election_wfh.R

library(tidycensus)
library(tidyverse)

fips_lookup <- fips_codes %>%
  distinct(state, state_code)

cd_wfh <- get_acs(
  geography = "congressional district",
  variables = "DP03_0024P",
  year = 2021,
  survey = "acs1"
) %>%
  mutate(state_code = str_sub(GEOID, 1, 2)) %>%
  left_join(fips_lookup, by = "state_code") %>%
  mutate(district = paste0(state, "-", str_sub(GEOID, 3, 4))) %>%
  mutate(district = str_replace(district, "00", "AL"))

# Source: https://docs.google.com/spreadsheets/d/1XbUXnI9OyfAuhP5P3vWtMuGc5UJlrhXbzZo3AwMuHtk/edit#gid=0
election <- read_csv("~/Downloads/Daily Kos Elections 2012, 2016 & 2020 presidential election results for congressional districts used in 2020 elections - Results.csv", skip = 1)

election20 <- election[,1:5] %>%
  set_names(c("district", "incumbent", "party", 
              "biden", "trump")) %>%
  mutate(party = str_remove(party, "[(]"), 
         party = str_remove(party, "[)]"))

wfh_election <- cd_wfh %>%
  select(district, estimate) %>%
  inner_join(election20, by = "district")

sysfonts::font.add.google("Roboto", "Roboto")

ggplot(wfh_election, aes(x = biden, y = estimate, color = party)) + 
  geom_point(size = 3, alpha = 0.7) + 
  theme_minimal(base_size = 14, base_family = "Roboto") + 
  scale_color_manual(values = c("blue", "red")) + 
  labs(x = "% Biden vote share, 2020",
       y = "% working from home, 2021 ACS",
       color = "Party of congressional\nrepresentative",
       caption = "Data sources: 2021 1-year ACS, Daily Kos (election data)\ntidycensus R package | @kyle_e_walker")