canmap

canmap provides easy access to standard Canadian geographic shapefiles, as well as the associated metadata that helps pick which one you’d like. It’s named after mapbox, but instead of a box it’s a can. Except mapcan was already taken on CRAN.

Installation

You can install the external development version of canmap with:

remotes::install_github("tweed1e/canmap")

And you can install the internal development version of canmap with:

remotes::install_gitlab("tweejes/canmap", host = "gitlab.statcan.ca")

Example

library(canmap)

default_shp <- dplyr::filter(shapefile_paths,
                  geo_code == "pr_" &
                  file_type == "digital boundary file" &
                  format == "ArcGIS (.shp)" &
                  language == "english"  &
                  ref_date == 2016 &
                  projection == "projection in Lambert conformal conic"
                )

default_shp
#> # A tibble: 1 x 10
#>   filepath size  path  ref_date geo_code geo_level file_type format projection
#>   <chr>    <chr> <chr>    <dbl> <chr>    <chr>     <chr>     <chr>  <chr>     
#> 1 lpr_000… 1.31M http…     2016 pr_      province… digital … ArcGI… projectio…
#> # … with 1 more variable: language <chr>

# then pick a shapefile and get the link:
(url <- default_shp[1, ]$path)
#> [1] "http://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/files-fichiers/2016/lpr_000a16a_e.zip"

# then you can download it yourself, or use download_geography(url)
shp_path <- download_geography(url)
#> lpr_000a16a_e.zip already downloaded, returning filepath to unzipped .shp.

# and use sf to read the shapefile.
provinces <- sf::read_sf(shp_path)

Then input into ggplot + sf:

library(ggplot2)

ggplot() +
  geom_sf(data = provinces) +
  theme_minimal() +
  labs(title = "Provinces & Territories / Digital Boundary File", 
       x = "Longitude", y = "Latitude",
       caption = paste0(".zip source name: ", default_shp$filepath, ".zip"))

Documentation

A list of useful links to clean up later:

• Geography definitions and documentation
• Root for geography downloads and documentation
• Direct link to download directory for 2016 shapefiles
• Heirarchy of geography

Parsing the filename for metadata

Suppose you’ve downloaded the geography file lpr_000a16a_e.zip. The filename defines the important geographic characteristics of the file (you can process using the code_pos dataset for code positions, or geo_info).

str(geo_info("lpr_000a16a_e"))
#> Classes 'tbl_df', 'tbl' and 'data.frame':    1 obs. of  8 variables:
#>  $ filename  : chr "lpr_000a16a_e"
#>  $ ref_date  : chr "2016"
#>  $ geo_code  : chr "pr_"
#>  $ geo_level : chr "province and territory"
#>  $ file_type : chr "digital boundary file"
#>  $ format    : chr "ArcGIS (.shp)"
#>  $ projection: chr "projection in Lambert conformal conic"
#>  $ language  : chr "english"

And each of these codes has a meaning that can be found (sometimes) in the geography guide that accompanies a downloaded file (but you can’t find out the details until after you’ve downloaded it, and is missing some information).

Your first default parameters should be:

• file_type == "a" (digital boundary file—it doesn’t look as good but it’s smaller)
• format == "a" (ArcGIS/ArcInfo®/.shp—for use with sf and other R geographic packages)
• geo_coverage == "000" (Canada—the only option AFAIK)
• projection == "g" (geographic projection/lat-long—this makes it less likely for the user to get caught up in coordinate reference systems [CRS] conversion issues)

The most important choices for the user are: year (2016 is the latest census year currently available), language (english or french) and geo_code/geo_level. A list of geo codes and geo levels are given in the code_book dataset:

dplyr::filter(code_book, code_type == "geo_level")
#> # A tibble: 34 x 3
#>    code_type code  code_desc                                        
#>    <chr>     <chr> <chr>                                            
#>  1 geo_level pr_   province and territory                           
#>  2 geo_level cd_   census division                                  
#>  3 geo_level ccs   census consolidated subdivision                  
#>  4 geo_level csd   census subdivision                               
#>  5 geo_level er_   economic region                                  
#>  6 geo_level cma   census metropolitan area and census agglomeration
#>  7 geo_level fed   federal electoral district                       
#>  8 geo_level ct_   census tract                                     
#>  9 geo_level dpl   designated place                                 
#> 10 geo_level pc_   population centre                                
#> # … with 24 more rows

Business data usually isn’t released below the economic region level (er_), while census data can go down to census tract (ct_), dissemination area (da_) or dissemination block (db_).

English and french maps are in different files, so they have different codes: lpr_000a16a_e.zip has the english province/territory maps and lpr_000a16a_f.zip has the french province/territory maps. The only difference, AFAIK, is that the guide and geography names are in french in the french version.

Notes & Other Packages

There are some other great packages to make Canadian maps!

• cancensus (R package available from CRAN + github)
• censusmapper.ca (from the same ppl as ^, but a website)
• rcanvec (R package to get v cool NTS maps, good for small scales)

And others probably! openstreetmap and osmdata and leaflet are all useful as well.

Please note that the ‘canmap’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.