States

This document provides examples of how to create choropleth maps of Mexican states using the mxmaps package. The examples demonstrate how to customize the maps, including changing the color scale, adding state labels, handling missing values in the legend, and arranging multiple maps in a grid.

Basic State Choropleth

This example shows how to create a simple choropleth map of Mexican states, displaying the total population by state.

library(mxmaps)

data("df_mxstate_2020")
df_mxstate_2020$value <- df_mxstate_2020$pop
mxstate_choropleth(df_mxstate_2020,
                    title = "Total population, by state") 

Customizing the Color Scale

This example demonstrates how to change the color scale of the map. The viridis color scale is used to represent the percentage of the population that speaks an indigenous language.

library(mxmaps)
library(viridis)
library(scales)

df_mxstate_2020$value <-  df_mxstate_2020$indigenous_language / 
  df_mxstate_2020$pop 
gg = MXStateChoropleth$new(df_mxstate_2020)
gg$title <- "Percentage of the population that speaks\nan indigenous language"
gg$set_num_colors(1)
gg$ggplot_scale <- scale_fill_viridis("percent", labels = percent)
gg$render()

Adding Labels to the Map

This example shows how to add labels to each state on the map. The ggrepel package is used to prevent the labels from overlapping.

library("geojsonio")
library("ggplot2")
library("ggrepel")
library("sf")
library("RJSONIO")
library("dplyr")

df_mxstate_2020$value <-  df_mxstate_2020$indigenous_language / 
  df_mxstate_2020$pop * 100
p <- mxstate_choropleth(df_mxstate_2020, 
                        num_colors = 1,
                        title = "Percentage of the population that speaks\nan indigenous language",
                        legend = "%")


data("mxstate.topoJSON")

tmpdir <- tempdir()
# have to use RJSONIO or else the topojson isn't valid
write(RJSONIO::toJSON(mxstate.topoJSON), file.path(tmpdir, "state.topojson"))
states <- topojson_read(file.path(tmpdir, "state.topojson")) |>
  arrange(id)
df_mxstate <- arrange(df_mxstate_2020, region)

# make sure the coordinates of the labels are in the correct order
 
df_mxstate_2020$lon <- st_coordinates(st_centroid(states))[,1]
df_mxstate_2020$lat <- st_coordinates(st_centroid(states))[,2]


df_mxstate_2020$group <- df_mxstate_2020$state_abbr

p +
  geom_text_repel(data = df_mxstate_2020, 
                  aes(lon, lat, label = state_abbr,), 
                  size = 3,
                  box.padding = unit(0.1, 'lines'), 
                  force = 0.1)

Legend for Missing Values

This example demonstrates how to add a legend for states with missing (NA) values. A separate legend is created to indicate which states have no data.

df_na <- df_mxstate_2020
df_na$value[1:20] <- NA
mxstate_choropleth(df_na,
                   num_colors = 1,
                   title = "Percentage of the population that speaks\nan indigenous language",
                   legend = "%") +
  # Add a fake color scale which we'll change to 'no data'
  geom_point(data = df_mxmunicipio_2020[1,],
             size = -1,
             aes(color = "",
                 group = NA)) +
  scale_color_manual(values = NA) +
  scale_fill_continuous(low="orange", high="darkred", 
                        na.value = "lightgray") +
  theme(legend.key = element_rect(color = "black")) + # Add a border to the legend
  # Add an extra color legend with a giant square
  guides(color = guide_legend("no data",
                              override.aes=list(color = "lightgray",
                                                shape = 15, # shape 15 is a black square
                                                size = 7)))

Creating Small Multiples (Faceting)

While mxmaps is not directly compatible with facet_grid, this example shows how to mimic faceting functionality by creating multiple maps and arranging them in a grid using the ggarrange function from the ggpubr package. This allows for the comparison of different variables or populations side-by-side.

library(mxmaps)
library(ggplot2)
library(ggpubr)

data("df_mxstate_2020")
df <- rbind(df_mxstate_2020, df_mxstate_2020)

df$genero <- rep(c("m", "f"), each= 32)
df$value <- ifelse(df$genero == "m", df$pop_male, df$pop_female)
# This is needed so that both the male and female population scales have
# the same values
df$value <- cut(df$value, breaks = c(0, 5e4, 1e6, 2e6, 4e6, 20e7))


f <- mxstate_choropleth(subset(df, genero == "f"),
                        title = "F population, by state",
                        num_colors = 5) 
m <- mxstate_choropleth(subset(df, genero == "m"),
                        title = "M population, by state",
                        num_colors = 5)

ggarrange(f, m,
          labels = c("", ""),
          ncol = 1, nrow = 2)

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