library(tidyverse)
library(dplyr)
library(tidytuesdayR)
library(lubridate)
library(readr)Project 1: Co-Medians
Project 1 Proposal
Dataset
## read data
static_list <-readRDS("data/static_list.RDS")
contribution_data_all_states <-readRDS("data/contribution_data_all_states.RDS")The datasets we are using are from TidyTuesday [data] and come from the website Data For Progress. For context, Data For Progress is a think tank and polling firm that provides data for progressive campaigns, causes, and candidates. Their work has been used by US representatives, senators, and local leaders, and it has been cited by the White House. The datasets given were static_list, contribution_data_all_states, pride_aggregates, fortune_aggregates, pride_sponsors, donors, and corp_by_politician. After looking into the datasets further, we realized that there was overlap of information in static_list, fortune_aggregates, and pride_aggregates, where static_list captured the most complete information from the other two combined, so we decided to use that dataset. Our decision to also use the contribution_data_all_states dataset was made for similar reasons. In many ways it is a combination of the others in terms of information and variables, but we appreciated that it included that information by individual contribution rather than by company or politician.
The first dataset we are using is static_list, containing information on 126 companies (each row corresponds to a company) and whether they donated to pride, if they have an HRC business pledge, the dollar amount contributed across states (USD), the number of politicians contributed to, and the number of states where they made contributions. It has 6 columns/variables. A few of the variables we are using from this dataset for our analysis include Company, Pride?, HRC Business Pledge, and Amount Contributed Across States. The second dataset is contribution_data_all_states with 13210 observations with each row denoting a single donation/contribution, along with information on the company it was made by, whether or not they were a pride and sponsor match, if they were a pride event sponsor, HRC business pledge, donor name, politician donated to, state donated to, amount of donation (USD), date, citation, donor type, comments, and an archive. It has a total of 13 columns/variables. Some of the important variables we are using to answer our research questions from this dataset are Politician, Date, and Amount.
Reason for Choosing this Dataset
Overall, we thought that these datasets would be extremely interesting both to visualize and to learn about. We are all advocates for LGBTQ+ rights, and we were very intrigued by the prevalence of corporate pride sponsors who also donate to anti-LGBTQ+ campaigns. This article (https://19thnews.org/2022/06/companies-anti-lgbtq-bills-lawmakers/) outlines the fact that many companies have publicly opposed anti-LGBTQ+ bills and have pro-LGBTQ campaigns, however, some of these companies simultaneously fund lawmakers who back anti-LGBTQ+ bills. In particular, from 2021-2022, the newspaper “Popular Information” found that 25 companies who were publicly in support of the pride movement have also donated $13M to politicians who back anti-LGBTQ+ bills. We believe this is sneaky and dishonest behavior by corporations that deserves attention in order to help others become more aware of these hypocritical practices.
Questions and Analysis Plan
Question 1
The first question we want to answer using this dataset is: What is the relationship between a company’s status as a pride sponsor, whether they have an HRC pledge, and their contributions to anti-LGBTQ+ campaigns? The Human Rights Campaign (HRC) business pledge is a commitment for companies to show their clear opposition to harmful legislation aimed at restricting the access of LGBTQ people in society and the workplace. We would expect that companies who support the LGBTQ+ community would not also make anti-LGBTQ+ donations, but this is not the case. Even more so, even companies with HRC pledges make these anti-LGBTQ+ donations. Therefore, we wanted to compare company contribution amounts to uncover patterns associated with these categorizations.
Source: Human Rights Campaign
Plan for Answering Question 1
For our first question, we plan to make a bar chart visualization of the top 10 or 20 companies who contributed the most to anti-LGBTQ+ campaigns (in USD). From there, we will color the bars based on whether or not the company is a Pride sponsor, and add a pattern (ex. stripes with ggpattern) on the bar to indicate whether a company has an HRC pledge in place. We are interested in seeing which companies are donating the most money to anti-LGBTQ+ campaigns, as well as how many of the top corporate contributers are blatantly anti-LGBTQ+ vs. those that are hypocritical Pride sponsors.
For the second visualization, we want to investigate the distribution of anti-LGBTQ+ contribution amounts by company within the three categories of company: not a pride sponsor company, pride sponsor company without HRC pledge, pride sponsor company with HRC pledge. We will create a company_category variable to include these distinguishments. To accomplish this, we plan to make a density plot of the amount contributed to anti-LGBTQ+ campaigns, filled by which category of company the donation was made by. This visualization will provide insight into the distribution of contribution amounts that companies contribute to anti-LGBTQ+ campaigns across these three categories of company.
Source: Stack Overflow
Question 2
Our second question asks: how have the donations to politicians who support anti-LGBTQ+ campaigns changed over time? We are very curious as to how the total amount of money contributed to anti-LGBTQ+ politicians has changed from 2013-2022, as there has been growing discussion about human rights issues especially in the past few years and we would hope to see a decline in the amount contributed. However, we also know that outspoken homophobia and transphobia seemed to grow during and following Trump’s presidency.
Speaking of the dialogue on these topics, Greg Abbott has been receiving a lot of public attention recently as an outspoken Texas Republican. According to this data, he has received the highest number of individual donations overall, despite him only being included over the years 2019-2022. As an extension of our main question, we are also interested in further looking into donations that were made specifically to Greg Abbott and seeing how his amounts compare to overall trends across all politicians. Overall, we would hypothesize that the trend of anti-LGBTQ+ political campaign donations would decrease over time (or one would hope they do), however due to the large media presence of Greg Abbott and his strong rhetoric involving this topic, we believe he might prove a contrast to the overall trend.
Plan for Answering Question 2
First, we plan to create a line plot showing how contributions (in USD amounts) to anti-LGBTQ+ politicians has changed over time from 2013 to 2022. We feel as though this is one of the first and most basic questions that someone would have regarding data on contributions to anti-LGBTQ+ political campaigns over multiple years. To do this, we plan to plot the yearly average across all politicians of each politician’s individual yearly average USD amount received. We will add these as points (using geom_point()) with size proportional to the number of politicians donated to in that year. We also plan to create a line connecting these points (using geom_line()) that will highlight the trend over time. In an additional ggplot layer, we will single out just Greg Abbott’s yearly average amount received and plot this with another geom_line() using a different color. For this plot we will need to create the variable year by isolating year from the Date variable, create a variable for yearly_total_per_politician by calculating the total amount donated to each politician each year, create a variable num_politicians for the number of politicians donated to each year, and a variable for mean_yearly_total_per_politician by calculating the average of yearly_total_per_politician per year. For the Greg Abbott layer, we also will need to summarize his total amount received by year (which we can call yearly_abbott_total).
We are also interested in how the total USD amounts contributed have changed over time. To do this, we plan on creating a stacked bar plot with year on the x axis and total amount (USD) on the y axis. The height of each bar will indicate that year’s total money received by anti-LGBTQ+ politicians across the states included. Diving further, we still wanted to also look at Greg Abbott and how he compares to the overall amount. We decided we would utilize a stacked bar plot, coloring by Greg Abbott vs. all the other politicians except Abbott. We understand that a challenge with stacked bar plots is that it is hard to compare them when their heights are different. Thus, we will add annotations for the different proportions (in %) to make the stacked bar plots easier to interpret and to make it more clear how the contributions to Abbott compare to those of the other politicians.
We will have to create an indicator variable identifying Abbott, which we can call abbott_or_other. Then we will have to group by this indicator variable and year to summarize the total donations by year, creating a variable yearly_total. We plan to zoom into the four years of 2019-2022 specifically and visualize the total contributions to anti-LGBTQ+ political campaigns, as well as Abbott’s proportion of that amount. Also, we understand that the same states are not included in the data across all years. This will be slightly rectified by filtering for just Abbott’s years (2019-2022). Upon further exploration, we also may need to limit which states we are looking at within our plot.