R/data-piracy.R

In OpenIntroStat/openintro: Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' Piracy and PIPA/SOPA
#'
#' This dataset contains observations on all 100 US Senators and 434 of the
#' 325 US Congressional Representatives related to their support of anti-piracy
#' legislation that was introduced at the end of 2011.
#'
#' The Stop Online Piracy Act (SOPA) and the Protect Intellectual Property Act
#' (PIPA) were two bills introduced in the US House of Representatives and the
#' US Senate, respectively, to curtail copyright infringement.  The bill was
#' controversial because there were concerns the bill limited free speech
#' rights.  ProPublica, the independent and non-profit news organization,
#' compiled this dataset to compare the stance of legislators towards the
#' bills with the amount of campaign funds that they received from groups
#' considered to be supportive of or in opposition to the legislation.
#'
#' For more background on the legislation and the formulation of
#' \code{money_pro} and \code{money_con}, read the documentation on ProPublica,
#' linked below.
#'
#' @name piracy
#' @docType data
#' @format A data frame with 534 observations on the following 8 variables.
#' \describe{
#'   \item{name}{Name of legislator.}
#'   \item{party}{Party affiliation as democrat (\code{D}), Republican (\code{R}), or Independent (\code{I}).}
#'   \item{state}{Two letter state abbreviation.}
#'   \item{money_pro}{Amount of money in dollars contributed to the legislator's campaign in 2010 by groups generally thought to be supportive of PIPA/SOPA: movie and TV studios, record labels.}
#'   \item{money_con}{Amount of money in dollars contributed to the legislator's campaign in 2010 by groups generally thought to be opposed to PIPA/SOPA: computer and internet companies.}
#'   \item{years}{Number of years of service in Congress.}
#'   \item{stance}{Degree of support for PIPA/SOPA with levels \code{Leaning No}, \code{No}, \code{Undecided}, \code{Unknown}, \code{Yes}}
#'   \item{chamber}{Whether the legislator is a member of either the \code{house} or \code{senate}.}
#' }
#' @source \url{https://projects.propublica.org/sopa}
#' The list may be slightly out of date since many politician's perspectives on
#' the legislation were in flux at the time of data collection.
#' @keywords Congress SOPA PIPA copyright infringement legislation datasets
#' @examples
#'
#' library(dplyr)
#' library(ggplot2)
#'
#' pipa <- filter(piracy, chamber == "senate")
#'
#' pipa |>
#'   group_by(stance) |>
#'   summarise(money_pro_mean = mean(money_pro, na.rm = TRUE)) |>
#'   ggplot(aes(x = stance, y = money_pro_mean)) +
#'   geom_col() +
#'   labs(
#'     x = "Stance", y = "Average contribution, in $",
#'     title = "Average contribution to the legislator's campaign in 2010",
#'     subtitle = "by groups supportive of PIPA/SOPA (movie and TV studios, record labels)"
#'   )
#'
#' ggplot(pipa, aes(x = stance, y = money_pro)) +
#'   geom_boxplot() +
#'   labs(
#'     x = "Stance", y = "Contribution, in $",
#'     title = "Contribution by groups supportive of PIPA/SOPA",
#'     subtitle = "Movie and TV studios, record labels"
#'   )
#'
#' ggplot(pipa, aes(x = stance, y = money_con)) +
#'   geom_boxplot() +
#'   labs(
#'     x = "Stance", y = "Contribution, in $",
#'     title = "Contribution by groups opposed to PIPA/SOPA",
#'     subtitle = "Computer and internet companies"
#'   )
#'
#' pipa |>
#'   filter(
#'     money_pro > 0,
#'     money_con > 0
#'   ) |>
#'   mutate(for_pipa = ifelse(stance == "yes", "yes", "no")) |>
#'   ggplot(aes(x = money_pro, y = money_con, color = for_pipa)) +
#'   geom_point() +
#'   scale_color_manual(values = c("gray", "red")) +
#'   scale_y_log10() +
#'   scale_x_log10() +
#'   labs(
#'     x = "Contribution by pro-PIPA groups",
#'     y = "Contribution by anti-PIPA groups",
#'     color = "For PIPA"
#'   )
"piracy"