Creating a choropleth map using USA regions as the boundaries

[drevanzyl]

Good day!

I would like to create a choropleth map with R using USA regions as the boundaries. Here is an example created in PowerPoint:

This is what I could acchieve with qmplot:

library(tidyverse)
library(ggmap)
library(ggrepel)
data <- tibble::tribble(
                  ~lon,       ~lat,
            -98.133208, 11.4326077,
            -89.552784,  11.634501,
           -62.2766186, 44.5090949,
           -65.3279894, 33.1067754,
           -67.7095365, 44.6294348,
            -96.552784,  35.634501,
           -97.3279093, 29.7724417,
           -82.6363869, 28.2949194,
           -80.2061931, 46.0133808,
            -72.014118, 32.4681642,
           -76.2531465, 47.3666368,
           -82.1650991, 46.7758541,
             -5.696645,  11.945587,
           -112.707349, 38.5205043,
           -63.0884036, 52.3930959,
            -87.128901, 39.1242719,
           -65.1626756, 31.3463503,
           -94.3254958, 40.3274999,
             -98.56121, 42.5770056,
          -115.4429944, 46.1502862,
          -117.7901088, 30.6913751,
           -63.7389596, 54.6584068,
          -109.1147095, 24.2156978,
          -119.8340735, 22.6832497,
          -117.8780275, 37.7311394,
           -67.1763467, 38.5861576,
           -96.4427769,   25.14644,
           -78.0814292, 15.5386936,
           -74.4185584, 34.5834425,
           -77.4185584, 36.5834425,
           -79.4185584, 37.5834425,
            -63.121085, 33.9241038,
            -88.121085, 41.9241038,
          -112.7260713,  40.836309,
            -90.552784,  30.634501,
           -109.552784,  12.634501,
           -73.9224329, 48.6153549
          ) 

data <-
  data %>% sample_n(200, replace = TRUE) %>% mutate(
    total = round( runif(nrow(.), min = 100, max = 10000), digits = 0) ,
    lon =  lon + round( runif(nrow(.), min = -10, max = 10), digits = 0),
    lat =  lat + round( runif(nrow(.), min = -10, max = 10), digits = 0)
  ) 

qmplot(x = lon, y = lat, data = data, maptype = "toner-lite", legend = "topleft" , alpha = .3,geom = c("point","density2d"), size = total)+ theme(legend.position = "none")+ 
  geom_label_repel(aes(label=total),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') 
#> Using zoom = 3...
#> Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.
#> Warning in min(x): no non-missing arguments to min; returning Inf
#> Warning in max(x): no non-missing arguments to max; returning -Inf
#> Warning in min(x): no non-missing arguments to min; returning Inf
#> Warning in max(x): no non-missing arguments to max; returning -Inf
#> Warning: ggrepel: 174 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

^{Created on 2021-09-16 by the reprex package (v2.0.1)}

Any suggestions?

Thank you very much.

André

[williaml]

You have points. You want to join them to the polygons then represent them as the value. Check out the sf package.

[drevanzyl]

Hi @williaml , thank you. I think your suggestion got me closer: Mapping by US Census Divisions in ggplot2 Unfortunately, I'm still unable to use the Total in my data to fill the map below.

library(tidyverse)
library(tigris)
#> To enable 
#> caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
# Download state data and filter states
sts <- states() #%>%
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  #filter(!STUSPS %in% c('HI', 'AK', 'PR', 'GU', 'VI', 'AS', 'MP'))

# Summarize to DIVISION polygons, see sf::st_union
REGION <- sts %>%
  group_by(REGION) %>% 
  summarize()

# Plot it
ggplot() + 
  theme_void() +
  geom_sf(data = sts, 
          aes(fill = as.numeric(REGION)), 
          color = 'white') +
  geom_sf(data = REGION, 
          color = 'black', 
          fill = NA,
          size = 1) +

  scale_fill_viridis_c() +
  coord_sf(crs = 5070) +
  labs(fill = NULL)  

^{Created on 2021-09-18 by the reprex package (v2.0.1)}

[williaml]

It would be something like this. You could put the state borders on top like you did for your first map. You also don't need to group by region.

# spatial points using your data
data_pt <- sf::st_as_sf(data, coords = c("lon", "lat"), crs = 4269) 

# join points to region spatially
# then make non spatial
# summarise to get total
region_counts <- REGION %>% 
  st_transform(4269) %>% 
  st_intersection(data_pt) %>% 
  st_set_geometry(NULL) %>% 
  group_by(REGION) %>% 
  summarise(total = sum(total))

# join counts to region, spatial data
region_data <- left_join(REGION, region_counts) %>% 
  st_transform(5070)

# Plot it
ggplot() + 
  theme_void() +
  geom_sf(data = region_data, 
          aes(fill = total), 
          color = 'white') +
  scale_fill_viridis_c() +
  coord_sf(crs = 5070) +
  labs(fill = NULL)