How to Create a Map using GGPlot2

World map

Retrieve the world map data:

world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white")

Map for specific regions

1. Retrieve map data for one or multiple specific regions:

# Some EU Contries
some.eu.countries <- c(
  "Portugal", "Spain", "France", "Switzerland", "Germany",
  "Austria", "Belgium", "UK", "Netherlands",
  "Denmark", "Poland", "Italy", 
  "Croatia", "Slovenia", "Hungary", "Slovakia",
  "Czech republic"
)
# Retrievethe map data
some.eu.maps <- map_data("world", region = some.eu.countries)

# Compute the centroid as the mean longitude and lattitude
# Used as label coordinate for country's names
region.lab.data <- some.eu.maps %>%
  group_by(region) %>%
  summarise(long = mean(long), lat = mean(lat))

2. Visualize

ggplot(some.eu.maps, aes(x = long, y = lat)) +
  geom_polygon(aes( group = group, fill = region))+
  geom_text(aes(label = region), data = region.lab.data,  size = 3, hjust = 0.5)+
  scale_fill_viridis_d()+
  theme_void()+
  theme(legend.position = "none")

World map colored by life expectancy

Here, we’ll create world map colored according to the value of life expectancy at birth in 2015. The data is retrieved from the WHO (World Health Organozation) data base using the WHO R package.

1. Retrieve life expectancy data and prepare the data:

library("WHO")
library("dplyr")
life.exp <- get_data("WHOSIS_000001")             # Retrieve the data
life.exp <- life.exp %>%
  filter(year == 2015 & sex == "Both sexes") %>%  # Keep data for 2015 and for both sex
  select(country, value) %>%                      # Select the two columns of interest
  rename(region = country, lifeExp = value) %>%   # Rename columns
  # Replace "United States of America" by USA in the region column
  mutate(
    region = ifelse(region == "United States of America", "USA", region)
    )                                     

2. Merge map and life expectancy data:

world_map <- map_data("world")
life.exp.map <- left_join(life.exp, world_map, by = "region")

3. Create the choropleth map. Note that, data are missing for some region in the map below:

• Use the function geom_polygon():

ggplot(life.exp.map, aes(long, lat, group = group))+
  geom_polygon(aes(fill = lifeExp ), color = "white")+
  scale_fill_viridis_c(option = "C")

• Or use the function geom_map():

ggplot(life.exp.map, aes(map_id = region, fill = lifeExp))+
  geom_map(map = life.exp.map,  color = "white")+
  expand_limits(x = life.exp.map$long, y = life.exp.map$lat)+
  scale_fill_viridis_c(option = "C")

US map colored by violent crime rates

Demo data set: USArrests (Violent Crime Rates by US State, in 1973).

# Prepare the USArrests data
library(dplyr)
arrests <- USArrests 
arrests$region <- tolower(rownames(USArrests))
head(arrests)

##            Murder Assault UrbanPop Rape     region
## Alabama      13.2     236       58 21.2    alabama
## Alaska       10.0     263       48 44.5     alaska
## Arizona       8.1     294       80 31.0    arizona
## Arkansas      8.8     190       50 19.5   arkansas
## California    9.0     276       91 40.6 california
## Colorado      7.9     204       78 38.7   colorado

# Retrieve the states map data and merge with crime data
states_map <- map_data("state")
arrests_map <- left_join(states_map, arrests, by = "region")

# Create the map
ggplot(arrests_map, aes(long, lat, group = group))+
  geom_polygon(aes(fill = Assault), color = "white")+
  scale_fill_viridis_c(option = "C")