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"
)
See Also
If you are interested in political data, check out the following related packages: