fec16

Basics of tame data

We used the tame data principles to produce uniform data frames that are easy to navigate and link together. The following are the guidelines we used to tame our data:

1. Variable names:
   • Lower case and with underscores instead of spaces using clean_names()
   • 20 characters or less
   • The same names were used for the same variables in different data frames
   • Names to be meaningful
2. Variable types:
   • Encode variable with dates with as.Date, unless it is only a year then it would be numeric type
   • Encode categorical variables that a have a limited number of values with as.factor otherwise it would be of character type
3. Tidy or long data format instead of wide
4. Missing data has value NA
5. Removed signs such as % and $ attached to numeric values

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 presidential election
• results_senate: the senate results of the 2016 general presidential election
• results_president: the final results of the 2016 general presidential 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 general presidential election
• contributions: candidates and their contributions from committees during the 2016 general election
• expenditures: the operating expenditures
• transactions: transactions between committees

Example 1:

Which presidential candidate won a majority in more states, and how many votes did they get?

Using the results_president dataset, we can also see what actually happened in the 2016 elections.

Here is how we can summarize the number of wins by candidate:

wins <- left_join(results_president, candidates) %>%
  group_by(cand_id, cand_name) %>%
  summarise(
    total_votes = sum(general_votes, na.rm = TRUE),
    states_won = sum(won)
  ) %>%
  arrange(desc(total_votes))
head(wins)
#> # A tibble: 6 × 4
#> # Groups:   cand_id [6]
#>   cand_id   cand_name                                     total_votes states_won
#>   <chr>     <chr>                                               <dbl>      <int>
#> 1 P00003392 "CLINTON, HILLARY RODHAM / TIMOTHY MICHAEL K…    65853514         21
#> 2 P80001571 "TRUMP, DONALD J. / MICHAEL R. PENCE "           62984828         30
#> 3 P20002671 "JOHNSON, GARY / WILLIAM \"BILL\" WELD"           4489341          0
#> 4 P20003984 "STEIN, JILL"                                     1457218          0
#> 5 P60022654 "MCMULLIN, EVAN / MINDY FINN"                      731991          0
#> 6 P60021102 "CASTLE, DARRELL LANE"                             203090          0

Compare these results to those reported by the Wikipedia. We can show the results using a simple bar chart:

Each win is for a single state. There are 51 total wins for the 50 States and Washington, D.C.

ggplot(
  wins %>% head(6),
  aes(x = reorder(cand_name, total_votes), y = total_votes, fill = cand_id)
) +
  geom_col() +
  scale_fill_discrete(guide = FALSE) +
  scale_y_continuous(labels = comma) +
  labs(
    title = "2016 Presidential Election",
    subtitle = "Total votes",
    x = NULL, y = "Number of Votes"
  ) +
  coord_flip()
#> Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
#> ggplot2 3.3.4.
#> ℹ Please use "none" instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

We can see that Hillary Clinton earned the most votes.

Example 2:

What is the relationship between contributions of candidates in the house and total votes they get?

Here we investigate what kind of relationship, if any, the candidates with over a 1000 votes got with the total contributions they made. We are interested in candidates with an ID number so we can join them with the contribution data set by using inner_join.

all_contributions <- fec16::read_all_contributions()
results_by_cand <- left_join(results_house, candidates, by = "cand_id") %>%
  left_join(all_contributions, by = "cand_id") %>%
  group_by(cand_id, cand_name, cand_pty_affiliation) %>%
  summarise(
    sum_votes = sum(general_votes),
    contribution = sum(transaction_amt)
  ) %>%
  filter(sum_votes > 1000)
head(results_by_cand)

#> # A tibble: 6 × 5
#> # Groups:   cand_id, cand_name [6]
#>   cand_id   cand_name                cand_pty_affiliation sum_votes contribution
#>   <chr>     <chr>                    <chr>                    <dbl>        <dbl>
#> 1 H0AL02087 ROBY, MARTHA             REP                   63801078      1105157
#> 2 H0AL05163 BROOKS, MO               REP                   30641403       251855
#> 3 H0AL07086 SEWELL, TERRYCINA ANDREA DEM                  169016210      1249038
#> 4 H0AR01083 CRAWFORD, ERIC ALAN RICK REP                   45047170       385616
#> 5 H0AR03055 WOMACK, STEVE            REP                   73193704       568116
#> 6 H0AZ01259 GOSAR, PAUL DR.          REP                   78139008       680874

Next, we plot the contributions and votes on a scatter plot and plot a trend line that would make it easy for us to see the relationship. Since there are outliers in the data, it is best to not use a continuous axis scale in order to see all of the points.

ggplot(results_by_cand, aes(x = contribution, y = sum_votes)) +
  geom_point() +
  scale_x_log10(labels = comma) +
  scale_y_sqrt(labels = comma) +
  geom_smooth(method = "auto") +
  labs(
    title = "Contributions vs. Votes in 2016",
    x = "Contributions in US Dollars", y = "Total Votes"
  )

As we can see, the highest contributors got the highest amount of votes so it has a positive correlation, but then it falls after a large contribution amount.

Example 3:

What is the distribution of number of votes across winners in the US House of Representatives?

Visualize the results of the popular vote in the elections and see how many people voted:

house_winners <- left_join(results_house, candidates, by = "cand_id") %>%
  mutate(party_1 = str_sub(party, 1, 1)) %>%
  filter(won, state %in% state.abb) %>%
  group_by(cand_id, cand_name, party_1) %>%
  summarize(
    total_votes = sum(general_votes),
    total_pct = sum(general_percent),
  )

ggplot(
  house_winners,
  aes(
    x = total_votes, y = total_pct,
    color = party_1
  )
) +
  geom_point() +
  labs(
    title = "Not all Congressional Races are the same",
    fill = "Candidate", x = "Total Votes", "Total Percent"
  ) +
  scale_y_continuous(labels = comma)