fec16

fec16 contains relational data from the Federal Election Commission website pertaining to candidates and committees for the United States 2015-2016 election cycle. Additionally, result of the 2016 general election and contribution data both from committees and individuals are included.

Installation

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

devtools::install_github("ranawg/fec16")

library(fec16)

Data

• candidates: all candidates registered with the FEC during the 2015-2016 election cycle
• committees: all committees registered with the FEC during the 2015-2016 election cycle
• results: the results of the 2016 general presidential election
• individuals: a sample of 5000 individual contributions to candidates/committees during the primary and general 2016 elections
• committee_contributions: total contributions, aggregated by candidate, from committees

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(fec16)
library(tidyverse)

candidates %>%
  filter(cand_pty_aff == "REP" | cand_pty_aff == "DEM") %>%
  group_by(cand_pty_aff) %>%
  summarise(size = n())
#> # A tibble: 2 x 2
#>   cand_pty_aff  size
#>   <fct>        <int>
#> 1 DEM           2242
#> 2 REP           2678

Joining Data

We can join any of the datasets using cand_id. Each dataset with the exception of the individuals dataset contains a possible joining key: cand_id.

Here is an example of calculating how many candidates are in each of the two major parties: Democratic (DEM) and Republican (REP), based on their committee type:

cand_cmte <- candidates %>%
  full_join(committees, by = "cand_id") %>%
  filter(cand_pty_aff == "REP" | cand_pty_aff == "DEM") %>%
  group_by(cand_pty_aff, committee_type) %>%
  summarise(n = n()) %>%
  drop_na(committee_type)
head(cand_cmte)
#> # A tibble: 6 x 3
#> # Groups:   cand_pty_aff [109]
#>   cand_pty_aff committee_type     n
#>   <fct>        <fct>          <int>
#> 1 DEM          H               1540
#> 2 DEM          P                147
#> 3 DEM          S                278
#> 4 REP          H               1715
#> 5 REP          P                218
#> 6 REP          S                399

Data Visualization

And extending that to create a visualization to see the results easily.

ggplot(cand_cmte, aes(x = committee_type, y = n, fill = cand_pty_aff)) + 
  geom_col(position = "dodge") +
  labs(title = "Bar Chart of Total Committees by Type and Party", 
       x = "Committee Type", y = "Count", fill = "Candidate Party Affiliation")

Contributors

• Irene Ryan
• Marium Tapal
• Rana Gahwagy