Data preparation

The first step is to read and prepare the data we want to visualize. We use the us_states data from the spData package as the source of the inset map, and north_carolina from the sf package as the source of the main map.

library(spData)
data("us_states", package = "spData")
north_carolina = read_sf(system.file("shape/nc.shp", package = "sf"))

Both objects should have the same coordinate reference system (crs). Here, we use crs = 2163, which represents the US National Atlas Equal Area projection.

us_states_2163 = st_transform(us_states, crs = 2163)
north_carolina_2163 = st_transform(north_carolina, crs = 2163)

We also need to have the borders of the area we want to highlight (use in the main map). This can be done by extracting the bounding box of our north_carolina_2163 object.

north_carolina_2163_bb = st_as_sfc(st_bbox(north_carolina_2163))

Maps creation

The second step is to create both inset and main maps independently. The inset map should show the context (larger area) and highlight the area of interest.

ggm1 = ggplot() + 
  geom_sf(data = us_states_2163, fill = "white") + 
  geom_sf(data = north_carolina_2163_bb, fill = NA, color = "red", size = 1.2) +
  theme_void()

ggm1

The main map’s role is to tell the story. Here we show the number of births between 1974 and 1978 in the North Carolina counties (the BIR74 variable) using the Mint color palette from the rcartocolor palette. We also customize the legend position and size - this way, the legend is a part of the map, instead of being somewhere outside the map frame.

ggm2 = ggplot() + 
  geom_sf(data = north_carolina_2163, aes(fill = BIR74)) +
  scale_fill_carto_c(palette = "Mint") +
  theme_void() +
  theme(legend.position = c(0.4, 0.05),
        legend.direction = "horizontal",
        legend.key.width = unit(10, "mm"))

ggm2

Maps joining

The final step is to join two maps. This can be done using functions from the cowplot package. We create an empty ggplot layer using ggdraw(), fill it with out main map (draw_plot(ggm2)), and add an inset map by specifing its position and size:

gg_inset_map1 = ggdraw() +
  draw_plot(ggm2) +
  draw_plot(ggm1, x = 0.05, y = 0.65, width = 0.3, height = 0.3)

gg_inset_map1

The final map can be saved using the ggsave() function.

ggsave(filename = "01_gg_inset_map.png", 
       plot = gg_inset_map1,
       width = 8, 
       height = 4,
       dpi = 150)

Data preparation

This map will use the US states borders (states()) as the source of the inset map and the Kentucky Senate legislative districts (state_legislative_districts()) as the main map.

library(tigris)
options(tigris_use_cache = TRUE)
us_states = states(cb = FALSE, class = "sf")
ky_districts = state_legislative_districts("KY", house = "upper",
                                           cb = FALSE, class = "sf")

The states() function, in addition to the 50 states, also returns the District of Columbia, Puerto Rico, American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the US Virgin Islands. For our purpose, we are interested in the continental 48 states and the District of Columbia only; therefore, we remove the rest of the divisions using subset().

us_states = subset(us_states, 
                   !NAME %in% c(
                     "United States Virgin Islands",
                     "Commonwealth of the Northern Mariana Islands",
                     "Guam",
                     "American Samoa",
                     "Puerto Rico",
                     "Alaska",
                     "Hawaii"
                   ))

The same as in the example above, we transform both objects to have the same projection.

ky_districts_2163 = st_transform(ky_districts, crs = 2163)
us_states_2163 = st_transform(us_states, crs = 2163)

We also extract the bounding box of the main object here. However, instead of using it directly, we add a buffer of 10,000 meters around it. This output will be handy in both inset and main maps.

ky_districts_2163_bb = st_as_sfc(st_bbox(ky_districts_2163))
ky_districts_2163_bb = st_buffer(ky_districts_2163_bb, dist = 10000)

The ky_districts_2163 object does not have any interesting variables to visualize, so we create some random values here. However, we could also join the districts’ data with another dataset in this step.

ky_districts_2163$values = runif(nrow(ky_districts_2163))

Map creation

The inset map should be as clear and simple as possible.

ggm3 = ggplot() + 
  geom_sf(data = us_states_2163, fill = "white", size = 0.2) + 
  geom_sf(data = ky_districts_2163_bb, fill = NA, color = "blue", size = 1.2) +
  theme_void()

ggm3

On the other hand, the main map looks better when we provide some additional context to our data. One of the ways to achieve it is to add the borders of the neighboring states.

Importantly, we also need to limit the extent of our main map to the range of the frame in the inset map. This can be done with the coord_sf() function.

ggm4 = ggplot() + 
  geom_sf(data = us_states_2163, fill = "#F5F5DC") +
  geom_sf(data = ky_districts_2163, aes(fill = values)) +
  scale_fill_carto_c(palette = "Sunset") +
  theme_void() +
  theme(legend.position = c(0.5, 0.07),
        legend.direction = "horizontal",
        legend.key.width = unit(10, "mm"),
        plot.background = element_rect(fill = "#BFD5E3")) +
  coord_sf(xlim = st_bbox(ky_districts_2163_bb)[c(1, 3)],
           ylim = st_bbox(ky_districts_2163_bb)[c(2, 4)])

ggm4

Finally, we draw two maps together, trying to find the best location and size for the inset map.

gg_inset_map2 = ggdraw() +
  draw_plot(ggm4) +
  draw_plot(ggm3, x = 0.02, y = 0.65, width = 0.35, height = 0.35)

gg_inset_map2

The final map can be saved using the ggsave() function.

ggsave(filename = "02_gg_inset_map.png", 
       plot = gg_inset_map2,
       width = 7.05, 
       height = 4,
       dpi = 150)