9  Human geography lab 2

The following packages will be required for this chapter:

pacman::p_load(tidyverse, maps)

9.0.1 World map

• The maps package contains data for the world, USA, France, Italy and New Zealand. It contains information such as latitude, longitude, group, region, etc. like this.

• First call the world map data

• You can see the data structure by using head command.

world_map <- map_data("world")
head(world_map)

       long      lat group order region subregion
1 -69.89912 12.45200     1     1  Aruba      <NA>
2 -69.89571 12.42300     1     2  Aruba      <NA>
3 -69.94219 12.43853     1     3  Aruba      <NA>
4 -70.00415 12.50049     1     4  Aruba      <NA>
5 -70.06612 12.54697     1     5  Aruba      <NA>
6 -70.05088 12.59707     1     6  Aruba      <NA>

• By using the ggplot2 boundaries can be drawn with geom_polygon() with latitude and longitude as shown in Figure 9.1

world_map %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "lightgray", colour = "black", size = 0.1) 

Figure 9.1: World countries

9.0.2 Japan map

• If we limit the data with filter(), we can also draw a map of specific countries.

• For instance, we will filter the data for Japan and draw a map as shown in Figure 9.2

world_map %>% 
  filter(region == "Japan") %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "lightgray", colour = "black", size = 0.1)

Figure 9.2: Japan

• We will change the color of Japan map and draw a map as shown in Figure 9.3

world_map %>% 
  filter(region == "Japan") %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "blue", colour = "black", size = 0.1)

Figure 9.3: Japan

• We will filter the data for China and draw a map as shown in Figure 9.4

world_map %>% 
  filter(region == "China") %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "red", colour = "black", size = 0.1)

Figure 9.4: China

• We will filter the data for Brazil and draw a map as shown in Figure 9.5

world_map %>% 
  filter(region == "Brazil") %>% 
  ggplot(aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "green", colour = "black", size = 0.1)

Figure 9.5: Brazil