[R Course] Data Visualization with R: Maps

R Courses Data Visualization

Let’s concentrate on using ggplot2 data for maps and creating maps.

Thierry Warin https://warin.ca/aboutme.html (HEC Montréal and CIRANO (Canada))https://www.hec.ca/en/profs/thierry.warin.html
08-16-2019

There is a multitude ways of getting data to create maps. In the Data Visualization with R course, you’ve seen some of them.

As you know, the ggplot2 package allows you to produce maps. It also provides data for maps, like longitude and latitude of each countries. It will be easy to turn these data into a data frame suitable for plotting with ggplot2.

World

Let’s start with a basic map of the world.

Step 1: Load packages

Step 2: Retrieve data

For now, we only use the data of the world to create a map of the world. Later, we’ll see maps for particular region of the world like continents, countries, states or even county!

world <- map_data("world")

Step 3: Create maps

ggplot(data = world, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  theme_void()

Continents

Europe

Step 1: Load packages

Step 2: Retrieve data

Getting the world data as in the previous World section.

world <- map_data("world")

As you may have guessed, we want to produce a map of Europe. We listed Europe countries to create a map of Europe.

europe <- subset(world, region %in% c("Albania", "Andorra", "Armenia", "Austria", "Azerbaijan",
                                      "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria",
                                      "Croatia", "Cyprus", "Czechia","Denmark","Estonia","Finland", 
                                      "France","Georgia", "Germany", "Greece","Hungary","Iceland", 
                                      "Ireland", "Italy","Kazakhstan", "Kosovo", "Latvia","Liechtenstein", 
                                      "Lithuania", "Luxembourg","Malta","Moldova","Monaco","Montenegro",
                                      "Macedonia", "Netherlands","Norway","Poland","Portugal","Romania",
                                      "Russia","San Marino","Serbia","Slovakia","Slovenia","Spain",
                                      "Sweden","Switzerland","Turkey","Ukraine","UK","Vatican"))

Step 3: Create maps

ggplot(data = europe, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  theme_void()

The map is too focused on the eastern part. We added some options to refocus the map.

ggplot(data = europe, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  theme_void() +
  coord_fixed(ratio=1.6, xlim = c(-50, 80))

We used the coord_fixed() function to adjust the plot. The default ratio is 1. We used 1.6 as the ration. Honestly, it’s just a “try and error” method. The xlim option allows to cut the map horizontally (To cut the map vertically use the ylim option). The number -50 cut the map on the left part of the map. The number 80 cut the map on the right part of the map. Of course, you can cut more or less than 50 on the left side and more or less than 80 for the right side. To see the details of the function.

To keep the entire map without cutting the eastern part like the map above, but with a better focus, here the following code:

ggplot(data = europe, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  theme_void() +
  coord_fixed(ratio=1.5, xlim = c(-15,180), ylim = c(35,80))

Americas

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")
americas <- subset(world, region %in% c("USA","Brazil","Mexico", "Colombia", "Argentina", "Canada",
                                      "Peru","Venezuela","Chile","Guatemala","Ecuador", "Bolivia", "Cuba",
                                      "Honduras", "Paraguay", "Nicaragua","El Salvador", "Costa Rica", "Panama",
                                      "Uruguay",  "Jamaica",  "Trinidad and Tobago", "Guyana", "Suriname", "Belize",
                                      "Barbados", "Saint Lucia", "Grenada", "Saint Vincent and the Grenadines", 
                                      "Antigua and Barbuda", "Saint Kitts and Nevis"))

Step 3: Create maps

ggplot(data = americas, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(ratio=1.1, xlim = c(-180, -35))  + 
  theme_void() 

Oceania

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")
oceania <- subset(world, region %in% c("Australia", "Fiji", "Kiribati", "Marshall Islands", "Micronesia", 
                                      "Nauru", "New Zealand", "Palau"," Papua New Guinea", "Samoa", 
                                      "Solomon Islands", "Tonga", "Tuvalu", "Vanuatu"))

Step 3: Create maps

ggplot(data = oceania, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(xlim = c(110,180)) +
  theme_void()

Asia

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")
asia <- subset(world, region %in% c("Afghanistan", "Armenia", "Azerbaijan", "Bahrain", "Bangladesh", "Bhutan", 
                                    "Brunei", "Cambodia", "China", "Cyprus", "Georgia", "India", "Indonesia", 
                                    "Iran", "Iraq", "Israel", "Japan", "Jordan", "Kazakhstan", "Kuwait", 
                                    "Kyrgyzstan", "Laos", "Lebanon", "Malaysia", "Maldives", "Mongolia", "Myanmar", 
                                    "Nepal", "North Korea", "Oman", "Pakistan", "Palestine", "Philippines", "Qatar", 
                                    "Russia", "Saudi Arabia", "Singapore", "South Korea", "Sri Lanka", "Syria", 
                                    "Taiwan", "Tajikistan", "Thailand", "Timor-Leste", "Turkey", "Turkmenistan", 
                                    "United Arab Emirates", "Uzbekistan", "Vietnam", "Yemen"))

Step 3: Create maps

ggplot(data = asia, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.2) +
  theme_void()

Africa

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")
africa <- subset(world, region %in% c("Algeria","Angola","Benin","Botswana","Burkina Faso","Burundi",
                                      "Cabo Verde","Cameroon","Central African Republic","Chad","Comoros",
                                      "Democratic Republic of the Congo","Republic of Congo","Ivory Coast",
                                      "Djibouti","Egypt","Equatorial Guinea","Eritrea","Swaziland","Ethiopia",
                                      "Gabon","Gambia","Ghana","Guinea","Guinea-Bissau","Kenya","Lesotho","Liberia",
                                      "Libya","Madagascar","Malawi","Mali","Mauritania","Mauritius","Morocco",
                                      "Mozambique","Namibia","Niger","Nigeria","Rwanda","Sao Tome and Principe",
                                      "Senegal","Seychelles","Sierra Leone","Somalia","South Africa","South Sudan",
                                      "Sudan","Tanzania","Togo","Tunisia","Uganda","Zambia","Zimbabwe"))

Step 3: Create maps

ggplot(data = africa, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed() + 
  theme_void() 

To color a country (in this case Nigeria):

nigeria <- subset(world, region %in% c("Nigeria"))
ggplot(data = africa, aes(x = long, y = lat, group = group)) + 
  geom_polygon(data = africa, fill = "white", color = "black") +
  geom_polygon(data = nigeria, fill = "gray", color = "black") +
  coord_fixed() + 
  theme_void() 

Countries

Nigeria

Step 1: Load packages

To color a specific country in a map, here how to do it:

Step 2: Retrieve data

nigeria <- map_data("world", region = "Nigeria")

Step 3: Create maps

To only keep the map of Nigeria:

ggplot(data = nigeria, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed() + 
  theme_void() 

Canada

Step 1: Load packages

Step 2: Retrieve data

canada <- map_data("world", region = "Canada")

Step 3: Create maps

ggplot(data=canada, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", color="black")+
  coord_fixed(1.8) + 
  theme_void()

China

Step 1: Load packages

Step 2: Retrieve data

china <- map_data("world", region = "China")

Step 3: Create maps

ggplot(china, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", color="black")+
  coord_fixed(1.3) + 
  theme_void() 

United States

Step 1: Load packages

Step 2: Retrieve data

usa <- map_data("world", region = "USA")

Step 3: Create maps

ggplot(data=usa, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", color = "black")+
  coord_fixed(1.8, xlim = c(-180,0)) + 
  theme_void()

If you don’t like the appearance of the map, here an alternative.

  1. We only keep the mainland:
(mainland <- ggplot(data=usa, aes(x = long, y = lat, group = group)) +
              geom_polygon(fill = "white", color = "black")+
              coord_fixed(1.5, xlim = c(-125,-68), ylim = c(50,22)) + 
              theme_void())

  1. We keep the Alaska:
(alaska <- ggplot(data = usa, aes(x = long, y = lat, group = group)) +
              geom_polygon(fill = "white", color = "black") +
              coord_fixed(1.6, xlim = c(-178,-132), ylim = c(52,71)) + 
              theme_void())

  1. We keep Hawaii:
(hawaii <- ggplot(data=usa, aes(x = long, y = lat, group = group)) +
            geom_polygon(fill = "white", color = "black") +
            coord_fixed(1.5, xlim = c(-162,-153), ylim = c(23,18)) + 
            theme_void())

  1. We put it together:
ratioAlaska <- (25 - 2) / (16 - (-24))
ratioHawaii  <- (23 - 18) / (-154 - (-161))

ggdraw(mainland) +
  draw_plot(alaska, width = 0.26, height = 0.26 * 10/6 * ratioAlaska, x = 0.1, y = 0.01) +
  draw_plot(hawaii, width = 0.15, height = 0.15 * 10/6 * ratioHawaii, x = 0.3, y = 0.01)

States

All US states

Step 1: Load packages

Step 2: Retrieve data

usa <- map_data("world", region = "USA")

usa_mainland <- subset(usa, subregion != "Alaska")
usa_mainland <- subset(usa_mainland, subregion != "Hawaii")

usa_states <- map_data("state")

Step 3: Create maps

ggplot(data = usa_mainland, aes(x = long, y = lat, group = group)) + 
  geom_polygon(data = usa_states, fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

To color a state:

nevada <- subset(usa_states, region == "nevada")
ggplot(data = usa_states, aes(x = long, y = lat, group = group)) + 
  geom_polygon(data = usa_states, fill = "white", color = "black") +
  geom_polygon(data = nevada, fill = "gray", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

Nevada

Step 1: Load packages

Step 2: Retrieve data

nevada <- subset(usa_states, region == "nevada")

Step 3: Create maps

ggplot(data = nevada, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

Texas

Step 1: Load packages

Step 2: Retrieve data

texas <- subset(usa_states, region == "texas")

Step 3: Create maps

To only keep a state (in this case Texas):

ggplot(data = texas, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

California

Step 1: Load packages

Step 2: Retrieve data

california <- subset(usa_states, region == "california")

Step 3: Create maps

ggplot(data = california, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

County

Los Angeles

Step 1: Load packages

Step 2: Retrieve data

usa_states <- map_data("state")
california <- subset(usa_states, region %in% c("california"))

usa_counties <- map_data("county")
california_counties <- subset(usa_counties, region == "california")

losAngeles <- subset(california_counties, subregion == "los angeles")

Step 3: Create maps

ggplot(data = california, aes(x = long, y = lat, group = group)) + 
  geom_polygon(data = california_counties, fill = "white", color = "black") +
  geom_polygon(data = losAngeles, fill = "gray", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

To only keep the county map:

ggplot(data = losAngeles, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

San Francisco

Step 1: Load packages

Step 2: Retrieve data

usa_counties <- map_data("county")
california_counties <- subset(usa_counties, region == "california")

sanFrancisco <- subset(california_counties, subregion == "san francisco")

Step 3: Create maps

ggplot(data = sanFrancisco, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black") +
  coord_fixed(1.4) + 
  theme_void() 

Associations and Agreements

European Union

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")

european.union.countries <- c("Bulgaria","Cyprus","Estonia","Finland","Greece","Ireland","Latvia",
                              "Lithuania","Luxembourg","Malta","Romania" ,"Sweden","Portugal", 
                              "Spain", "France", "Switzerland", "Germany","Austria", "Belgium", 
                              "UK", "Netherlands", "Denmark", "Poland", "Italy", "Croatia", 
                              "Slovenia", "Hungary", "Slovakia", "Czech republic")

european.union <- map_data("world", region = european.union.countries)

Step 3: Create maps

ggplot(european.union, aes(x = long, y = lat)) +
  geom_polygon(aes(group = group), fill = "white", color="black") +
  coord_fixed(1.2) + 
  theme_void() 

NAFTA

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")

NAFTA.countries <- c("USA","Canada", "Mexico")

NAFTA <- map_data("world", region = NAFTA.countries)

Step 3: Create maps

ggplot(NAFTA, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill = "white", color="black") +
  coord_fixed(1.2, xlim = c(-175,-55)) +
  theme_void()

Campuses

SKEMA

Step 1: Load packages

Step 2: Retrieve data

world <- map_data("world")

world <- subset(world, region != "Antarctica")
world <- subset(world, region != "French Southern and Antarctic Lands")

skema.campuses <- subset(world, region %in% c("France", "USA", "China", "South Africa", "Brazil"))

skema.campuses <- subset(skema.campuses, !(subregion %in% c("Alaska")))

Step 3: Create maps

# Produce a World map
ggplot(data = world, aes(x = long, y = lat, group = group)) + 
  geom_polygon(fill = "white", color = "black", size = 0.3) +
  geom_polygon(data = skema.campuses, fill = "gray", color = "black", size = 0.3) +
  coord_fixed(1.5) +
  theme_void()


Citation

For attribution, please cite this work as

Warin (2019, Aug. 16). www.warin.ca: [R Course] Data Visualization with R: Maps. Retrieved from https://warin.ca/posts/rcourse-datavisualizationwithr-maps/

BibTeX citation

@misc{warin2019[r,
  author = {Warin, Thierry},
  title = {www.warin.ca: [R Course] Data Visualization with R: Maps},
  url = {https://warin.ca/posts/rcourse-datavisualizationwithr-maps/},
  year = {2019}
}