Getting started with ‘ggmapinset’

library(ggmapinset)
#> Loading required package: sf
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
library(ggplot2)

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)

This article provides some recipes for working with insets.

Example usage

This example uses the mosquito surveillance dataset ggmapinset::mozzies_nsw2301. It includes points from across New South Wales.

head(mozzies_nsw2301)
#> # A tibble: 6 × 7
#>   location week_ending species             count type     lat  long
#>   <chr>    <date>      <fct>               <ord> <fct>  <dbl> <dbl>
#> 1 Albury   2023-01-07  Culex annulirostris low   inland -36.1  147.
#> 2 Albury   2023-01-07  Aedes vigilax       <NA>  inland -36.1  147.
#> 3 Albury   2023-01-07  total               low   inland -36.1  147.
#> 4 Albury   2023-01-14  Culex annulirostris high  inland -36.1  147.
#> 5 Albury   2023-01-14  Aedes vigilax       <NA>  inland -36.1  147.
#> 6 Albury   2023-01-14  total               high  inland -36.1  147.

Firstly, we can recreate the basic maps from the report accompanying the dataset. Since the dataset has latitude and longitude coordinates, it can easily be converted into a spatial data frame with sf::st_as_sf().

library(dplyr)
library(sf)

# just take the total count from a single week of the data
mozzies <- mozzies_nsw2301 |>
  filter(species == "total", week_ending == as.Date("2023-01-07")) |>
  st_as_sf(coords = c("long", "lat"), crs = st_crs("WGS84"))

labels <- c("Low (<50)", "Medium (50-100)", "High (101-1,000)",
            "Very High (1,001-10,000)", "Extreme (>10,000)")

scale1 <-
  scale_colour_manual(
    name = NULL,
    values = c("green", "gold", "darkorange", "red", "black"),
    labels = labels,
    na.value = "grey",
    drop = FALSE
  )
scale2 <-
  scale_size_ordinal(
    name = NULL,
    labels = labels,
    range = c(3, 5),
    na.value = 2,
    drop = FALSE
  )

ggplot(mozzies) +
  geom_sf(data = nswgeo::nsw, fill = NA) +
  geom_sf(aes(size = count, colour = count)) +
  geom_sf_text(aes(label = location), hjust = 0, nudge_x = 0.25, size = 3) +
  coord_sf(xlim = c(NA, 158)) +
  scale1 + scale2 +
  theme_void()
#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude data

The warning about sf::st_point_on_surface can be disregarded. Any errors due to the coordinate system are unlikely to make much visual difference to where text is placed in this case.

This plot looks a little congested. We can improve things a bit by using the repulsive version of the label geom from {ggrepel}:

library(ggrepel)

ggplot(mozzies) +
  geom_sf(data = nswgeo::nsw, fill = NA) +
  geom_sf(aes(size = count, colour = count)) +
  geom_text_repel(
    aes(label = location, geometry = geometry),
    hjust = 0,
    nudge_x = 0.25,
    size = 3,
    max.overlaps = 15,
    point.padding = 0,
    min.segment.length = 1,
    stat = "sf_coordinates"
  ) +
  coord_sf(xlim = c(NA, 158)) +
  scale1 + scale2 +
  theme_void()
#> Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
#> give correct results for longitude/latitude data
#> Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

The main thing to note above is that geom_text_repel() is not hooked into the {ggplot2}’s geospatial integration, so it needs to be told to use stat_sf_coordinates() to compute the coordinates, and it needs an explicit mapping for the geometry aesthetic.

That improved most of the map except for the Sydney region where most of the labels are missing since they would overlap. The max.overlaps parameter to geom_text_repel() can help, but doesn’t address the overcrowding issue. This is where an inset can help.

First we define the inset we want. We can collect up all the points from the dataset that were labelled with type == "sydney" and use some standard geospatial functions to get the diameter and centre of a circle that will cover all those points. We then specify that we want this circle to be enlarged by a factor of 4, and shifted to south and east:

sydney <- filter(mozzies, type == "sydney")
sydney_size <- st_distance(sydney, sydney) |> max() |> units::set_units("km")
sydney_centre <- st_union(sydney) |> st_centroid()

sydney_inset <- configure_inset(
  centre = sydney_centre,
  radius = as.numeric(sydney_size),
  translation = c(400, -200),
  scale = 4,
  units = "km"
)

Finally, we can repeat the previous plot with the _inset version of the relevant layers. The inset configuration is passed to the coord. The only other change is that to make sure the labels for Sydney sites appear in the inset instead of the base map, we need to remap the x and y aesthetics to the versions computed by the underlying stat.

ggplot(mozzies) +
  geom_sf_inset(data = nswgeo::nsw, fill = NA) +
  geom_sf_inset(aes(size = count, colour = count), map_base = "clip") +
  geom_text_repel(
    aes(
      x = after_stat(x_inset),
      y = after_stat(y_inset),
      label = location,
      geometry = geometry
    ),
    hjust = 0,
    nudge_x = 0.25,
    size = 3,
    force_pull = 2,
    max.overlaps = Inf,
    point.padding = 0,
    min.segment.length = 1,
    stat = "sf_coordinates_inset"
  ) +
  geom_inset_frame() +
  coord_sf_inset(xlim = c(NA, 158), inset = sydney_inset) +
  scale1 + scale2 +
  theme_void()

Further tweaks of label placement can be achieved by playing around with the parameters of geom_text_repel(), or by passing vectors of positions into the nudge_x and nudge_y parameters.

Different aesthetics for inset layer

By default, geom_sf_inset() creates two copies of the map layer: one for the base map and the other for the inset map. The inset is transformed and clipped, but uses the same underlying aesthetics mapping and parameters.

If you want to have different aesthetics for the two layers, you’ll need to turn off this copying with map_base = "none". With this parameter set and an inset parameter provided, only the inset layer will be drawn. To draw only the base layer, you can use map_inset = "none", inset = NULL, or simply use the normal geom_sf().

ggplot(nc) +
  # this is equivalent to the following line:
  # geom_sf_inset(fill = "white", map_inset = "none") +
  geom_sf(fill = "white") +
  geom_sf_inset(aes(fill = AREA), map_base = "none") +
  geom_inset_frame() +
  coord_sf_inset(inset = configure_inset(
    centre = sf::st_centroid(sf::st_geometry(nc)[nc$NAME == "Bladen"]), scale = 1.5,
    translation = c(-180, -50), radius = 50, units = "mi"
  ))