Plotting maps with choroplethr

According to the below graphic from the Sacramento Bee, Latinos have overtaken whites to become the most populous ethnic group.

Let's use the choroplethr package to examine the racial distribution of California counties.

Let's start with the preliminaries: installing and loading the package, as well as loading the relevant datasets. We'd also need to load the ggplot2 library, which will come useful in plotting the maps.

install.packages("choroplethr")

library(choroplethr)

data(df_state_demographics)

data(df_county_demographics)

library(ggplot2)

Thereafter, we create the individual maps. Notice that each map is created by adding "layers" on top of each other. For example, choro_white consists of three layers:
1. county population data of whites (county_choropleth accesses df_county_demographics$value)
2. a graph title,
3. a geographical map (coord_map()), which is part of ggplot2.


df_county_demographics$value = df_county_demographics$percent_white
choro_white = county_choropleth(df_county_demographics, state_zoom="california", num_colors=1) +
  ggtitle("California Counties\n Percent White") +
  coord_map()

df_county_demographics$value = df_county_demographics$percent_hispanic
choro_hispanic = county_choropleth(df_county_demographics, state_zoom="california", num_colors=1) + 
  ggtitle("California Counties\n Percent Hispanic") + 
  coord_map()

df_county_demographics$value = df_county_demographics$percent_asian
choro_asian = county_choropleth(df_county_demographics, state_zoom="california", num_colors=1) + 
  ggtitle("California Counties\n Percent Asian") + 
  coord_map()

Finally, we need to display the maps we've produced:

library(gridExtra)
grid.arrange(choro_white, choro_hispanic, choro_asian, nrow=3)

The output:

As is expected, the southern counties have more Hispanics.