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nascaR.data is a curated group of datasets across NASCAR’s top three series: Cup, Xfinity, and Trucks. There are 21 sets available to explore & use for creating tables or other data visualizations.
Install nascaR.data with:
remotes::install_github('kyleGrealis/nascaR.data')or use the official CRAN release:
install.packages('nascaR.data')Project updates:
This package is a collection of NASCAR race, driver, owner and manufacturer data across the three major NASCAR divisions: NASCAR Cup Series, NASCAR Xfinity Series, and NASCAR Craftsman Truck Series. The curated data begins with the 1949 season and extends through the end of the 2023 season. Data was sourced with permission from DriverAverages.com.
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:
{r, echo=TRUE} 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.
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).
{r, echo=TRUE, eval=FALSE, warning=FALSE} 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?
{r, echo=TRUE, eval=FALSE, warning=FALSE} cup_driver_career |> arrange(desc(career_wins)) |> slice_head(n = 5) |> ggplot(aes(driver, career_win_pct)) + geom_bar(stat = 'identity') + coord_flip()
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.
{r, eval=FALSE, warning=FALSE} 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?
```{r, eval=FALSE,, warning=FALSE} cup <- cup_race_data |> mutate(series = ‘Cup’) |> filter(finish == 1) |> select(season, race, finish, money, series)
xfinity <- xfinity_race_data |> mutate(series = ‘Xfinity’) |> filter(finish == 1) |> select(season, race, driver, money, series)
truck <- truck_race_data |> 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.
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.