Getting started with ‘ggautomap’

library(nswgeo)
library(ggautomap)
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)

This article provides some recipes for plots that might be of interest. These examples use map data from the {nswgeo} package.

Many of the examples use the same example dataset modelled after the structure of a linelist: rows are distinct events, and they can have a type of A or B. Each event is associated with a location described at different granularities by the postcode, lga, and lhd columns.

head(covid_cases_nsw)
#> # A tibble: 6 × 5
#>   postcode lga       lhd                 year type 
#>   <chr>    <chr>     <chr>              <int> <chr>
#> 1 2427     Mid-Coast Hunter New England  2022 B    
#> 2 2761     Blacktown Western Sydney      2021 A    
#> 3 2426     Mid-Coast Hunter New England  2022 B    
#> 4 2148     Blacktown Western Sydney      2022 B    
#> 5 2768     Blacktown Western Sydney      2021 A    
#> 6 2766     Blacktown Western Sydney      2021 B

You need to specify which column has the feature by setting the location aesthetic. This example has three different columns of locations for different feature types; your dataset only needs to have one of these.

In general you’ll start with geom_boundaries() to draw the base map. This geom needs to be told which feature_type you’re after (e.g. "nswgeo.lga" for LGAs). All of the summary geoms of ggautomap can then be used to draw your data.

Scatter

covid_cases_nsw %>%
  ggplot(aes(location = lga)) +
  geom_boundaries(feature_type = "nswgeo.lga") +
  geom_geoscatter(aes(colour = type), sample_type = "random", size = 0.5) +
  coord_automap(feature_type = "nswgeo.lga", xlim = c(147, 153), ylim = c(-33.7, -29)) +
  guides(colour = guide_legend(override.aes = list(size = 1))) +
  theme_void()

Points are drawn at random within the boundaries of their location.

Insets

To show a zoomed in part of the map as an inset, you can configure an inset and provide it to each relevant geom. The geoms in this package are all inset-aware. See {ggmapinset} for details.

covid_cases_nsw %>%
  ggplot(aes(location = lga)) +
  geom_boundaries(feature_type = "nswgeo.lga") +
  geom_geoscatter(aes(colour = type), size = 0.5) +
  geom_inset_frame() +
  coord_automap(feature_type = "nswgeo.lga", inset = configure_inset(
    centre = "Blacktown", radius = 40, units = "km",
    scale = 7, translation = c(400, -100)
  )) +
  theme_void()

Packed points

This next example uses geom_centroids() to place the points in a packed circle in the centre of each feature. It also shows how you can fine-tune the plot with the usual {ggplot2} functions.

covid_cases_nsw %>%
  dplyr::filter(year >= 2021) %>%
  ggplot(aes(location = lhd)) +
  geom_boundaries(feature_type = "nswgeo.lhd") +
  geom_centroids(aes(colour = type), position = position_circle_repel_sf(scale = 35), size = 1) +
  geom_inset_frame() +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Sydney", radius = 80, units = "km", feature_type = "nswgeo.lhd",
    scale = 6, translation = c(650, -100)
  )) +
  facet_wrap(vars(year)) +
  labs(x = NULL, y = NULL) +
  theme_void() +
  theme(strip.text = element_text(size = 12))

Choropleths

If your data has multiple rows for each location (such as our example dataset where the rows are disease cases) then you can use geom_choropleth() to aggregate these into counts.

covid_cases_nsw %>%
  ggplot(aes(location = lhd)) +
  geom_choropleth() +
  geom_boundaries(
    feature_type = "nswgeo.lhd", colour = "black", linewidth = 0.1,
    outline.aes = list(colour = NA)
  ) +
  geom_inset_frame() +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Western Sydney", radius = 60, units = "km",
    scale = 5, translation = c(400, -100)
  )) +
  scale_fill_steps(low = "#e6f9ff", high = "#00394d", n.breaks = 5, na.value = "white") +
  theme_void()

On the other hand, if your dataset has only one row per location and there is an existing column that you’d like to map to the fill aesthetic, then instead use geom_sf_inset(..., stat = "automap"):

summarised_data <- data.frame(
  lhd = c("Western Sydney", "Sydney", "Far West", "Mid North Coast", "South Western Sydney"),
  cases = c(250, 80, 20, NA, 100)
)

summarised_data %>%
  ggplot(aes(location = lhd)) +
  geom_sf_inset(aes(fill = cases), stat = "automap", colour = NA) +
  geom_boundaries(
    feature_type = "nswgeo.lhd", colour = "black", linewidth = 0.1,
    outline.aes = list(colour = NA)
  ) +
  geom_inset_frame() +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Western Sydney", radius = 60, units = "km",
    scale = 3.5, translation = c(350, 0)
  )) +
  scale_fill_gradient(low = "#e6f9ff", high = "#00394d", na.value = "grey90") +
  theme_void()

Positioning text

These examples give some different ways of placing text, accounting for possible insets.

covid_cases_nsw %>%
  ggplot(aes(location = lhd)) +
  geom_choropleth() +
  geom_boundaries(feature_type = "nswgeo.lhd") +
  geom_inset_frame() +
  geom_sf_label_inset(aes(label = lhd),
    stat = "automap_coords",
    data = ~ dplyr::slice_head(.x, by = lhd)
  ) +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Western Sydney", radius = 60, units = "km",
    scale = 3.5, translation = c(350, 0)
  )) +
  labs(x = NULL, y = NULL) +
  theme_void()

The repulsive labels from {ggrepel} can be used; they just require a bit of massaging since they don’t natively understand the spatial data. Note that you may also wish to use point.padding = NA to disable the default repulsion caused by the labelled points, which is good for labelling scatter plots but often doesn’t make sense in mapping contexts.

library(ggrepel)

# label all features that have data
covid_cases_nsw %>%
  ggplot(aes(location = lhd)) +
  geom_choropleth() +
  geom_boundaries(feature_type = "nswgeo.lhd") +
  geom_inset_frame() +
  geom_label_repel(
    aes(
      x = after_stat(x_inset),
      y = after_stat(y_inset),
      label = lhd
    ),
    stat = "automap_coords",
    nudge_x = 3,
    nudge_y = 1,
    point.padding = NA,
    data = ~ dplyr::slice_head(.x, by = lhd)
  ) +
  scale_fill_distiller(direction = 1) +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Western Sydney", radius = 60, units = "km",
    scale = 3.5, translation = c(350, 0)
  )) +
  labs(x = NULL, y = NULL) +
  theme_void()


# label all features in the map regardless of data, hiding visually overlapping labels
covid_cases_nsw %>%
  ggplot(aes(location = lhd)) +
  geom_choropleth() +
  geom_boundaries(feature_type = "nswgeo.lhd") +
  geom_inset_frame() +
  geom_label_repel(
    aes(
      x = after_stat(x_inset),
      y = after_stat(y_inset),
      geometry = geometry,
      label = lhd_name
    ),
    stat = "sf_coordinates_inset",
    data = cartographer::map_sf("nswgeo.lhd"),
    point.padding = NA,
    inherit.aes = FALSE
  ) +
  scale_fill_distiller(direction = 1, palette = 2) +
  coord_automap(feature_type = "nswgeo.lhd", inset = configure_inset(
    centre = "Western Sydney", radius = 60, units = "km",
    scale = 4, translation = c(500, 0)
  )) +
  labs(x = NULL, y = NULL) +
  theme_void()