migration_presidential.R

# Requires tidycensus dev version
# remotes::install_github("walkerke/tidycensus")

library(tidyverse)
library(tidycensus)
library(showtext)
font_add_google("Montserrat")
showtext_auto()

pres_results <- read_csv("https://raw.githubusercontent.com/tonmcg/US_County_Level_Election_Results_08-20/master/2020_US_County_Level_Presidential_Results.csv")

county_mig <- get_estimates(
  geography = "county",
  variables = "RDOMESTICMIG",
  vintage = 2023
)

merged <- left_join(pres_results, county_mig, by = c("county_fips" = "GEOID")) %>%
  mutate(pct_dem = 100 * per_dem,
         dem = ifelse(per_dem > per_gop, "More Biden", "More Trump"))


ggplot(merged, aes(x = pct_dem, y = value, color = dem,
                   size = total_votes)) +
  geom_point(alpha = 0.6) +
  scale_color_manual(values = c("More Biden" = "blue", "More Trump" = "red")) +
  scale_size_area(labels = scales::label_number(big.mark = ","), max_size = 10,
                  breaks = c(10000, 100000, 1000000, 5000000)) +
  theme_minimal(base_family = "Montserrat", base_size = 20) +
  scale_x_continuous(labels = scales::percent_format(scale = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  guides(color = guide_legend(override.aes = list(size = 4))) +
  labs(size = "Total votes cast",
       color = "Preferred candidate",
       x = "% voting for President Biden, 2020",
       y = "Net domestic migration per 1000 residents, 2022-2023",
       title = "Domestic Migration and 2020 Presidential Election Results",
       subtitle = "US Counties | 2023 Census Population Estimates",
       caption = "tidycensus R package | @kyle_e_walker")