R/data-gerrymander.R

In usdata: Data on the States and Counties of the United States

#' Gerrymander
#'
#' A dataset on gerrymandering and its influence on House elections. The data set was originally built by Jeff Whitmer.
#'
#' @name gerrymander
#' @docType data
#' @format A data frame with 435 rows and 12 variables:
#' \describe{
#'   \item{district}{Congressional district.}
#'   \item{last_name}{Last name of 2016 election winner.}
#'   \item{first_name}{First name of 2016 election winnner.}
#'   \item{party16}{Political party of 2016 election winner.}
#'   \item{clinton16}{Percent of vote received by Clinton in 2016 Presidential Election.}
#'   \item{trump16}{Percent of vote received by Trump in 2016 Presidential Election.}
#'   \item{dem16}{Did a Democrat win the 2016 House election. Levels of 1 (yes) and 0 (no).}
#'   \item{state}{State the Representative is from.}
#'   \item{party18}{Political Party of the 2018 election winner.}
#'   \item{dem18}{Did a Democrat win the 2018 House election. Levels of 1 (yes) and 0 (no).}
#'   \item{flip18}{Did a Democrat flip the seat in the 2018 election? Levels of 1 (yes) and 0 (no).}
#'   \item{gerry}{Categorical variable for prevalence of gerrymandering with levels of low, mid and high.}
#' }
#' @source [Washington Post](https://www.washingtonpost.com/news/wonk/wp/2014/05/15/americas-most-gerrymandered-congressional-districts/)
#' @keywords datasets
#'
#' @examples
#' library(ggplot2)
#' library(dplyr)
#' ggplot(gerrymander |> filter(gerry != "mid"), aes(clinton16, dem16, color = gerry)) +
#'   geom_jitter(height = 0.05, size = 3, shape = 1) +
#'   geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) +
#'   scale_color_manual(values = c("purple", "orange")) +
#'   labs(
#'     title = "Logistic Regression of 2016 House Elections",
#'     subtitle = "by Congressional District",
#'     x = "Percent of Presidential Vote Won by Clinton",
#'     y = "Seat Won by Democrat Candidate",
#'     color = "Gerrymandering"
#'   )
"gerrymander"