library(nascaR.data)
The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
2024-06-23
NASCAR is one of the top-tier racing sports in North America and competes against F1 and IndyCar for the top viewership spot. Approximately 3.22 million people watch a race on any given weekend throughout the season. The nascaR.data
package is the result of wanting to share a passion for the sport and provide an option to the typical go-to packages when learning new data visualization tools.
nascaR.data
is packed full of NASCAR results dating back to the first Daytona Beach race in 1949! Use this package to discover race trends across the NASCAR Cup Series, Xfinity Series, and Craftsman Truck Series. Answer fun questions like “which driver has accumulated the most wins overall?”, “which owner has the best top 10 percentage at Daytona?”, or see which manufacturer has dominated which series in a certain season. It’s all here, so let’s strap in to our race seats, fire up those engines, and let’s take some warm-up laps.
nascaR.data
provides access to 21 different datasets (7 per series) and are broken down by overall race results and driver, owner, and manufacturer season & career records. Let’s check our gauges and see what’s under the hood:
library(nascaR.data)
Use ?nascaR.data::cup_race_data
to view a list of variable descriptions. This package has been designed to swap cup
for xfinity
or truck
to see the same data structure (variables) for the respective series. Would you rather inspect driver-specific results listed by season or their overall career? No problem… this is an easy pit stop: cup_driver_career
or xfinity_owner_season
or truck_mfg_overall
.
Note
Manufacturer (
mfg
) results use the term “overall” instead of “career” as is used for driver and owner.
cup_mfg_overall
vscup_driver_career
Which drivers are in the Top 5 for wins in the NASCAR Cup Series?
First, organize the drivers in descending order by win. Then, subset to keep the Top 5 winningest drivers. Lastly, feed the data into a horizontal bar chart (some other tweaks will be applied to enhance the visual output).
|>
cup_driver_career arrange(desc(career_wins)) |>
slice_head(n = 5) |>
ggplot(aes(driver, career_wins)) +
geom_bar(stat = 'identity') +
coord_flip()
Wow! This doesn’t even look like a close race. Richard Petty clearly leads the field with 200 wins. However, let’s take a drive a little deeper into the turn and account for the number of races each driver competed in. What if we compare these same five drivers by win percentage?
|>
cup_driver_career arrange(desc(career_wins)) |>
slice_head(n = 5) |>
ggplot(aes(driver, career_win_pct)) +
geom_bar(stat = 'identity') +
coord_flip()
Accounting for Richard Petty’s 1185 races to David Pearson’s 575 races, imagine how many more wins Pearson would have if he competed in as many races as The King.
Which manufacturer has the best win percentage by season?
Let’s go behind the pits and see what the manufacturers are up to in the Truck Series.
|>
truck_mfg_season ggplot(aes(season, mfg_season_win_pct, group = manufacturer, color = manufacturer)) +
geom_line() +
geom_point()
No clear trend emerges, though it appears that there may be a 5-year clustering of winning percentage. For example, the Dodges experienced success in the early 2000s, but started to fall off before exiting the series. And while Ford has seemingly had gradual improvement, you can clearly see the success of the Toyota camp since joining the Truck series in 2004.
Collect your race winnings
How has the average money for winning a race changed over time?
<- cup_race_data |>
cup mutate(series = 'Cup') |>
filter(finish == 1) |>
select(season, race, finish, money, series)
<- xfinity_race_data |>
xfinity mutate(series = 'Xfinity') |>
filter(finish == 1) |>
select(season, race, driver, money, series)
<- truck_race_data |>
truck mutate(series = 'Truck') |>
filter(finish == 1) |>
select(season, race, driver, money, series)
bind_rows(cup, xfinity, truck) |>
group_by(series, season) |>
summarize(mean_money = mean(money, na.rm = TRUE)) |>
ggplot(aes(season, mean_money, group = series, color = series)) +
geom_point() +
geom_line()
Race winnings in the Cup series experienced exponential growth beginning in the 1980s while Xfinity and Truck Series winnings have remained relatively the same since 2000.
I hope this gives you a little taste of what is included in this package. There’s plenty of opportunity to further clean and reshape the data for data visualizations or model prepping. I’ll be adding more data throughout the season.
nascaR.data
was built with R version 4.4.0 with the tidyverse
(2.0.0), rUM
(2.0.0), and ggtext
(0.1.2) packages used to preprocess and summarize data.1–5
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.