AE 05: Gerrymandering + data exploration II

Getting started

Packages

We’ll use the tidyverse package for this analysis.

library(tidyverse)
library(usdata)
library(ggbeeswarm)

Data

The data are availale in the usdata package.

glimpse(gerrymander)

Rows: 435
Columns: 12
$ district   <chr> "AK-AL", "AL-01", "AL-02", "AL-03", "AL-04", "AL-05", "AL-0…
$ last_name  <chr> "Young", "Byrne", "Roby", "Rogers", "Aderholt", "Brooks", "…
$ first_name <chr> "Don", "Bradley", "Martha", "Mike D.", "Rob", "Mo", "Gary",…
$ party16    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R", "R", "R",…
$ clinton16  <dbl> 37.6, 34.1, 33.0, 32.3, 17.4, 31.3, 26.1, 69.8, 30.2, 41.7,…
$ trump16    <dbl> 52.8, 63.5, 64.9, 65.3, 80.4, 64.7, 70.8, 28.6, 65.0, 52.4,…
$ dem16      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,…
$ state      <chr> "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AR", "AR",…
$ party18    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R", "R", "R",…
$ dem18      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,…
$ flip18     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ gerry      <fct> mid, high, high, high, high, high, high, high, mid, mid, mi…

Congressional districts per state

Which state has the most congressional districts? How many congressional districts are there in this state?

gerrymander |>
  count(state, sort = TRUE)

# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

Gerrymandering and flipping

Is a Congressional District more likely to be flipped to a Democratic seat if it has high prevalence of gerrymandering or low prevalence of gerrymandering? Support your answer with a visualization and summary statistics.

gerrymander |>
  mutate(flip18 = as.factor(flip18)) |>
  ggplot(aes(x = gerry, fill = flip18)) +
  geom_bar(position = "fill")

gerrymander |>
  count(gerry, dem18) |>
  group_by(gerry) |>
  mutate(p = n / sum(n))

# A tibble: 6 × 4
# Groups:   gerry [3]
  gerry dem18     n     p
  <fct> <dbl> <int> <dbl>
1 low       0    25 0.403
2 low       1    37 0.597
3 mid       0   131 0.485
4 mid       1   139 0.515
5 high      0    52 0.505
6 high      1    51 0.495

Aesthetic mappings

Recreate the following visualization, and then improve it.

ggplot(gerrymander, aes(x = gerry, y = clinton16)) +
  geom_beeswarm(color = "gray50", alpha = 0.5) +
  geom_boxplot(aes(color = gerry), alpha = 0.5) +
  theme_minimal()

Render, commit, and push

Render your Quarto document.
Go to the Git pane and check the box next to each file listed, i.e., stage your changes. Commit your staged changes using a simple and informative message.
Click on push (the green arrow) to push your changes to your application exercise repo on GitHub.
Go to your repo on GitHub and confirm that you can see the updated files. Once your updated files are in your repo on GitHub, you’re good to go!