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Ingesting NASCAR Data

Source: vignettes/ingesting-nascar-data.Rmd
ingesting-nascar-data.Rmd

The nascaR.data package hosts its canonical race results datasets publicly on Cloudflare R2. While R users can leverage load_series(), non-R users or developers building external pipelines can access these datasets directly in both Parquet and CSV formats.

Direct Download URLs

All datasets are updated every Monday at 5:00 AM EST during the racing season.

Parquet is highly recommended as it preserves exact column data types and has a significantly smaller file size.

  • Cup Series: https://nascar.kylegrealis.com/cup_series.parquet
  • NXS Series: https://nascar.kylegrealis.com/nxs_series.parquet
  • Truck Series: https://nascar.kylegrealis.com/truck_series.parquet

CSV Format

  • Cup Series: https://nascar.kylegrealis.com/cup_series.csv
  • NXS Series: https://nascar.kylegrealis.com/nxs_series.csv
  • Truck Series: https://nascar.kylegrealis.com/truck_series.csv

Command-Line Download

You can download any of these files directly using curl or wget:

# Download Cup Series CSV results
curl -O https://nascar.kylegrealis.com/cup_series.csv

# Download NXS Series Parquet results
curl -O https://nascar.kylegrealis.com/nxs_series.parquet

Critical Ingestion Note: NASCAR Car Numbers

When ingesting the CSV files, pay special attention to the Car (car number) column.

In NASCAR, car numbers can contain leading zeros (e.g., "08", "09"), which represent entirely different teams and entries from single-digit numbers (e.g., "8", "9").

By default, most CSV parsers (including R’s read.csv() and Python’s pandas.read_csv()) guess column types based on initial rows and will parse the Car column as an integer. This strips leading zeros, incorrectly converting "08" to 8.

Correct Ingestion in R

When reading the CSV file in R, explicitly specify that the Car column should be parsed as a character string:

# Base R
cup <- read.csv(
  "cup_series.csv",
  colClasses = c(Car = "character"),
  stringsAsFactors = FALSE
)

# tidyverse / readr
cup <- readr::read_csv(
  "cup_series.csv",
  col_types = readr::cols(Car = readr::col_character())
)

Correct Ingestion in Python (pandas)

When reading the CSV file in Python, use the dtype argument to treat the Car column as a string:

import pandas as pd

# Load CSV and preserve leading zeros
cup = pd.read_csv("cup_series.csv", dtype={"Car": str})

By specifying the type as a string/character, you ensure the car numbers remain accurate for historical roster matching.