pre-county-population.R

library(tidycensus)

county_population <- get_acs(
  geography = "county",
  variables = "B01003_001E", # 总人口变量
  year = 2021,
  survey = "acs5"           # 5年期数据
)

head(county_population)

library(dplyr)
# 选择county_population中的变量Geo_ID和Name和estimate
counties_population <- county_population %>%
  select(GEOID, NAME, estimate) %>%
  rename(county_fips = GEOID, county_name = NAME, population = estimate)


library(sf)
library(urbnmapr)
counties_sf <- get_urbn_map("counties", sf = TRUE)

# 将counties_sf和counties_population合并
library(tidyverse)
counties_sf_size <- counties_sf %>%
  left_join(counties_population, by = c("county_fips" = "county_fips"))

counties_sf_size1 <- counties_sf_size %>%
  select(county_fips, county_name.y, population, geometry)%>%
  rename(county_name = county_name.y)

# couties_sf_size是fake demand data

counties_sf_size2 <- st_transform(counties_sf_size1, crs = 4326)

counties_sf_size2 <- counties_sf_size2 %>% 
  as_mapbox_source()

counties_sf_size2 %>% write_rds("counties_sf_processed.rds")