utah_libraries.R

library(tidyverse)
library(sf)
library(scico)

# https://gis.utah.gov/data/boundaries/citycountystate/
counties <- st_read("data/Counties/Counties.shp")

# https://gis.utah.gov/data/society/schools-libraries/
libraries <- st_read("data/Libraries/Libraries.shp")

# Count the number of libraries per county
libraries_county <- libraries %>%
  group_by(COUNTY) %>%
  summarize(num_libraries = n()) %>%
  # Make NAME uppercase for joining later
  mutate(NAME = str_to_upper(COUNTY))

# Merge the county-level library counts with the county data
counties_with_data <- counties %>%
  st_join(libraries_county) %>%
  mutate(people_per_library = POP_CURRES / num_libraries)

# Plot this!
ggplot() +
  geom_sf(data = counties_with_data, aes(fill = people_per_library), size = 0.25) +
  geom_sf(data = libraries, size = 0.5) +
  coord_sf(crs = 26912, datum = NA) + 
  scale_fill_scico(palette = "bilbao", end = 0.9,
                   labels = scales::comma, na.value = "white",
                   name = "People per library") +
  guides(fill = guide_colorbar(barwidth = 10, barheight = 1)) +
  labs(title = "Libraries in Utah", 
       subtitle = "Davis and Weber counties seem to be the most\nunderserved given their populations") +
  theme_void(base_family = "Clear Sans") +
  theme(legend.position = "bottom",
        plot.title = element_text(face = "bold"))

Here's the final map