fec16

Data Package for the 2016 United States Federal Elections


License
CC0-1.0

Documentation

fec16

Lifecycle: experimental CRAN status Travis-CI Build Status CRAN RStudio mirror downloads

fec16 contains data from the Federal Election Commission (FEC) website pertaining to candidates, committees, results, contributions from committees and individuals, and other financial data for the United States 2015-2016 election cycle. Additionally, for the datasets that are included as samples, the package includes functions that import the full versions.

Installation

Get the latest released version from CRAN:

install.packages("fec16")

Or the development version from GitHub:

# If you haven't installed the remotes package yet, do so:
# install.packages("remotes")
remotes::install_github("baumer-lab/fec16")
# Load package
library(fec16)

Datasets Included

Full Datasets

  • candidates: candidates registered with the FEC during the 2015-2016 election cycle
  • committees: committees registered with the FEC during the 2015-2016 election cycle
  • campaigns: the House/Senate current campaigns
  • results_house: the House results of the 2016 general election
  • results_senate: the Senate results of the 2016 general election
  • results_president: the final results of the 2016 general election
  • pac: Political Action Committee (PAC) and party summary financial information

Sample Datasets (with 1000 random rows each)

  • individuals: individual contributions to candidates/committees during the 2016 election cycle
  • contributions: candidates and their contributions from committees during the 2016 election cycle
  • expenditures: the operating expenditures
  • transactions: transactions between committees

Functions Included

The following functions retrieve the entire datasets for the sampled ones listed above. The size of the raw file that is downloaded by calling each function is given for reference. All functions have an argument n_max which defaults to the entire dataset but the user can specify the max length of the dataset to be loaded via this argument.

  • read_all_individuals() ~ 1.45GB
  • read_all_contributions() ~ 15.4MB
  • read_all_expenditures() ~ 52.1MB
  • read_all_transactions() ~ 79.2MB

Examples

Data Wrangling

fec16 can be used to summarize data in order see how many candidates are running for elections (in all offices) for the two major parties:

library(dplyr)

data <- candidates %>%
  filter(cand_pty_affiliation %in% c("REP", "DEM")) %>%
  group_by(cand_pty_affiliation) %>%
  summarize(size = n())

data
#> # A tibble: 2 x 2
#>   cand_pty_affiliation  size
#>   <chr>                <int>
#> 1 DEM                   1270
#> 2 REP                   1495

Data Visualization

We can visualize the above data:

library(ggplot2)

ggplot(data, aes(x = cand_pty_affiliation, y = size, fill = cand_pty_affiliation)) +
  geom_col() +
  labs(
    title = "Number of Candidates Affiliated with the Two Major Parties",
    x = "Party", y = "Count", fill = "Candidate Party Affiliation"
  )

Contributors