Making Publication Quality Inset Maps in R using ggplot2

Experiment coordinates

# coordinates
data <- read.csv("https://raw.githubusercontent.com/MohsinRamay/sampledata/main/GPS_coordinates_for_map.csv") %>% 
  select(-3)

gt(head(data))

Map of Australia

I’ll extract the map of Australia from the ozmaps package using the ozmap function.

# Australia map
sf_aus <- ozmap("states")

Closest city names

I’ll extract the closest city names and their coordinates from the original data set by filtering the first row of each grouped Town data.

# cities 
town <- data %>% 
  arrange(Town) %>% 
  group_by(Town) %>% 
  filter(row_number()==1) %>% 
  ungroup()

gt(town)

Map

At first, I’ll create a simple map for the whole country of Australia with experimental sites. I’ll use different symbols for site types and different colors for weed grasses.

Raw Map

data %>% 
  ggplot() + 
  geom_sf(data = sf_aus) +
  geom_point(aes(x = Longitude, y = Latitude, color = Weed.species, shape = Site)) +
  xlim(112, 155) +
  labs() +
  theme_bw()

As you can see, the study sites are clustered (in New South Wales) when plotted on the country-wide scaled map. However, to make a better sense of the study locations with reference to nearby cities/towns, we need to plot them on a focused scale. For that purpose, we first need to identify the extent of the study sites.

Extent

I’ll used simple horizontal and vertical lines to visualize the extent of study sites.

data %>% 
  ggplot() + 
  geom_sf(data = sf_aus) +
  geom_point(aes(x = Longitude, y = Latitude, color = Weed.species, shape = Site)) +
  xlim(112, 155) +
  geom_hline(yintercept = -38, lty = 2, colour = "red") +
  geom_hline(yintercept = -29, lty = 2, colour = "red") +
  geom_vline(xintercept = 147, lty = 2, colour = "red") +
  geom_vline(xintercept = 153, lty = 2, colour = "red") +
  labs() +
  theme_bw()

Using the min and max values of coordinates from previous map, we can draw a polygon over the study sites and see if this extent can best visualize the data.

data %>% 
  ggplot() + 
  geom_sf(data = sf_aus) +
  geom_point(aes(x = Longitude, y = Latitude, color = Weed.species, shape = Site)) +
  xlim(112, 155) +
  geom_rect(aes(xmin = 147, xmax = 153, ymin = -38, ymax = -29), color = "red", fill = NA) +
  labs() +
  theme_bw()

Focus Map

Now, I’ll plot a focused version of the study sites using the extent coordinates determined in the previous steps.

data %>% 
  mutate(point_size = ifelse(Site == "Demonstration", 1, 0)) %>% 
  ggplot() + 
  geom_sf(data = sf_aus) +
  geom_point(aes(x = Longitude, y = Latitude, color = Weed.species, shape = Site, size = point_size > 0), alpha = 0.75) +
  scale_size_manual(values=c(2,3.5)) +
  xlim(147, 153) +
  ylim(-38, -30) +
  theme_test() +
  guides(size = "none")

This version looks better compared to the previous one. However, we need to add some information to give it a reference. Also, we need to remove the borderline of Canberra in the southern part of New South Wales. For that purpose, I’ll dissolve the boundary line using the st_union function of library sf. We’ll then use the dissolved polygon in subsequent maps.

# Now the dissolve
library(sf)

AN = sf_aus %>% 
  filter(NAME %in% c("New South Wales", "Australian Capital Territory"))

NM <- st_union(AN)

plot(NM)

Add Map elements

Professional maps also include some elements like North Arrow and scale etc. We’ll add these components to our map as well. Besides that, I’ll also add the closest town names and fill the non-land area with lightblue color for reference and distinction respectively.

ss <- data %>% 
  mutate(point_size = ifelse(Site == "Demonstration", 1, 0)) %>% 
  ggplot() + 
  geom_sf(data = NM) +
  geom_point(aes(x = Longitude, y = Latitude, color = Weed.species, shape = Site, size = point_size > 0), alpha = 0.75) +
  scale_size_manual(values=c(2,3.5)) +
  xlim(147, 153) +
  ylim(-38, -30) +
  ggrepel::geom_text_repel(aes(x = Longitude, y = Latitude, label = Town), data = town, nudge_y = 0.06, nudge_x = 0.06, size = 2) +
  labs(color = "Weed species") +
  theme_test() +
  theme(panel.background = element_rect(fill = "lightblue")) +
  guides(size = "none") +
  ggsn::north(location = "topleft", scale = 0.8, symbol = 12,
               x.min = 151.5, x.max = 152.5, y.min = -36, y.max = -38) +
  ggsn::scalebar(location = "bottomleft", dist = 100,
           dist_unit = "km", transform = TRUE, 
           x.min=150.5, x.max=152, y.min=-38, y.max=-30,
           st.bottom = FALSE, height = 0.025,
           st.dist = 0.05, st.size = 3)

ss

Inset Map

Now, I’ll create a full-scale map of Australia with a red polygon showing the extent of study sites and the focused map.

# Map of Australia
inset <- sf_aus %>% 
  ggplot() + 
  geom_sf() +
  #geom_sf(data = NM) +
  geom_rect(aes(xmin = 147, xmax = 153, ymin = -38, ymax = -30), color = "red", fill = NA) +
  xlim(112, 155) +
  labs(x = NULL, y = NULL) +
  theme_test() + 
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.ticks.length = unit(0, "pt"),
        axis.title=element_blank(),
        plot.margin = margin(0, 0, 0, 0, "cm"),
        panel.background = element_rect(fill = "lightblue"))

inset

Final Map

Now, I’ll combine both maps where the map of Australia is inset on the upper left corner.

ss

# Combining both maps
print(inset, vp = viewport(0.322, 0.859, width = 0.25, height = 0.25))

This inset map better shows the locations of study sites with reference to the country and provinces and is more professional.

That’s it!

Feel free to reach me out if you got any questions.