migration_temperature.R

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

continental <- read_csv("https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/county/mapping/110-tavg-202402-1.csv", skip = 4) |>
  mutate(avg_temp = as.numeric(Value),
         GEOID = map_chr(ID, ~{
           last3 <- str_sub(.x, start = -3)
           first2 <- tidycensus:::validate_state(str_sub(.x, end = 2))
           str_c(first2, last3)
         }))


county_mig <- get_estimates(
  geography = "county",
  variables = c("RDOMESTICMIG", "POPESTIMATE"),
  vintage = 2023,
  output = "wide"
)

merged <- left_join(continental, county_mig, by = "GEOID") 


ggplot(merged, aes(x = avg_temp, y = RDOMESTICMIG, color = avg_temp,
                   size = POPESTIMATE)) +
  geom_point(alpha = 0.6) +
  scale_color_viridis_c(option = "turbo") +
  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 = function(x) paste0(x, "°F")) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  guides(color = guide_legend(override.aes = list(size = 4))) +
  labs(size = "Total population",
       color = "Average temperature",
       x = "Average temperature, February 2024",
       y = "Net domestic migration per 1000 residents, 2022-2023",
       title = "Domestic Migration and Average February Temperatures",
       subtitle = "Continental US Counties | 2023 Census Population Estimates",
       caption = "tidycensus R package | Temperature data from NCEI / NOAA\n@kyle_e_walker | Connecticut not included due to inconsistent county definitions") + 
  theme(plot.caption = element_text(hjust = 0))