Figure 5.5 Maps of areas with widely varying populations

figures

code

Not too much to say here, except that it would be nice if somebody would develop a better approach to mapping US-wide data. Counties really are a terrible base unit. Augmenting the data with population density helps a little…

Code

library(sf)
library(tmap)
library(cols4all)
library(dplyr)

ca <- st_read("ca-pops.gpkg") |>
  mutate(area = st_area(geom), 
         pop_density = population / area * 1000000)

An opportunity here to get acquainted with the new semantics of tmap version 4…

Code

m1 <- tm_shape(ca) + 
  tm_polygons(
    fill = "population", col = "white", lwd = 0.5,
    fill.scale = tm_scale_intervals(values = "brewer.reds", 
                                    style = "pretty"),
    fill.legend = tm_legend(title = "Population", 
                            position = c(0.53, 0.8))) + 
  tm_layout(frame = FALSE, legend.frame = FALSE)
  
m2 <- tm_shape(ca) + 
  tm_polygons(
    fill = "pop_density", col = "white", lwd = 0.5,
    fill.scale = tm_scale_intervals(values = "brewer.greens",
                                    style = "pretty"),
    fill.legend = tm_legend(title = "Pop density", 
                            position = c(0.53, 0.8))) + 
  tm_layout(frame = FALSE, legend.frame = FALSE)

m3 <- tm_shape(ca) + 
  tm_polygons(
    fill = "pop_density", col = "white", lwd = 0.5,
    fill.scale = tm_scale_intervals(values = "brewer.blues", 
                                    style = "log10_pretty"),
    fill.legend = tm_legend(title = "Pop density", 
                            position = c(0.53, 0.8))) + 
  tm_layout(frame = FALSE, legend.frame = FALSE)
  
tmap_arrange(m1, m2, m3)

Code

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