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Insets with ggplot2 and tmap - and mapsf!

A map on a map


03 mar 2022

This post is dedicated to Dominic Royé, AKA \@dr_xeo

A common challenge when creating maps is how to include an inset map in your visualization. An inset map is nothing more than a smaller map usually included in a corner that may provide additional context to the overall map. It is also useful for representing spatial units that may form part of a country but whose geographical location would result in an imperfect visualization, or for including small units that would otherwise not be shown on the map.

I have already covered this using the base plot() function, but this time I show how to produce these insets using the ggplot2 and tmap packages. In short: use the cowplot package.

Test case: Canary Islands as an inset

In this example, I create a map of Spain using mapSpain and an inset for the Canary Islands.

The “true” map of Spain is:

library(mapSpain)
library(sf)
library(ggplot2)
library(dplyr)

regions <- esp_get_ccaa(moveCAN = FALSE)

ggplot(regions) +
  geom_sf()

Map of Spain with autonomous community boundaries and the Canary Islands in their geographic position southwest of the mainland.

I use a different CRS for each part of Spain. In the case of mainland Spain, I use ETRS89 / UTM 30N (EPSG:25830) and for the Canary Islands I use REGCAN95 / UTM 28N (EPSG:4083).

main <- regions %>%
  filter(ccaa.shortname.es != "Canarias") %>%
  st_transform(25830)

ggplot(main) +
  geom_sf()

Map of mainland Spain and the Balearic Islands with autonomous community boundaries, excluding the Canary Islands.

island <- regions %>%
  filter(ccaa.shortname.es == "Canarias") %>%
  st_transform(4083)

ggplot(island) +
  geom_sf()

Map of the Canary Islands in their local projected coordinate system.

So that was easy! Just a couple of maps using ggplot2. Let’s start mixing and matching!

On ggplot2

We have already created two quick maps with ggplot2. Now, to produce our map with insets we will:

  1. Produce two plots: the main plot and the subplot, providing a minimal style. We will store them as ggplot objects.

  2. Combine both objects with cowplot.

# Main plot
main_gg <- ggplot(main) +
  geom_sf() +
  theme_void() +
  theme(
    plot.background = element_rect(fill = "grey85", colour = NA),
    # Add a bit of margin on the bottom left
    # We will place the inset there
    plot.margin = margin(l = 80, b = 80)
  )

# Sub plot
sub_gg <- ggplot(island) +
  geom_sf() +
  theme_void() +
  # Add a border to the inset
  theme(
    panel.border = element_rect(fill = NA, colour = "black"),
    plot.background = element_rect(fill = "grey95")
  )

We have our objects in place, and now is when the magic happens! With cowplot, we can combine both maps into a single one. You may need to play a bit with the parameters x, y, hjust and vjust of the subplot to improve the placement:

library(cowplot)

ggdraw() +
  draw_plot(main_gg) +
  draw_plot(sub_gg,
    height = 0.2,
    x = -0.25,
    y = 0.08
  )

Map of Spain with the Canary Islands moved into a boxed inset at the lower left.

Note also that this approach is valid not only for maps, but for all types of plots produced by ggplot2, since this package is not specific to map objects:

# Combining non-spatial plots
library(palmerpenguins)

mass_flipper <- ggplot(
  data = penguins,
  aes(
    x = flipper_length_mm,
    y = body_mass_g
  )
) +
  geom_point(aes(
    color = species,
    shape = species
  ),
  size = 3,
  alpha = 0.8
  ) +
  theme_minimal() +
  scale_color_manual(values = c("darkorange", "purple", "cyan4"))

flipper_hist <- ggplot(data = penguins, aes(x = flipper_length_mm)) +
  geom_histogram(aes(fill = species),
    alpha = 0.5,
    position = "identity",
    show.legend = FALSE
  ) +
  scale_fill_manual(values = c("darkorange", "purple", "cyan4")) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white"))


# Nonsense plot!
ggdraw() +
  draw_plot(mass_flipper) +
  draw_plot(flipper_hist,
    scale = 0.25,
    y = 0.3,
    x = -0.2
  )

Scatter plot of penguin body mass against flipper length, colored by species, with an inset histogram of flipper length at the upper left.

On tmap

We can follow a similar approach with tmap. In version 3.x.x (there is a new revamped version under development), we can use tmap_grob() to convert the tmap objects to the objects that cowplot can handle.

library(tmap)

main_tmap <- tm_shape(main) +
  tm_polygons() +
  tm_layout(
    inner.margins = c(.3, .3, 0, 0),
    frame = FALSE
  )


main_tmap <- tmap_grob(main_tmap)

sub_tmap <- tm_shape(island) +
  tm_polygons()

sub_tmap <- tmap_grob(sub_tmap)

Once we have these new “grobs”, we can use the same approach as the one we applied to ggplot2 objects.

ggdraw() +
  draw_plot(main_tmap) +
  draw_plot(sub_tmap,
    height = 0.3,
    x = -0.2
  )

Map of Spain with regional boundaries and a Canary Islands inset at the lower left.

Update: On mapsf

Timotheé Giraud (AKA \@rgeomatic), the developer of mapsf, also shared how to create inset maps using that package:

library(mapsf)

mf_map(main)
mf_inset_on(island, pos = "bottomright", cex = .3)
mf_map(island)
box(lwd = .5)
mf_inset_off()

Map of Spain with regional boundaries and a boxed Canary Islands inset at the lower right.

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