Feel free to try the exercises below at your leisure. Solutions will be posted later in the week!

Geospatial

  1. Using the Texas unemployment figures here, create a choropleth of Texas counties in ggplot2 where darker filled counties represent higher unemployment rates. (Texas county shapefile downloadable from the Census Bureau here.) (If you want to challenge yourself, try also replicating this map in leaflet!)
library(dplyr)
library(sf)
library(ggplot2)

tx_unemploy <- read.csv('https://raw.githubusercontent.com/apodkul/ppol6803_03/main/Data/tx_county_data.csv')

tx_shape <- sf::read_sf('cb_2018_us_county_20m/cb_2018_us_county_20m.shp') %>%
  filter(STATEFP == 48) %>% # Subset to Texas
  mutate(FIPS_ST_CN = stringr::str_c(STATEFP, COUNTYFP) %>% as.numeric()) 

tx_shape %>% 
  left_join(tx_unemploy, by = 'FIPS_ST_CN') %>%
  ggplot() + 
  geom_sf(aes(fill = Unemployment)) + 
  theme_minimal() + 
  coord_sf() + 
  scale_fill_gradient(low = 'white', 
                      high = 'black') + 
  ggtitle("Texas Unemployment by County")

  1. Create a bubble map of U.S. states (excluding AK and HI) with a point at each state’s centroid sized proportionally to the number of airports in each state. (Hint: sf::st_centroid or rgeos::gCentroid might be helpful here.)
library(dplyr)
library(sf)
library(ggplot2)

us_airport <- read.csv('https://raw.githubusercontent.com/apodkul/ppol6803_03/main/Data/airport_list.csv') %>%
  group_by(State) %>%
  dplyr::summarize(Count = n()) %>%
  filter(!(State %in% c("Alaska", "Hawaii", "Dist. Of Columbia")))

us_shape <- sf::read_sf('cb_2018_us_state_20m/cb_2018_us_state_20m.shp') %>%
  filter(!(STUSPS %in% c("AK", "DC", "HI", 'PR'))) # Remove AK, HI, DC and PR

ggplot(us_shape) + 
  geom_sf(fill = 'white') + 
  coord_sf() + 
  geom_sf(data = sf::st_centroid(us_shape) %>%
               left_join(us_airport, by = c("NAME" = "State")), 
          mapping = aes(size = Count), 
          shape = 21, color = 'navy', 
          fill = alpha('maroon', .5)) +
  theme_void() +
  scale_size_continuous('Airport Count') + 
  theme(legend.position = 'bottom')