Compare Locations/Events with Geobubble Charts (02_04)

          library(tidyverse)
        
          library(sf)
        
          library(tigris)
        
          library(rmapshaper)
        
          library(janitor)
        
          library(ggrepel)
        
          

        
          us_airport_passengers <- read_csv("data/us-airport-passenger-numbers_2019.csv")
        
          

        
          us_airport_passengers_2019 <- us_airport_passengers %>% 
        
            filter(year == 2019) %>% 
        
            st_as_sf(coords = c("long", "lat"),
        
                     crs = 4326)
        
          

        
          us_contiguous_sf <- states() %>% 
        
            clean_names() %>% 
        
            mutate(statefp = as.numeric(statefp)) %>% 
        
            filter(statefp < 60,
        
                   !statefp %in% c(2, 15)) %>% 
        
            ms_simplify()
        
          

        
          us_airport_passengers_2019 <- us_airport_passengers_2019 %>% 
        
            arrange(desc(total_passengers))
        
          

        
          ggplot() +
        
            geom_sf(data = us_contiguous_sf) +
        
            geom_sf(data = us_airport_passengers_2019,
        
                    aes(size = total_passengers),
        
                    pch = 21,
        
                    alpha = 0.7,
        
                    fill = "grey70") +
        
            scale_size_area(max_size = 10)
        
          

        
          

        
          # ==== let's go a bit further ====
        
          

        
          airport_passenger_extremes <- us_airport_passengers_2019 %>% 
        
            slice(c(1, nrow(.)))
        
          

        
          airport_passenger_extremes_coords <- st_coordinates(airport_passenger_extremes) %>% 
        
            as_tibble() %>% 
        
            rename(long = X,
        
                   lat = Y)
        
          

        
          airport_passenger_extremes <- airport_passenger_extremes %>% 
        
            st_drop_geometry() %>% 
        
            bind_cols(airport_passenger_extremes_coords)
        
          

        
          ggplot() +
        
            geom_sf(data = us_contiguous_sf) +
        
            geom_sf(data = us_airport_passengers_2019,
        
                    aes(size = total_passengers),
        
                    pch = 21,
        
                    alpha = 0.7,
        
                    fill = "grey70") +
        
            geom_label_repel(data = airport_passenger_extremes,
        
                             aes(x = long,
        
                                 y = lat,
        
                                 label = airport),
        
                             nudge_y = c(-2, 2)) +
        
            scale_size_area(max_size = 10) +
        
            theme_void() +
        
            theme(legend.position = "bottom")