Basics
f1dataR serves as a tool to get neatly organized Formula
1 data into your R environment. Here we will go over the basic functions
to understand how the package works.
The most sought-after aspect of F1 data is telemetry data. Let’s get
Leclerc’s fastest lap from the first race of 2022:
library(f1dataR)
load_driver_telemetry(2022, 1, "Q", driver = "LEC", laps = "fastest")
#> # A tibble: 697 × 19
#> date session_time time rpm speed n_gear throttle brake drs source
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl> <chr>
#> 1 2022-03-19 15:58:18 4397. 0 10514 292 7 100 FALSE 12 interpolation
#> 2 2022-03-19 15:58:18 4397. 0.084 10502 293 7 100 FALSE 12 pos
#> 3 2022-03-19 15:58:18 4398. 0.152 10478 294 8 100 FALSE 12 car
#> 4 2022-03-19 15:58:18 4398. 0.384 10519 295 8 100 FALSE 12 pos
#> 5 2022-03-19 15:58:18 4398. 0.392 10560 296 8 100 FALSE 12 car
#> 6 2022-03-19 15:58:19 4398. 0.784 10628 297 8 100 FALSE 12 pos
#> 7 2022-03-19 15:58:19 4398. 0.792 10696 299 8 100 FALSE 12 car
#> 8 2022-03-19 15:58:19 4398. 0.952 10696 300 8 100 FALSE 12 car
#> 9 2022-03-19 15:58:19 4398. 1.02 10734 301 8 100 FALSE 12 pos
#> 10 2022-03-19 15:58:19 4399. 1.32 10773 302 8 100 FALSE 12 pos
#> # ℹ 687 more rows
#> # ℹ 9 more variables: relative_distance <dbl>, status <chr>, x <dbl>, y <dbl>, z <dbl>,
#> # distance <dbl>, driver_ahead <chr>, distance_to_driver_ahead <dbl>, driver_code <chr>
Now let’s use ggplot2 to visualize some of the data we have
library(dplyr)
library(ggplot2)
lec <- load_driver_telemetry(2022, 1, "Q", driver = "LEC", laps = "fastest") %>%
head(300)
ggplot(lec, aes(distance, throttle)) +
geom_line() +
theme_minimal()
What if we get more drivers involved. Let’s also get the Qualifying
data from Hamilton and Pérez
ham <- load_driver_telemetry(2022, 1, "Q", driver = "HAM", laps = "fastest") %>%
head(300)
per <- load_driver_telemetry(2022, 1, "Q", driver = "PER", laps = "fastest") %>%
head(300)
data <- bind_rows(lec, ham, per)
ggplot(data, aes(distance, throttle, color = driver_code)) +
geom_line() +
theme_minimal()
Integrated plotting
There are a couple of functions in the package that help with
plotting. The first one is theme_dark_f1() that simply
applies a theme similar to the official F1 graphics. We can apply it to
our previous data.
ggplot(data, aes(distance, speed, color = driver_code)) +
geom_line() +
theme_dark_f1(axis_marks = TRUE) +
theme(
axis.title = element_text(),
axis.line = element_line(color = "white"),
) +
labs(
title = "Speed Comparison for Bahrain 2022"
)
Another built-in function is plot_fastest() that can
plot the speed or gear changes throughout the fastest lap for a
driver/race.
plot_fastest(2022, 1, "R", "PER")
#> ℹ If the session has not been loaded yet, this could take a minute
Combining several functions
Now let’s look at a more complete analysis. We want to visualize how
lap time change over time (tyre age) for Pérez with every compound used
in the Spanish GP.
laps <- load_session_laps(2023, "Spain") %>%
filter(driver == "PER") %>%
group_by(compound) %>%
# Remove in and out laps
filter(tyre_life != 1 & tyre_life != max(tyre_life)) %>%
ungroup()
ggplot(laps, aes(tyre_life, lap_time, color = compound)) +
geom_line() +
geom_point() +
theme_dark_f1(axis_marks = TRUE) +
labs(
color = "Tyre Compound",
y = "Lap Time (Seconds)",
x = "Tyre Life (Laps)"
) +
scale_color_manual(
values = c("white", "yellow", "red")
) +
scale_y_continuous(breaks = seq(75, 85, 1)) +
scale_x_continuous(breaks = seq(2, 26, 4))
Now let’s visualize the portion of the track where Verstappen had the
throttle 100% open in the 2023 Canadian GP. Note that we’ll pass the
plot through the helper function correct_track_ratio() to
ensure the plotted track has correct dimensions (and a few other tweaks
for pretty plotting). Alternatively, you can call
ggplot2::coord_fixed() while building track plots to ensure
the x & y ratios are equal.
ver_can <- load_driver_telemetry(
season = 2023,
round = "Canada",
driver = "VER",
laps = "fastest"
) %>%
mutate(open_throttle = ifelse(throttle == 100, "Yes", "No"))
throttle_plot <- ggplot(ver_can, aes(x, y, color = as.factor(open_throttle), group = NA)) +
geom_path(linewidth = 4, lineend = "round") +
scale_color_manual(values = c("white", "red")) +
theme_dark_f1() +
labs(
title = "Verstappen Flat Out",
subtitle = "Canada 2023 | Fastest Lap",
color = "Throttle 100%"
)
correct_track_ratio(throttle_plot)
For a simpler visualization let’s look at the average time it took
each team to pit in round 4 of 2023. For this we will have to load the
pit data, the results data (to extract driver + team combos), and read
the constructor data to get the colors for our plot. Note the time is
the difference from pit entry to exit, not stopped time.
pit_data <- load_pitstops(2023, 4)
driver_team <- load_results(2023, 4) %>%
select(driver_id, constructor_id)
pit_constructor <- pit_data %>%
left_join(driver_team, by = "driver_id") %>%
group_by(constructor_id) %>%
summarise(pit_time = mean(as.numeric(duration)))
pit_constructor$constructor_color <- sapply(pit_constructor$constructor_id, get_team_color, season = 2023, round = 4, USE.NAMES = FALSE)
pit_constructor$team_name <- sapply(pit_constructor$constructor_id, get_team_name, season = 2023, short = TRUE, USE.NAMES = FALSE)
ggplot(pit_constructor, aes(x = team_name, y = pit_time, fill = team_name)) +
geom_bar(stat = "identity", fill = pit_constructor$constructor_color) +
theme_dark_f1(axis_marks = TRUE) +
theme(
legend.position = "none"
) +
labs(
x = "Constructor",
y = "Average Pit Time (seconds)"
) +
ggtitle("Average time in pit lane for each team", subtitle = "Azerbaijan Grand Prix 2023")
