1     3     4 #> 4     3     2     2     4 #> 5     3     3     1     4  # Multiple columns ---------------------------------------------------------- # You can unnest multiple columns simultaneously df <- tibble(   x = 1:2,   y = list(     tibble(a = 1, b = 2),     tibble(a = 3:4, b = 5:6)   ),   z = list(     tibble(c = 1, d = 2),     tibble(c = 3:4, d = 5:6)   ) ) df %>% unnest(c(y, z)) #> # A tibble: 3 × 5 #>       x     a     b     c     d #>   <int> <dbl> <dbl> <dbl> <dbl> #> 1     1     1     2     1     2 #> 2     2     3     5     3     5 #> 3     2     4     6     4     6  # Compare with unnesting one column at a time, which generates # the Cartesian product df %>%   unnest(y) %>%   unnest(z) #> # A tibble: 5 × 5 #>       x     a     b     c     d #>   <int> <dbl> <dbl> <dbl> <dbl> #> 1     1     1     2     1     2 #> 2     2     3     5     3     5 #> 3     2     3     5     4     6 #> 4     2     4     6     3     5 #> 5     2     4     6     4     6"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"unnest_auto() picks unnest_wider() unnest_longer() inspecting inner names list-col: elements unnamed, uses unnest_longer(indices_include = FALSE). elements named, least one name common across components, uses unnest_wider(). Otherwise, falls back unnest_longer(indices_include = TRUE). handy rapid interactive exploration recommend using scripts, succeed even underlying data radically changes.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"","code":"unnest_auto(data, col)"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatically call unnest_wider() or unnest_longer() — unnest_auto","text":"data data frame. col <tidy-select> List-column unnest.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column into rows — unnest_longer","title":"Unnest a list-column into rows — unnest_longer","text":"unnest_longer() turns element list-column row. naturally suited list-columns elements unnamed length element varies row row. unnest_longer() generally preserves number columns x modifying number rows. Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column into rows — unnest_longer","text":"","code":"unnest_longer(   data,   col,   values_to = NULL,   indices_to = NULL,   indices_include = NULL,   keep_empty = FALSE,   names_repair = \"check_unique\",   simplify = TRUE,   ptype = NULL,   transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column into rows — unnest_longer","text":"data data frame. col <tidy-select> List-column(s) unnest. selecting multiple columns, values row recycled common size. values_to string giving column name (names) store unnested values . multiple columns specified col, can also glue string containing \"{col}\" provide template column names. default, NULL, gives output columns names input columns. indices_to string giving column name (names) store inner names positions (named) values. multiple columns specified col, can also glue string containing \"{col}\" provide template column names. default, NULL, gives output columns names values_to, suffixed \"_id\". indices_include single logical value specifying whether add index column. value inner names, index column character vector names, otherwise integer vector positions. NULL, defaults TRUE value inner names indices_to provided. indices_to provided, indices_include FALSE. keep_empty default, get one row output element list unchopping/unnesting. means size-0 element (like NULL empty data frame vector), entire row dropped output. want preserve rows, use keep_empty = TRUE replace size-0 elements single row missing values. names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_longer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column into rows — unnest_longer","text":"","code":"# `unnest_longer()` is useful when each component of the list should # form a row df <- tibble(   x = 1:4,   y = list(NULL, 1:3, 4:5, integer()) ) df %>% unnest_longer(y) #> # A tibble: 5 × 2 #>       x     y #>   <int> <int> #> 1     2     1 #> 2     2     2 #> 3     2     3 #> 4     3     4 #> 5     3     5  # Note that empty values like `NULL` and `integer()` are dropped by # default. If you'd like to keep them, set `keep_empty = TRUE`. df %>% unnest_longer(y, keep_empty = TRUE) #> # A tibble: 7 × 2 #>       x     y #>   <int> <int> #> 1     1    NA #> 2     2     1 #> 3     2     2 #> 4     2     3 #> 5     3     4 #> 6     3     5 #> 7     4    NA  # If the inner vectors are named, the names are copied to an `_id` column df <- tibble(   x = 1:2,   y = list(c(a = 1, b = 2), c(a = 10, b = 11, c = 12)) ) df %>% unnest_longer(y) #> # A tibble: 5 × 3 #>       x     y y_id  #>   <int> <dbl> <chr> #> 1     1     1 a     #> 2     1     2 b     #> 3     2    10 a     #> 4     2    11 b     #> 5     2    12 c      # Multiple columns ---------------------------------------------------------- # If columns are aligned, you can unnest simultaneously df <- tibble(   x = 1:2,   y = list(1:2, 3:4),   z = list(5:6, 7:8) ) df %>%   unnest_longer(c(y, z)) #> # A tibble: 4 × 3 #>       x     y     z #>   <int> <int> <int> #> 1     1     1     5 #> 2     1     2     6 #> 3     2     3     7 #> 4     2     4     8  # This is important because sequential unnesting would generate the # Cartesian product of the rows df %>%   unnest_longer(y) %>%   unnest_longer(z) #> # A tibble: 8 × 3 #>       x     y     z #>   <int> <int> <int> #> 1     1     1     5 #> 2     1     1     6 #> 3     1     2     5 #> 4     1     2     6 #> 5     2     3     7 #> 6     2     3     8 #> 7     2     4     7 #> 8     2     4     8"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest a list-column into columns — unnest_wider","title":"Unnest a list-column into columns — unnest_wider","text":"unnest_wider() turns element list-column column. naturally suited list-columns every element named, names consistent row--row. unnest_wider() preserves rows x modifying columns. Learn vignette(\"rectangle\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest a list-column into columns — unnest_wider","text":"","code":"unnest_wider(   data,   col,   names_sep = NULL,   simplify = TRUE,   strict = FALSE,   names_repair = \"check_unique\",   ptype = NULL,   transform = NULL )"},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest a list-column into columns — unnest_wider","text":"data data frame. col <tidy-select> List-column(s) unnest. selecting multiple columns, values row recycled common size. names_sep NULL, default, names left . string, outer inner names pasted together using names_sep separator. values unnested unnamed, names_sep must supplied, otherwise error thrown. names_sep supplied, names automatically generated unnamed values increasing sequence integers. simplify TRUE, attempt simplify lists length-1 vectors atomic vector. Can also named list containing TRUE FALSE declaring whether attempt simplify particular column. named list provided, default unspecified columns TRUE. strict single logical specifying whether apply strict vctrs typing rules. FALSE, typed empty values (like list() integer()) nested within list-columns treated like NULL contribute type unnested column. useful working JSON, empty values tend lose type information show list(). names_repair Used check output data frame valid names. Must one following options: \"minimal\": name repair checks, beyond basic existence, \"unique\": make sure names unique empty, \"check_unique\": (default), name repair, check unique, \"universal\": make names unique syntactic function: apply custom name repair. tidyr_legacy: use name repair tidyr 0.8. formula: purrr-style anonymous function (see rlang::as_function()) See vctrs::vec_as_names() details terms strategies used enforce . ptype Optionally, named list prototypes declaring desired output type component. Alternatively, single empty prototype can supplied, applied components. Use argument want check element type expect simplifying. ptype specified, simplify = FALSE simplification possible, list-column returned element type ptype. transform Optionally, named list transformation functions applied component. Alternatively, single function can supplied, applied components. Use argument want transform parse individual elements extracted. ptype transform supplied, transform applied ptype.","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/unnest_wider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest a list-column into columns — unnest_wider","text":"","code":"df <- tibble(   character = c(\"Toothless\", \"Dory\"),   metadata = list(     list(       species = \"dragon\",       color = \"black\",       films = c(         \"How to Train Your Dragon\",         \"How to Train Your Dragon 2\",         \"How to Train Your Dragon: The Hidden World\"       )     ),     list(       species = \"blue tang\",       color = \"blue\",       films = c(\"Finding Nemo\", \"Finding Dory\")     )   ) ) df #> # A tibble: 2 × 2 #>   character metadata         #>   <chr>     <list>           #> 1 Toothless <named list [3]> #> 2 Dory      <named list [3]>  # Turn all components of metadata into columns df %>% unnest_wider(metadata) #> # A tibble: 2 × 4 #>   character species   color films     #>   <chr>     <chr>     <chr> <list>    #> 1 Toothless dragon    black <chr [3]> #> 2 Dory      blue tang blue  <chr [2]>  # Choose not to simplify list-cols of length-1 elements df %>% unnest_wider(metadata, simplify = FALSE) #> # A tibble: 2 × 4 #>   character species   color     films     #>   <chr>     <list>    <list>    <list>    #> 1 Toothless <chr [1]> <chr [1]> <chr [3]> #> 2 Dory      <chr [1]> <chr [1]> <chr [2]> df %>% unnest_wider(metadata, simplify = list(color = FALSE)) #> # A tibble: 2 × 4 #>   character species   color     films     #>   <chr>     <chr>     <list>    <list>    #> 1 Toothless dragon    <chr [1]> <chr [3]> #> 2 Dory      blue tang <chr [1]> <chr [2]>  # You can also widen unnamed list-cols: df <- tibble(   x = 1:3,   y = list(NULL, 1:3, 4:5) ) # but you must supply `names_sep` to do so, which generates automatic names: df %>% unnest_wider(y, names_sep = \"_\") #> # A tibble: 3 × 4 #>       x   y_1   y_2   y_3 #>   <int> <int> <int> <int> #> 1     1    NA    NA    NA #> 2     2     1     2     3 #> 3     3     4     5    NA  # 0-length elements --------------------------------------------------------- # The defaults of `unnest_wider()` treat empty types (like `list()`) as `NULL`. json <- list(   list(x = 1:2, y = 1:2),   list(x = list(), y = 3:4),   list(x = 3L, y = list()) )  df <- tibble(json = json) df %>%   unnest_wider(json) #> # A tibble: 3 × 2 #>   x         y         #>   <list>    <list>    #> 1 <int [2]> <int [2]> #> 2 <NULL>    <int [2]> #> 3 <int [1]> <NULL>     # To instead enforce strict vctrs typing rules, use `strict` df %>%   unnest_wider(json, strict = TRUE) #> # A tibble: 3 × 2 #>   x          y          #>   <list>     <list>     #> 1 <int [2]>  <int [2]>  #> 2 <list [0]> <int [2]>  #> 3 <int [1]>  <list [0]>"},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":null,"dir":"Reference","previous_headings":"","what":"US rent and income data — us_rent_income","title":"US rent and income data — us_rent_income","text":"Captured 2017 American Community Survey using tidycensus package.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US rent and income data — us_rent_income","text":"","code":"us_rent_income"},{"path":"https://tidyr.tidyverse.org/dev/reference/us_rent_income.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US rent and income data — us_rent_income","text":"dataset variables: GEOID FIP state identifier NAME Name state variable Variable name: income = median yearly income, rent = median monthly rent estimate Estimated value moe 90% margin error","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":null,"dir":"Reference","previous_headings":"","what":"World Health Organization TB data — who","title":"World Health Organization TB data — who","text":"subset data World Health Organization Global Tuberculosis Report, accompanying global populations. uses original codes World Health Organization. column names columns 5 60 made combining new_ : method diagnosis (rel = relapse, sn = negative pulmonary smear, sp = positive pulmonary smear, ep = extrapulmonary), gender (f = female, m = male), age group (014 = 0-14 yrs age, 1524 = 15-24, 2534 = 25-34, 3544 = 35-44 years age, 4554 = 45-54, 5564 = 55-64, 65 = 65 years older). who2 lightly modified version makes teaching basics easier tweaking variables slightly consistent dropping iso2 iso3. newrel replaced new_rel, _ added gender.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"World Health Organization TB data — who","text":"","code":"who  who2  population"},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"who","dir":"Reference","previous_headings":"","what":"who","title":"World Health Organization TB data — who","text":"data frame 7,240 rows 60 columns: country Country name iso2, iso3 2 & 3 letter ISO country codes year Year new_sp_m014 - new_rel_f65 Counts new TB cases recorded group. Column names encode three variables describe group.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"who-","dir":"Reference","previous_headings":"","what":"who2","title":"World Health Organization TB data — who","text":"data frame 7,240 rows 58 columns.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"population","dir":"Reference","previous_headings":"","what":"population","title":"World Health Organization TB data — who","text":"data frame 4,060 rows three columns: country Country name year Year population Population","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/who.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"World Health Organization TB data — who","text":"https://www..int/teams/global-tuberculosis-programme/data","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":null,"dir":"Reference","previous_headings":"","what":"Population data from the World Bank — world_bank_pop","title":"Population data from the World Bank — world_bank_pop","text":"Data population World Bank.","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Population data from the World Bank — world_bank_pop","text":"","code":"world_bank_pop"},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Population data from the World Bank — world_bank_pop","text":"dataset variables: country Three letter country code indicator Indicator name: SP.POP.GROW = population growth, SP.POP.TOTL = total population, SP.URB.GROW = urban population growth, SP.URB.TOTL = total urban population 2000-2018 Value year","code":""},{"path":"https://tidyr.tidyverse.org/dev/reference/world_bank_pop.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Population data from the World Bank — world_bank_pop","text":"Dataset World Bank data bank: https://data.worldbank.org","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-development-version","dir":"Changelog","previous_headings":"","what":"tidyr (development version)","title":"tidyr (development version)","text":"pivot_wider_spec() now throws informative error non-data frame inputs (@catalamarti, #1510). tidyr now requires dplyr >=1.1.0 (#1568, @catalamarti).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-131","dir":"Changelog","previous_headings":"","what":"tidyr 1.3.1","title":"tidyr 1.3.1","text":"CRAN release: 2024-01-24 pivot_wider now uses .|> syntax dplyr helper message identify duplicates (@boshek, #1516)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-130","dir":"Changelog","previous_headings":"","what":"tidyr 1.3.0","title":"tidyr 1.3.0","text":"CRAN release: 2023-01-24","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-1-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 1.3.0","text":"New family consistent string separating functions: separate_wider_delim(), separate_wider_position(), separate_wider_regex(), separate_longer_delim(), separate_longer_position(). functions thorough refreshes separate() extract(), featuring improved performance, greater consistency, polished API, new approach handling problems. use stringr supersede extract(), separate(), separate_rows() (#1304). named character vector interface used separate_wider_regex() similar nc package Toby Dylan Hocking. nest() gains .argument allows specify columns nest (rather columns nest, .e. ...). Additionally, .key argument longer deprecated, used whenever ... isn’t specified (#1458). unnest_longer() gains keep_empty argument like unnest() (#1339). pivot_longer() gains cols_vary argument controlling ordering output rows relative original row number (#1312). New datasets who2, household, cms_patient_experience, cms_patient_care demonstrate various tidying challenges (#1333).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.3.0","text":"... argument pivot_longer() pivot_wider() moved front function signature, required arguments optional ones. Additionally, pivot_longer_spec(), pivot_wider_spec(), build_longer_spec(), build_wider_spec() gained ... arguments similar location. change allows us easily add new features pivoting functions without breaking existing CRAN packages user scripts. pivot_wider() provides temporary backwards compatible support case single unnamed argument previously positionally matched id_cols. one special case still works, throw warning encouraging explicitly name id_cols argument. read pattern, see https://design.tidyverse.org/dots--required.html (#1350).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"lifecycle-changes-1-3-0","dir":"Changelog","previous_headings":"","what":"Lifecycle changes","title":"tidyr 1.3.0","text":"functions deprecated tidyr 1.0 1.2 (old lazyeval functions ending _ various arguments unnest()) now warn every use. made defunct 2024 (#1406).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-3-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.3.0","text":"unnest_longer() now consistently drops rows either NULL empty vectors (like integer()) default. Set new keep_empty argument TRUE retain . Previously, keep_empty = TRUE implicitly used NULL, keep_empty = FALSE used empty vectors, inconsistent tidyr verbs argument (#1363). unnest_longer() now uses \"\" index column fully unnamed vectors. also now consistently uses NA index column empty vectors “kept” keep_empty = TRUE (#1442). unnest_wider() now errors values unnested unnamed names_sep provided (#1367). unnest_wider() now generates automatic names partially unnamed vectors. Previously generated fully unnamed vectors, resulting strange mix automatic names name-repaired names (#1367).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"General","title":"tidyr 1.3.0","text":"tidyr functions now consistently disallow renaming tidy-selection. Renaming never meaningful functions, previously either effect caused problems (#1449, #1104). tidyr errors (including input validation) thoroughly reviewed generally likely point right direction (#1313, #1400). uncount() now generic implementations can provided objects data frames (@mgirlich, #1358). uncount() gains ... argument. comes required optional arguments (@mgirlich, #1358). nest(), complete(), expand(), fill() now document support grouped data frames created dplyr::group_by() (#952). built datasets now standard tibbles (#1459). R >=3.4.0 now required, line tidyverse standard supporting previous 5 minor releases R. rlang >=1.0.4 vctrs >=0.5.2 now required (#1344, #1470). Removed dependency ellipsis favor equivalent functions rlang (#1314).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-packing-and-chopping-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Nesting, packing, and chopping","title":"tidyr 1.3.0","text":"unnest(), unchop(), unnest_longer(), unnest_wider() better handle lists additional classes (#1327). pack(), unpack(), chop(), unchop() gain error_call argument, turn improves error calls shown nest() various unnest() adjacent functions (#1446). chop(), unpack(), unchop() gain ..., must empty (#1447). unpack() better job reporting column name duplication issues gives better advice resolve using names_sep. also improves errors functions use unpack(), like unnest() unnest_wider() (#1425, #1367).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Pivoting","title":"tidyr 1.3.0","text":"pivot_longer() longer supports interpreting values_ptypes = list() names_ptypes = list() NULL. empty list() now interpreted <list> prototype apply columns, consistent 0-length value interpreted (#1296). pivot_longer(values_drop_na = TRUE) faster aren’t missing values drop (#1392, @mgirlich). pivot_longer() now memory efficient due usage vctrs::vec_interleave() (#1310, @mgirlich). pivot_longer() now throws slightly better error message values_ptypes names_ptypes provided coercion can’t made (#1364). pivot_wider() now throws better error message column selected names_from values_from also selected id_cols (#1318). pivot_wider() now faster names_sep provided (@mgirlich, #1426). pivot_longer_spec(), pivot_wider_spec(), build_longer_spec(), build_wider_spec() gain error_call argument, resulting better error reporting pivot_longer() pivot_wider() (#1408).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-3-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Missing values","title":"tidyr 1.3.0","text":"fill() now works correctly column named .direction data (#1319, @tjmahr). replace_na() faster aren’t missing values replace (#1392, @mgirlich). documentation replace argument replace_na() now mentions replace always cast type data (#1317).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-121","dir":"Changelog","previous_headings":"","what":"tidyr 1.2.1","title":"tidyr 1.2.1","text":"CRAN release: 2022-09-08 Hot patch release resolve R CMD check failures.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-120","dir":"Changelog","previous_headings":"","what":"tidyr 1.2.0","title":"tidyr 1.2.0","text":"CRAN release: 2022-02-01","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.2.0","text":"complete() expand() longer allow complete expand grouping column. never well-defined since completion/expansion grouped data frame happens “within” group otherwise potential produce erroneous results (#1299). replace_na() longer allows type data change replacement applied. replace now always cast type data replacement made. example, means using replacement value 1.5 integer column longer allowed. Similarly, replacing missing values list-column must now done list(\"foo\") rather just \"foo\".","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-2-0","dir":"Changelog","previous_headings":"","what":"Pivoting","title":"tidyr 1.2.0","text":"pivot_wider() gains new names_expand id_expand arguments turning implicit missing factor levels variable combinations explicit ones. similar drop argument spread() (#770). pivot_wider() gains new names_vary argument controlling ordering combining names_from values values_from column names (#839). pivot_wider() gains new unused_fn argument controlling summarize unused columns aren’t involved pivoting process (#990, thanks @mgirlich initial implementation). pivot_longer()’s names_transform values_transform arguments now accept single function applied columns (#1284, thanks @smingerson initial implementation). pivot_longer()’s names_ptypes values_ptypes arguments now accept single empty ptype applied columns (#1284).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-2-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.2.0","text":"unnest() unchop()’s ptype argument now accepts single empty ptype applied cols (#1284). unpack() now silently skips non-data frame columns specified cols. matches existing behavior unchop() unnest() (#1153).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-2-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.2.0","text":"unnest_wider() unnest_longer() can now unnest multiple columns (#740). unnest_longer()’s indices_to values_to arguments now accept glue specification, useful unnesting multiple columns. hoist(), unnest_longer(), unnest_wider(), ptype supplied, column can’t simplified, result list-column element type ptype (#998). unnest_wider() gains new strict argument controls whether strict vctrs typing rules applied. defaults FALSE backwards compatibility, often useful lax unnesting JSON, doesn’t always map one--one R’s types (#1125). hoist(), unnest_longer(), unnest_wider()’s simplify argument now accepts named list TRUE FALSE control simplification per column basis (#995). hoist(), unnest_longer(), unnest_wider()’s transform argument now accepts single function applied components (#1284). hoist(), unnest_longer(), unnest_wider()’s ptype argument now accepts single empty ptype applied components (#1284).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"grids-1-2-0","dir":"Changelog","previous_headings":"","what":"Grids","title":"tidyr 1.2.0","text":"complete() gains new explicit argument limiting fill implicit missing values. useful don’t want fill pre-existing missing values (#1270). complete() gains grouped data frame method. generates correct completed data frame groups involved (#396, #966).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-2-0","dir":"Changelog","previous_headings":"","what":"Missing values","title":"tidyr 1.2.0","text":"drop_na(), replace_na(), fill() updated utilize vctrs. means can use functions wider variety column types, including lubridate’s Period types (#1094), data frame columns, rcrd type vctrs. replace_na() longer replaces empty atomic elements list-columns (like integer(0)). value replaced list-column NULL (#1168). drop_na() longer drops empty atomic elements list-columns (like integer(0)). value dropped list-column NULL (#1228).","code":""},{"path":[]},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-1-2-0","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"General","title":"tidyr 1.2.0","text":"@mgirlich now tidyr author recognition significant sustained contributions. lazyeval variants tidyr verbs soft-deprecated. Expect move defunct stage next minor release tidyr (#1294). any_of() all_of() tidyselect now re-exported (#1217). dplyr >= 1.0.0 now required.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Pivoting","title":"tidyr 1.2.0","text":"pivot_wider() now gives better advice identify duplicates values uniquely identified (#1113). pivot_wider() now throws informative error values_fn doesn’t result single summary value (#1238). pivot_wider() pivot_longer() now generate informative errors related name repair (#987). pivot_wider() now works correctly values_fill data frame. pivot_wider() longer accidentally retains values_from pivoting zero row data frame (#1249). pivot_wider() now correctly handles case id column name collides value names_from (#1107). pivot_wider() pivot_longer() now check spec columns .name .value character vectors. Additionally, .name column must unique (#1107). pivot_wider()’s names_from values_from arguments now required default values name value don’t correspond columns data. Additionally, must identify least 1 column data (#1240). pivot_wider()’s values_fn argument now correctly allows anonymous functions (#1114). pivot_wider_spec() now works correctly 0-row data frame spec doesn’t identify rows (#1250, #1252). pivot_longer()’s names_ptypes argument now applied names_transform consistency rectangling functions (.e. hoist()) (#1233). check_pivot_spec() new developer facing function validating pivot spec argument. useful extending pivot_longer() pivot_wider() new S3 methods (#1087).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Nesting","title":"tidyr 1.2.0","text":"nest() generic now avoids computing .data, making compatible lazy tibbles (#1134). .names_sep argument data.frame method nest() now actually used (#1174). unnest()’s ptype argument now works expected (#1158). unpack() longer drops empty columns specified cols (#1191). unpack() now works correctly data frame columns containing 1 row 0 columns (#1189). chop() now works correctly data frames 0 rows (#1206). chop()’s cols argument longer optional. matches behavior cols seen elsewhere tidyr (#1205). unchop() now respects ptype unnesting non-list column (#1211).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Rectangling","title":"tidyr 1.2.0","text":"hoist() longer accidentally removes elements duplicated names (#1259).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"grids-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Grids","title":"tidyr 1.2.0","text":"grouped data frame methods complete() expand() now move group columns front result (addition columns completed expanded, already moved front). make intuitive sense, completing expanding “within” group, group columns first thing see (#1289). complete() now applies fill even columns complete specified (#1272). expand(), crossing(), nesting() now correctly retain NA values factors (#1275). expand_grid(), expand(), nesting(), crossing() now silently apply name repair automatically named inputs. avoids number issues resulting duplicate truncated names (#1116, #1221, #1092, #1037, #992). expand_grid(), expand(), nesting(), crossing() now allow columns unnamed data frames used expressions data frame specified, like expand_grid(tibble(x = 1), y = x). consistent tibble() behaves. expand_grid(), expand(), nesting(), crossing() now work correctly data frames containing 0 columns >0 rows (#1189). expand_grid(), expand(), nesting(), crossing() now return 1 row data frame inputs supplied, consistent prod() == 1L idea computations involving number combinations computed empty set return 1 (#1258).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"missing-values-1-2-0-1","dir":"Changelog","previous_headings":"Bug fixes and minor improvements","what":"Missing values","title":"tidyr 1.2.0","text":"drop_na() longer drops missing values columns tidyselect expression results 0 columns selected used (#1227). fill() now treats NaN like missing value (#982).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-114","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.4","title":"tidyr 1.1.4","text":"CRAN release: 2021-09-27 expand_grid() now twice fast pivot_wider() bit faster (@mgirlich, #1130). unchop() now much faster, propagates various functions, unnest(), unnest_longer(), unnest_wider(), separate_rows() (@mgirlich, @DavisVaughan, #1127). unnest() now much faster (@mgirlich, @DavisVaughan, #1127). unnest() longer allows unnesting list-col containing mix vector data frame elements. Previously, worked accident, considered -label usage unnest() now become error.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-113","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.3","title":"tidyr 1.1.3","text":"CRAN release: 2021-03-03 tidyr verbs longer “default” methods lazyeval fallbacks. means ’ll get clearer error messages (#1036). uncount() error non-integer weights gives clearer error message negative weights (@mgirlich, #1069). can unnest dates (#1021, #1089). pivot_wider() works data.table empty key variables (@mgirlich, #1066). separate_rows() works factor columns (@mgirlich, #1058).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-112","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.2","title":"tidyr 1.1.2","text":"CRAN release: 2020-08-27 separate_rows() returns 1.1.0 behaviour empty strings (@rjpatm, #1014).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-111","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.1","title":"tidyr 1.1.1","text":"CRAN release: 2020-07-31 New tidyr logo! stringi dependency removed; substantial dependency make tidyr hard compile resource constrained environments (@rjpat, #936). Replace Rcpp cpp11. See https://cpp11.r-lib.org/articles/motivations.html reasons .","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-110","dir":"Changelog","previous_headings":"","what":"tidyr 1.1.0","title":"tidyr 1.1.0","text":"CRAN release: 2020-05-20","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"general-features-1-1-0","dir":"Changelog","previous_headings":"","what":"General features","title":"tidyr 1.1.0","text":"pivot_longer(), hoist(), unnest_wider(), unnest_longer() gain new transform arguments; allow transform values “flight”. partly needed vctrs coercion rules become stricter, give greater flexibility available previously (#921). Arguments use tidy selection syntax now clearly documented updated use tidyselect 1.1.0 (#872).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Pivoting improvements","title":"tidyr 1.1.0","text":"pivot_wider() pivot_longer() considerably performant, thanks largely improvements underlying vctrs code (#790, @DavisVaughan). pivot_longer() now supports names_to = character() prevents name column created (#961). {r}   df <- tibble(id = 1:3, x_1 = 1:3, x_2 = 4:6)     df %>% pivot_longer(-id, names_to = character()) pivot_longer() longer creates .copy variable presence duplicate column names. makes consistent handling non-unique specs. pivot_longer() automatically disambiguates non-unique ouputs, can occur input variables include additional component don’t care want discard (#792, #793). {r}   df <- tibble(id = 1:3, x_1 = 1:3, x_2 = 4:6)     df %>% pivot_longer(-id, names_pattern = \"(.)_.\")   df %>% pivot_longer(-id, names_sep = \"_\", names_to = c(\"name\", NA))   df %>% pivot_longer(-id, names_sep = \"_\", names_to = c(\".value\", NA)) pivot_wider() gains names_sort argument allows sort column names order. default, FALSE, orders columns first appearance (#839). future version, ’ll consider changing default TRUE. pivot_wider() gains names_glue argument allows construct output column names glue specification. pivot_wider() arguments values_fn values_fill can now single values; now need use named list want use different values different value columns (#739, #746). also get improved errors ’re expected type.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-1-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.1.0","text":"hoist() now automatically names pluckers single string (#837). error use duplicated column names (@mgirlich, #834), now uses rlang::list2() behind scenes (means can now use !!! :=) (#801). unnest_longer(), unnest_wider(), hoist() better job simplifying list-cols. longer add unneeded unspecified() result still list (#806), work list contains non-vectors (#810, #848). unnest_wider(names_sep = \"\") now provides default names unnamed inputs, suppressing many previous name repair messages (#742).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-1-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.1.0","text":"pack() nest() gains .names_sep argument allows strip outer names inner names, symmetrical way argument unpack() unnest() combines inner outer names (#795, #797). unnest_wider() unnest_longer() can now unnest list_of columns. important unnesting columns created nest() pivot_wider(), create list_of columns id columns non-unique (#741).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-1-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 1.1.0","text":"chop() now creates list-columns class vctrs::list_of(). helps keep track type case chopped data frame empty, allowing unchop() reconstitute data frame correct number types column even observations. drop_na() now preserves attributes unclassed vectors (#905). expand(), expand_grid(), crossing(), nesting() evaluate inputs iteratively, can refer freshly created columns, e.g. crossing(x = seq(-2, 2), y = x) (#820). expand(), expand_grid(), crossing(), nesting() gain .name_repair giving control name repair strategy (@jeffreypullin, #798). extract() lets use NA , documented (#793). extract(), separate(), hoist(), unnest_longer(), unnest_wider() give better error message col missing (#805). pack()’s first argument now .data instead data (#759). pivot_longer() now errors values_to length-1 character vector (#949). pivot_longer() pivot_wider() now generic implementations can provided objects data frames (#800). pivot_wider() can now pivot data frame columns (#926) unite(na.rm = TRUE) now works types variable, just character vectors (#765). unnest_wider() gives better error message attempt unnest multiple columns (#740). unnest_auto() works input data contains column called col (#959).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-102","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.2","title":"tidyr 1.0.2","text":"CRAN release: 2020-01-24 Minor fixes dev versions rlang, tidyselect, tibble.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-101","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.1","title":"tidyr 1.0.1","text":"exist since accidentally released v1.0.2","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-100","dir":"Changelog","previous_headings":"","what":"tidyr 1.0.0","title":"tidyr 1.0.0","text":"CRAN release: 2019-09-11","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 1.0.0","text":"See vignette(\"-packages\") detailed transition guide. nest() unnest() new syntax. majority existing usage automatically translated new syntax warning. doesn’t work, put script use old versions can take closer look update code: nest() now preserves grouping, implications downstream calls group-aware functions, dplyr::mutate() filter(). first argument nest() changed data .data. unnest() uses emerging tidyverse standard disambiguate unique names. Use names_repair = tidyr_legacy request previous approach. unnest_()/nest_() lazyeval methods unnest()/nest() now defunct. deprecated time, , since interface changed, package authors need update avoid deprecation warnings. think one clean break less work everyone. lazyeval functions formally deprecated, made defunct next major release. (See lifecycle vignette details deprecation stages). crossing() nesting() now return 0-row outputs input length-0 vector. want preserve previous behaviour silently dropped inputs, convert empty vectors NULL. (discussion general pattern https://github.com/tidyverse/principles/issues/24)","code":"library(tidyr) nest <- nest_legacy unnest <- unnest_legacy"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"pivoting-1-0-0","dir":"Changelog","previous_headings":"","what":"Pivoting","title":"tidyr 1.0.0","text":"New pivot_longer() pivot_wider() provide modern alternatives spread() gather(). carefully redesigned easier learn remember, include many new features. Learn vignette(\"pivot\"). functions resolve multiple existing issues spread()/gather(). functions now handle mulitple value columns (#149/#150), support vector types (#333), use tidyverse conventions duplicated column names (#496, #478), symmetric (#453). pivot_longer() gracefully handles duplicated column names (#472), can directly split column names multiple variables. pivot_wider() can now aggregate (#474), select keys (#572), control generated column names (#208). demonstrate functions work practice, tidyr gained several new datasets: relig_income, construction, billboard, us_rent_income, fish_encounters world_bank_pop. Finally, tidyr demos removed. dated, superseded vignette(\"pivot\").","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"rectangling-1-0-0","dir":"Changelog","previous_headings":"","what":"Rectangling","title":"tidyr 1.0.0","text":"tidyr contains four new functions support rectangling, turning deeply nested list tidy tibble: unnest_longer(), unnest_wider(), unnest_auto(), hoist(). documented new vignette: vignette(\"rectangle\"). unnest_longer() unnest_wider() make easier unnest list-columns vectors either rows columns (#418). unnest_auto() automatically picks _longer() _wider() using heuristics based presence common names. New hoist() provides convenient way plucking components list-column top-level columns (#341). particularly useful working deeply nested JSON, provides convenient shortcut mutate() + map() pattern: {r} df %>% hoist(metadata, name = \"name\") # shortcut df %>% mutate(name = map_chr(metadata, \"name\"))","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nesting-1-0-0","dir":"Changelog","previous_headings":"","what":"Nesting","title":"tidyr 1.0.0","text":"nest() unnest() updated new interfaces closely aligned evolving tidyverse conventions. use theory developed vctrs consistently handle mixtures input types, arguments overhauled based last years experience. supported new vignette(\"nest\"), outlines main ideas nested data (’s still rough, get better time). biggest change operation multiple columns: df %>% unnest(x, y, z) becomes df %>% unnest(c(x, y, z)) df %>% nest(x, y, z) becomes df %>% nest(data = c(x, y, z)). done best ensure common uses nest() unnest() continue work, generating informative warning telling precisely need update code. Please file issue ’ve missed important use case. unnest() overhauled: New keep_empty parameter ensures every row input gets least one row output, inserting missing values needed (#358). Provides names_sep argument control inner outer column names combined. Uses standard tidyverse name-repair rules, default get error output contain multiple columns name. can override using name_repair (#514). Now supports NULL entries (#436).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"packing-and-chopping-1-0-0","dir":"Changelog","previous_headings":"","what":"Packing and chopping","title":"tidyr 1.0.0","text":"hood, nest() unnest() implemented chop(), pack(), unchop(), unpack(): pack() unpack() allow pack unpack columns data frame columns (#523). chop() unchop() chop rows sets list-columns. Packing chopping interesting primarily atomic operations underlying nesting (similarly, unchop unpacking underlie unnesting), don’t expect used directly often.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-1-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 1.0.0","text":"New expand_grid(), tidy version expand.grid(), lower-level existing expand() crossing() functions, takes individual vectors, sort uniquify . crossing(), nesting(), expand() rewritten use vctrs package. affect much existing code, considerably simplies implementation ensures functions work consistently across generalised vectors (#557). part alignment, functions now drop NULL inputs, 0-length vector.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-1-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 1.0.0","text":"full_seq() now also works gaps observations shorter given period, within tolerance given tol. Previously, gaps consecutive observations range [period, period + tol]; gaps can now range [period - tol, period + tol] (@ha0ye, #657). tidyr now re-exports tibble(), as_tibble(), tribble(), well tidyselect helpers (starts_with(), ends_width(), …). makes generating documentation, reprexes, tests easier, makes tidyr easier use without also attaching dplyr. functions take ... instrumented functions ellipsis package warn ’ve supplied arguments ignored (typically ’ve misspelled argument name) (#573). complete() now uses full_join() levels preserved even levels specified (@Ryo-N7, #493). crossing() now takes unique values data frame inputs, just vector inputs (#490). gather() throws error column data frame (#553). extract() (hence pivot_longer()) can extract multiple input values single output column (#619). fill() now implemented using dplyr::mutate_at(). radically simplifies implementation considerably improves performance working grouped data (#520). fill() now accepts downup updown fill directions (@coolbutuseless, #505). unite() gains na.rm argument, making easier remove missing values prior uniting values together (#203)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-083","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.3","title":"tidyr 0.8.3","text":"CRAN release: 2019-03-01 crossing() preserves factor levels (#410), now works list-columns (#446, @SamanthaToet). (also help expand() built top crossing()) nest() compatible dplyr 0.8.0. spread() works id variable names (#525). unnest() preserves column unnested input zero-length (#483), using list_of() attribute correctly restore columns, possible. unnest() run named unnamed list-columns length (@hlendway, #460).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-082","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.2","title":"tidyr 0.8.2","text":"CRAN release: 2018-10-28 separate() now accepts NA column name argument denote columns omitted result. (@markdly, #397). Minor updates ensure compatibility dependencies.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-081","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.1","title":"tidyr 0.8.1","text":"CRAN release: 2018-05-18 unnest() weakens test “atomicity” restore previous behaviour unnesting factors dates (#407).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-080","dir":"Changelog","previous_headings":"","what":"tidyr 0.8.0","title":"tidyr 0.8.0","text":"CRAN release: 2018-01-29","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 0.8.0","text":"deliberate breaking changes release. However, number packages failing errors related numbers elements columns, row names. possible accidental API changes new bugs. see error package, sincerely appreciate minimal reprex. separate() now correctly uses -1 refer far right position, instead -2. depended behaviour, ’ll need switch packageVersion(\"tidyr\") > \"0.7.2\"","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-0-8-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 0.8.0","text":"Increased test coverage 84% 99%. uncount() performs inverse operation dplyr::count() (#279)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-8-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.8.0","text":"complete(data) now returns data rather throwing error (#390). complete() zero-length completions returns original input (#331). crossing() preserves NAs (#364). expand() empty input gives empty data frame instead NULL (#331). expand(), crossing(), complete() now complete empty factors instead dropping (#270, #285) extract() better error message regex contain expected number groups (#313). drop_na() longer drops columns (@jennybryan, #245), works list-cols (#280). Equivalent NA list column empty (length 0) data structure. nest() now faster, especially long data frame collapsed nested data frame rows. nest() zero-row data frame works expected (#320). replace_na() longer complains try replace missing values variables present data (#356). replace_na() now also works vectors (#342, @flying-sheep), can replace NULL list-columns. throws better error message attempt replace something length 1. separate() longer checks ... empty, allowing methods make use . check added tidyr 0.4.0 (2016-02-02) deprecate previous behaviour ... passed strsplit(). separate() extract() now insert columns correct position drop = TRUE (#394). separate() now works correctly counts RHS using negative integer sep values (@markdly, #315). separate() gets improved warning message pieces aren’t expected (#375). separate_rows() supports list columns (#321), works empty tibbles. spread() now consistently returns 0 row outputs 0 row inputs (#269). spread() now works key column includes NA drop FALSE (#254). spread() longer returns tibbles row names (#322). spread(), separate(), extract() (#255), gather() (#347) now replace existing variables rather creating invalid data frame duplicated variable names (matching semantics mutate). unite() now works (documented) don’t supply variables (#355). unnest() gains preserve argument allows preserve list columns without unnesting (#328). unnest() can unnested list-columns contains lists lists (#278). unnest(df) now works df contains list-cols (#344)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-072","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.2","title":"tidyr 0.7.2","text":"CRAN release: 2017-10-16 SE variants gather_(), spread_() nest_() now treat non-syntactic names way pre tidy eval versions tidyr (#361). Fix tidyr bug revealed R-devel.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-071","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.1","title":"tidyr 0.7.1","text":"CRAN release: 2017-09-01 hotfix release account tidyselect changes unit tests. Note upcoming version tidyselect backtracks changes announced 0.7.0. special evaluation semantics selection changed back old behaviour new rules causing much trouble confusion. now data expressions (symbols calls : c()) can refer registered variables objects context. However semantics context expressions (calls : c()) remain . expressions evaluated context refer registered variables. ’re writing functions refer contextual objects, still good idea avoid data expressions following advice 0.7.0 release notes.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-070","dir":"Changelog","previous_headings":"","what":"tidyr 0.7.0","title":"tidyr 0.7.0","text":"CRAN release: 2017-08-16 release includes important changes tidyr internals. Tidyr now supports new tidy evaluation framework quoting (NSE) functions. also uses new tidyselect package selecting backend.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"breaking-changes-0-7-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"tidyr 0.7.0","text":"see error messages objects functions found, likely selecting functions now stricter arguments example selecting function gather() ... argument. change makes code robust disallowing ambiguous scoping. Consider following code: select first three columns (using x defined global environment), select first two columns (using column named x)? solve ambiguity, now make strict distinction data context expressions. data expression either bare name expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. practice means can longer refer contextual objects like : now explicit find objects. , can use quasiquotation operator !! evaluate argument early inline result: {r} mtcars %>% gather(var, value, !! 1:ncol(mtcars)) mtcars %>% gather(var, value, !! 1:x) mtcars %>% gather(var, value, !! -(1:x)) alternative turn data expression context expression using seq() seq_len() instead :. See section tidyselect information semantics. Following switch tidy evaluation, might see warnings “variable context set”. likely caused supplying helpers like everything() underscored versions tidyr verbs. Helpers always evaluated lazily. fix , just quote helper formula: drop_na(df,   ~everything()). selecting functions now stricter supply integer positions. see error along lines please round positions supplying tidyr. Double vectors fine long rounded.","code":"x <- 3 df <- tibble(w = 1, x = 2, y = 3) gather(df, \"variable\", \"value\", 1:x) mtcars %>% gather(var, value, 1:ncol(mtcars))  x <- 3 mtcars %>% gather(var, value, 1:x) mtcars %>% gather(var, value, -(1:x)) `-0.949999999999999`, `-0.940000000000001`, ... must resolve to integer column positions, not a double vector"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"switch-to-tidy-evaluation-0-7-0","dir":"Changelog","previous_headings":"","what":"Switch to tidy evaluation","title":"tidyr 0.7.0","text":"tidyr now tidy evaluation grammar. See programming vignette dplyr practical information tidy evaluation. tidyr port bit special. philosophy tidy evaluation R code refer real objects (data frame context), make exceptions rule tidyr. reason several functions accept bare symbols specify names new columns create (gather() prime example). tidy symbol represent actual object. workaround capture arguments using rlang::quo_name() (still support quasiquotation can unquote symbols strings). type NSE now discouraged tidyverse: symbols R code represent real objects. Following switch tidy eval underscored variants softly deprecated. However remain around time without warning backward compatibility.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"switch-to-the-tidyselect-backend-0-7-0","dir":"Changelog","previous_headings":"","what":"Switch to the tidyselect backend","title":"tidyr 0.7.0","text":"selecting backend dplyr extracted standalone package tidyselect tidyr now uses selecting variables. used selecting multiple variables (drop_na()) well single variables (col argument extract() separate(), key value arguments spread()). implies following changes: arguments selecting single variable now support features dplyr::pull(). can supply name position, including negative positions. Multiple variables now selected bit differently. now make strict distinction data context expressions. data expression either bare name expression like x:y c(x, y). data expression, can refer columns data frame. Everything else context expression can refer objects defined <-. can still refer contextual objects data expression explicit. One way explicit unquote variable environment tidy eval operator !!: hand, select helpers like start_with() context expressions. therefore easy refer objects never ambiguous data columns: {r} x <- \"d\" drop_na(df, starts_with(x)) special rules contrast dplyr tidyr verbs (data context scope) make sense selecting functions provide robust helpful semantics.","code":"x <- 2 drop_na(df, 2)     # Works fine drop_na(df, x)     # Object 'x' not found drop_na(df, !! x)  # Works as if you had supplied 2"},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-063","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.3","title":"tidyr 0.6.3","text":"CRAN release: 2017-05-15 Patch tests compatible dev tibble","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-062","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.2","title":"tidyr 0.6.2","text":"CRAN release: 2017-05-04 Register C functions Added package docs Patch tests compatible dev dplyr.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-061","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.1","title":"tidyr 0.6.1","text":"CRAN release: 2017-01-10 Patch test compatible dev tibble Changed deprecation message extract_numeric() point readr::parse_number() rather readr::parse_numeric()","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-060","dir":"Changelog","previous_headings":"","what":"tidyr 0.6.0","title":"tidyr 0.6.0","text":"CRAN release: 2016-08-12","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"api-changes-0-6-0","dir":"Changelog","previous_headings":"","what":"API changes","title":"tidyr 0.6.0","text":"drop_na() removes observations NA given variables. variables given, variables considered (#194, @janschulz). extract_numeric() deprecated (#213). Renamed table4 table5 table4a table4b make connection clear. key value variables table2 renamed type count.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-6-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.6.0","text":"expand(), crossing(), nesting() now silently drop zero-length inputs. crossing_() nesting_() versions crossing() nesting() take list input. full_seq() works correctly dates date/times.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-051","dir":"Changelog","previous_headings":"","what":"tidyr 0.5.1","title":"tidyr 0.5.1","text":"CRAN release: 2016-06-14 Restored compatibility R < 3.3.0 avoiding getS3method(envir = ) (#205, @krlmlr).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-050","dir":"Changelog","previous_headings":"","what":"tidyr 0.5.0","title":"tidyr 0.5.0","text":"CRAN release: 2016-06-12","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-functions-0-5-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tidyr 0.5.0","text":"separate_rows() separates observations multiple delimited values separate rows (#69, @aaronwolen).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-5-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.5.0","text":"complete() preserves grouping created dplyr (#168). expand() (hence complete()) preserves ordered attribute factors (#165). full_seq() preserve attributes dates date/times (#156), sequences longer need start 0. gather() can now gather together list columns (#175), gather_.data.frame(na.rm = TRUE) now removes missing values ’re actually present (#173). nest() returns correct output every variable nested (#186). separate() fills right--left (left--right!) fill = “left” (#170, @dgrtwo). separate() unite() now automatically drop removed variables grouping (#159, #177). spread() gains sep argument. -null, name columns “keyvalue”. Additionally, sep NULL missing values converted <NA> (#68). spread() works presence list-columns (#199) unnest() works non-syntactic names (#190). unnest() gains sep argument. non-null, rename columns nested data frames include original column name, nested column name, separated .sep (#184). unnest() gains .id argument works way bind_rows(). useful named list data frames vectors (#125). Moved useful sample datasets DSR package. Made compatible dplyr 0.4 0.5. tidyr functions create new columns aggressive re-encoding column names UTF-8.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-041","dir":"Changelog","previous_headings":"","what":"tidyr 0.4.1","title":"tidyr 0.4.1","text":"CRAN release: 2016-02-05 Fixed bug nest() nested data ending wrong row (#158).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-040","dir":"Changelog","previous_headings":"","what":"tidyr 0.4.0","title":"tidyr 0.4.0","text":"CRAN release: 2016-01-18","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"nested-data-frames-0-4-0","dir":"Changelog","previous_headings":"","what":"Nested data frames","title":"tidyr 0.4.0","text":"nest() unnest() overhauled support useful way structuring data frames: nested data frame. grouped data frame, one row per observation, additional metadata define groups. nested data frame, one row per group, individual observations stored column list data frames. useful structure lists objects (like models) one element per group. nest() now produces single list data frames called “data” rather list column variable. Nesting variables included nested data frames. also works grouped data frames made dplyr::group_by(). can override default column name .key. unnest() gains .drop argument controls happens list columns. default, ’re kept output doesn’t require row duplication; otherwise ’re dropped. unnest() now mutate() semantics ... - allows unnest transformed columns easily. (Previously used select semantics).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"expanding-0-4-0","dir":"Changelog","previous_headings":"","what":"Expanding","title":"tidyr 0.4.0","text":"expand() allows evaluate arbitrary expressions like full_seq(year). previously using c() created nested combinations, ’ll now need use nesting() (#85, #121). nesting() crossing() allow create nested crossed data frames individual vectors. crossing() similar base::expand.grid() full_seq(x, period) creates full sequence values min(x) max(x) every period values.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"minor-bug-fixes-and-improvements-0-4-0","dir":"Changelog","previous_headings":"","what":"Minor bug fixes and improvements","title":"tidyr 0.4.0","text":"fill() fills NULLs list-columns. fill() gains direction argument can fill either upwards downwards (#114). gather() now stores key column character, default. revert previous behaviour using factor (allows preserve ordering columns), use key_factor = TRUE (#96). tidyr verbs right thing grouped data frames created group_by() (#122, #129, #81). seq_range() removed. never used announced. spread() creates columns mixed type convert = TRUE (#118, @jennybc). spread() drop = FALSE handles zero-length factors (#56). spread()ing data frame key value columns creates one row output (#41). unite() now removes old columns adding new (#89, @krlmlr). separate() now warns defunct … argument used (#151, @krlmlr).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-031","dir":"Changelog","previous_headings":"","what":"tidyr 0.3.1","title":"tidyr 0.3.1","text":"CRAN release: 2015-09-10 Fixed bug attributes non-gather columns lost (#104)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-030","dir":"Changelog","previous_headings":"","what":"tidyr 0.3.0","title":"tidyr 0.3.0","text":"CRAN release: 2015-09-08","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"tidyr 0.3.0","text":"New complete() provides wrapper around expand(), left_join() replace_na() common task: completing data frame missing combinations variables. fill() fills missing values column last non-missing value (#4). New replace_na() makes easy replace missing values something meaningful data. nest() complement unnest() (#3). unnest() can now work multiple list-columns time. don’t supply columns names, unlist list-columns (#44). unnest() can also handle columns lists data frames (#58).","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.3.0","text":"tidyr longer depends reshape2. fix issues also try load reshape (#88). %>% re-exported magrittr. expand() now supports nesting crossing (see examples details). comes expense creating new variables inline (#46). expand_ SE evaluation correctly can pass character vector columns names (list formulas etc) (#70). extract() 10x faster now uses stringi instead base R regular expressions. also returns NA instead throwing error regular expression doesn’t match (#72). extract() separate() preserve character vectors convert TRUE (#99). internals spread() rewritten, now preserve attributes input value column. means can now spread date (#62) factor (#35) inputs. spread() gives informative error message key value don’t exist input data (#36). separate() displays first 20 failures (#50). finer control happens two matches: can fill missing values either “left” “right” (#49). separate() longer throws error number pieces aren’t expected - instead uses drops extra values fills right gives warning. input NA separate() extract() return silently return NA outputs, rather throwing error. (#77) Experimental unnest() method lists removed.","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"tidyr-020","dir":"Changelog","previous_headings":"","what":"tidyr 0.2.0","title":"tidyr 0.2.0","text":"CRAN release: 2014-12-05","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"new-functions-0-2-0","dir":"Changelog","previous_headings":"","what":"New functions","title":"tidyr 0.2.0","text":"Experimental expand() function (#21). Experiment unnest() function converting named lists data frames. (#3, #22)","code":""},{"path":"https://tidyr.tidyverse.org/dev/news/index.html","id":"bug-fixes-and-minor-improvements-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes and minor improvements","title":"tidyr 0.2.0","text":"extract_numeric() preserves negative signs (#20). gather() better defaults key value supplied. ... omitted, gather() selects columns (#28). Performance now comparable reshape2::melt() (#18). separate() gains extra argument lets control happens extra pieces. default throw “error”, can also “merge” “drop”. spread() gains drop argument, allows preserve missing factor levels (#25). converts factor value variables character vectors, instead embedding matrix inside data frame (#35).","code":""}]