dragracer: Data Sets for RuPaul’s Drag Race

{dragracer} is an R package of data sets for all available seasons of RuPaul’s Drag Race, excluding All Stars. It’s updated at the end of each season. I have a blog post that describes these data in some detail while also showcasing some of the things you can do with the data I provide. I offer this R package for two reasons. First, the fandom for this show is large and there is a discernible subset of the fandom that is interested in data science and the R programming language. I offer this package as a collection of accessible data with which they can experiment. I also offer this as a love letter of a kind to RuPaul’s Drag Race and all the contestants that have appeared on it. We have no choice but to “stan”, especially Jinkx Monsoon. Jinkx is the GOAT. The conspicuously short Season 8 masks Bob’s relative dominance. Sasha Velour is Champaign-Urbana’s greatest export since REO Speedwagon. Kameron Michaels should’ve won Season 10.

Installation

This package is now on CRAN. You should be able to install it as follows.

install.packages("dragracer")

The developmental version may have more goodies. You can install the development version from Github via the {devtools} package. I suppose using the {remotes} package would work as well.

devtools::install_github("svmiller/dragracer")

Usage

The data right now contain three data sets. rpdr_ep is episode-level data for all available seasons. These include observations about when the episode aired, the number of queens in the episode, mini-challenge winners, who appeared in the bottom for a given episode, and who was sent home. rpdr_contestants is a data frame of contestant-level information, including the contestant’s age, purported date of birth, hometown, and how they fared across all episodes in their particular season. rpdr_contep is episode-contestant-level data about how each contestant fared in a particular episode in which they were. You can read more through this command.

data(package = "dragracer")

The ensuing output will look like this.

Here is a simple scraping job to provide more information (by way of the description field in the associated R Documentation file).