piracy: Piracy and PIPA/SOPA

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

Piracy and PIPA/SOPA

Description

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.

Usage

piracy

Format

A data frame with 534 observations on the following 8 variables.

name

Name of legislator.

party

Party affiliation as democrat (D), Republican (R), or Independent (I).

state

Two letter state abbreviation.

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.

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.

years

Number of years of service in Congress.

stance

Degree of support for PIPA/SOPA with levels Leaning No, No, Undecided, Unknown, Yes

chamber

Whether the legislator is a member of either the house or senate.

Details

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 money_pro and money_con, read the documentation on ProPublica, linked below.

Source

https://projects.propublica.org/sopa The list may be slightly out of date since many politician's perspectives on the legislation were in flux at the time of data collection.

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"
  )

OpenIntroStat/openintro documentation built on June 4, 2024, 4:19 a.m.