An R package to access Formula 1 Data from the Jolpica API (formerly Ergast) and the official F1 data stream via the FastF1 Python library.
Install the stable version from CRAN:
install.packages("f1dataR")or install the development version from GitHub:
if (!require("remotes")) install.packages("remotes")
remotes::install_github("SCasanova/f1dataR")
library(f1dataR)Data is pulled from:
Note the Ergast Motor Racing Database API will be shutting down at the end of 2024. A new data source (Jolpica-F1 project) was identified and implemented.
load_laps(season = "current", race = "last")
This function loads lap-by-lap time data for all drivers in a given season and round. Round refers to race number. The defaults are current season and last race. Lap data is limited to 1996-present.
Example:
load_laps()
#> # A tibble: 1,177 × 6
#> driver_id position time lap time_sec season
#> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 norris 1 1:40.424 1 100. 2024
#> 2 max_verstappen 2 1:41.413 1 101. 2024
#> 3 hamilton 3 1:42.702 1 103. 2024
#> 4 russell 4 1:43.822 1 104. 2024
#> 5 piastri 5 1:45.268 1 105. 2024
#> 6 hulkenberg 6 1:45.723 1 106. 2024
#> 7 alonso 7 1:46.384 1 106. 2024
#> 8 leclerc 8 1:47.090 1 107. 2024
#> 9 colapinto 9 1:47.696 1 108. 2024
#> 10 perez 10 1:48.150 1 108. 2024
#> # ℹ 1,167 more rowsor
load_laps(season = 2021, round = 15)
#> # A tibble: 1,025 × 6
#> driver_id position time lap time_sec season
#> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 sainz 1 1:42.997 1 103. 2021
#> 2 norris 2 1:44.272 1 104. 2021
#> 3 russell 3 1:46.318 1 106. 2021
#> 4 stroll 4 1:47.279 1 107. 2021
#> 5 ricciardo 5 1:48.221 1 108. 2021
#> 6 alonso 6 1:49.347 1 109. 2021
#> 7 hamilton 7 1:49.826 1 110. 2021
#> 8 perez 8 1:50.617 1 111. 2021
#> 9 ocon 9 1:51.098 1 111. 2021
#> 10 raikkonen 10 1:51.778 1 112. 2021
#> # ℹ 1,015 more rows
load_driver_telemetry(season = "current", race = "last", session = "R", driver, laps = "all")
When the parameters for season (four digit year), round (number or GP name), session (FP1. FP2, FP3, Q, S, SS, or R), and driver code (three letter code) are entered, the function will load all data for a session and the pull the info for the selected driver. The first time a session is called, loading times will be relatively long but in subsequent calls this will improve to only a couple of seconds
load_driver_telemetry(season = 2022, round = 4, driver = "PER")
#> # A tibble: 592 × 19
#> date session_time time rpm speed n_gear throttle brake
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 2022-04-24 14:19:27 8308. 0 11221 282 7 100 FALSE
#> 2 2022-04-24 14:19:27 8308. 0.021 11221 283 7 100 FALSE
#> 3 2022-04-24 14:19:28 8308. 0.278 11221 284 7 100 FALSE
#> 4 2022-04-24 14:19:28 8308. 0.401 11279 285 7 100 FALSE
#> 5 2022-04-24 14:19:28 8309. 0.678 11337 286 7 100 FALSE
#> 6 2022-04-24 14:19:28 8309. 0.681 11376 287 7 100 FALSE
#> 7 2022-04-24 14:19:28 8309. 0.86 11416 288 7 100 FALSE
#> 8 2022-04-24 14:19:29 8309. 1.08 11456 289 7 100 FALSE
#> 9 2022-04-24 14:19:29 8309. 1.18 11461 289 7 100 FALSE
#> 10 2022-04-24 14:19:29 8309. 1.24 11467 290 7 100 FALSE
#> # ℹ 582 more rows
#> # ℹ 11 more variables: drs <dbl>, source <chr>, relative_distance <dbl>,
#> # status <chr>, x <dbl>, y <dbl>, z <dbl>, distance <dbl>,
#> # driver_ahead <chr>, distance_to_driver_ahead <dbl>, …
load_driver_telemetry(season = 2018, round = 7, "Q", "HAM", laps = "fastest")
#> # A tibble: 534 × 19
#> date session_time time rpm speed n_gear throttle brake
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 2018-06-09 18:59:18 3788. 0 10674 297 8 100 FALSE
#> 2 2018-06-09 18:59:18 3788. 0.016 10704 298 8 100 FALSE
#> 3 2018-06-09 18:59:18 3788. 0.043 10762 299 8 100 FALSE
#> 4 2018-06-09 18:59:19 3788. 0.256 10820 301 8 100 FALSE
#> 5 2018-06-09 18:59:19 3788. 0.343 10847 302 8 100 FALSE
#> 6 2018-06-09 18:59:19 3788. 0.496 10875 303 8 100 FALSE
#> 7 2018-06-09 18:59:19 3789. 0.643 10921 303 8 100 FALSE
#> 8 2018-06-09 18:59:19 3789. 0.736 10967 304 8 100 FALSE
#> 9 2018-06-09 18:59:19 3789. 0.943 10990 305 8 100 FALSE
#> 10 2018-06-09 18:59:19 3789. 0.976 11014 306 8 100 FALSE
#> # ℹ 524 more rows
#> # ℹ 11 more variables: drs <dbl>, source <chr>, relative_distance <dbl>,
#> # status <chr>, x <dbl>, y <dbl>, z <dbl>, distance <dbl>,
#> # driver_ahead <chr>, distance_to_driver_ahead <dbl>, …
load_session_laps(season = "current", race = "last", session = "R", add_weather = FALSE)
This function will give us detailed information of lap and sector times, tyres, weather (optional), and more for every lap of the GP and driver.
load_session_laps(season = 2023, round = 4, add_weather = TRUE)
#> # A tibble: 962 × 39
#> time driver driver_number lap_time lap_number stint pit_out_time pit_in_time
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3892. VER 1 110. 1 1 NaN NaN
#> 2 4000. VER 1 108. 2 1 NaN NaN
#> 3 4108. VER 1 108. 3 1 NaN NaN
#> 4 4215. VER 1 107. 4 1 NaN NaN
#> 5 4322. VER 1 107. 5 1 NaN NaN
#> 6 4430. VER 1 107. 6 1 NaN NaN
#> 7 4537. VER 1 107. 7 1 NaN NaN
#> 8 4643. VER 1 107. 8 1 NaN NaN
#> 9 4750. VER 1 107. 9 1 NaN NaN
#> 10 4861. VER 1 111. 10 1 NaN 4860.
#> # ℹ 952 more rows
#> # ℹ 31 more variables: sector1time <dbl>, sector2time <dbl>, sector3time <dbl>,
#> # sector1session_time <dbl>, sector2session_time <dbl>,
#> # sector3session_time <dbl>, speed_i1 <dbl>, speed_i2 <dbl>, speed_fl <dbl>,
#> # speed_st <dbl>, …
load_circuit_details(2023, 4)
This function loads circuit details for a specific race session. Note that different track layouts are used at some circuits depending on the year of the race. Useful for visualizing or annotating data. Contains information on corners, marshal_lights and marshal_sectors.
plot_fastest(season = "current", round = "last", session = "R", driver, color = "gear")
A built in plotting function that plots the circuit and a driver’s
fastest laps’ speed or gear exists.
plot_fastest(season = 2023, round = 1, session = "R", driver = "VER", color = "gear")
Two helper functions exist as well. The first, theme_dark_f1() assists
with colour schemes similar to that used in other F1 graphics. The
second, correct_track_ratio() is a function that fixes track ratio
issues that appear when you create images similar to that above from
plot_fastest(). Please refer to their documentation for usage.
The package echos the metadata information look-up from the FastF1 package. this is a convenient way to programmatically look up drivers, teams, driver-team relationships, team colors, driver colors, tire types & colors and more. See the following functions for this look-up:
get_driver_abbreviation()get_driver_color()get_driver_color_map()get_driver_colour()get_driver_colour_map()get_driver_name()get_driver_style()get_driver_telemetry()get_drivers_by_team()get_session_drivers_and_teams()get_team_by_driver()get_team_color()get_team_colour()get_team_name()get_tire_compounds()
Note that (in support of plotting functions) driver colors and marker
type / line style can be retrieved from get_driver_style(). The
function get_driver_color() will return the same color value for both
drivers in a team.
The cache directory for sessions can be set manually with the options function
options(f1dataR.cache = "path/to/directory")Many other functions exist, and are flexible enough to call the current
season with the string "current" or use the year as a numeric value.
Similarly, round can be "last" or a round number (from 1 to the
total number of races in a season).
load_constructors()load_drivers(season = "current")load_circuits(season = "current")load_pitstops(season = "current", round = "last")load_quali(season = "current", round = "last")load_results(season = "current", round = "last")load_schedule(season =2024)load_sprint(season = "current", round = "last")load_standings(season = "current", round = "last", type = c("driver", "constructor"))
clear_f1_cache()
Clears the cache for all functions in the package.