fec12 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 2011-2012 election cycle. Additionally, for the datasets that are included as samples, the package includes functions that import the full versions.
Please see fec16, a similar package that is on CRAN, for more documentation and examples.
fec12
is hosted on GitHub and call be installed by running the
following:
devtools::install_github("baumer-lab/fec12")
library(fec12)
candidates
: candidates registered with the FEC during the 2011-2012 election cyclecommittees
: committees registered with the FEC during the 2011-2012 election cyclecampaigns
: the house/senate current campaignsresults_house
: the house results of the 2012 general presidential electionresults_senate
: the senate results of the 2012 general presidential electionresults_president
: the final results of the 2012 general presidential electionpac
: Political Action Committee (PAC) and party summary financial information
individuals
: individual contributions to candidates/committees during the 2012 general presidential electioncontributions
: candidates and their contributions from committees during the 2012 general electionexpenditures
: the operating expenditurestransactions
: transactions between committees
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()
~ 250.6MBread_all_contributions()
~ 12.2MBread_all_expenditures()
~ 45.4MBread_all_transactions()
~ 35.6MB
fec12
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 1180
#> 2 REP 1472
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"
)