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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

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
states: geographical information about the 50 states

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

How is the data relational?

The headers of each table show the dataset name. The underlined variables are primary keys while all the others are foreign keys. The arrows show how the datasets are connected.

The diagram is built using the dm R package. The code can be found in data-raw/dm.R.

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 × 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.