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fec16 contains data from theFederal Election Commission(FEC) website pertaining to candidates,committees, results, contributions from committees and individuals, andother financialdata for the United States 2015-2016 electioncycle.Additionally, for the datasets that are included as samples, the packageincludes functions that import the full versions.

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 packagelibrary(fec16)

• candidates: candidates registered with the FEC during the 2015-2016election cycle
• committees: committees registered with the FEC during the 2015-2016election 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 financialinformation
• states: geographical information about the 50 states

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

The following functions retrieve the entire datasets for the sampledones listed above. The size of the raw file that is downloaded bycalling each function is given for reference. All functions have anargumentn_max which defaults to the entire dataset but the user canspecify 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

The headers of each table show the dataset name. The underlinedvariables areprimary keys while all the others areforeignkeys. The arrows show how the datasets are connected.

The diagram is built using thedm R package. The code can be found indata-raw/dm.R.

fec16 can be used to summarize data in order see how many candidatesare 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

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"  )

If you are interested in political data, check out the following relatedpackages:

• politicaldata
• ProPublicaR

• Marium Tapal
• Irene Ryan
• Rana Gahwagy
• Benjamin S. Baumer