fec16

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

fec16 is hosted on GitHub and call be installed by running the following:

devtools::install_github("baumer-lab/fec16")

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 presidential election
results_senate: the senate results of the 2016 general presidential election
results_president: the final results of the 2016 general presidential election
pac: Political Action Committee (PAC) and party summary financial information

Sample Datasets (with 1000 rows each)

individuals: individual contributions to candidates/committees during the 2016 general presidential election
contributions: candidates and their contributions from committees during the 2016 general election
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

Note: When these functions are run in the console, it is helpful to know the progress of the download taking place but running these functions in a R Markdown document leads to the progress bar of the download being printed repeatedly. A possible solution is to wrap the function in invisible(utils::capture.output()) hereby suppressing the messages.

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                   1258
#> 2 REP                   1481

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"
  )