Source: vignettes/plot_map.Rmd

This document explains plotting geospatial data using ggplot2 and ggfortify.

Plotting with {maps} package

ggplot2 can load map data provided by maps and {mapdata} package via map_data function. Loaded data is automatically converted to data.frame, thus you can plot maps via ggplot as below.

library(mapdata)
library(ggplot2)

jp <- ggplot2::map_data('world2', 'japan')
class(jp)
## [1] "data.frame"
head(jp)
##       long      lat group order region     subregion
## 1 123.8887 24.28013     1     1  Japan Iriomote Jima
## 2 123.8256 24.26606     1     2  Japan Iriomote Jima
## 3 123.7498 24.28330     1     3  Japan Iriomote Jima
## 4 123.6807 24.28804     1     4  Japan Iriomote Jima
## 5 123.6798 24.31777     1     5  Japan Iriomote Jima
## 6 123.7523 24.34849     1     6  Japan Iriomote Jima
ggplot(jp, aes(x = long, y = lat, group = group)) +
  geom_polygon()

ggfortify additionally allows to autoplot map instances as it is. You can specify geom and other options to controll the outlooks.

library(ggfortify)
jp <-  map('world2', 'japan', plot = FALSE, fill = TRUE)
class(jp)
## [1] "map"

p <- autoplot(jp, geom = 'polygon', fill = 'subregion') + 
  theme(legend.position="none")
p

Also, maps package provides some geospatial data. Following example retrieves Japanese city locations from world’s city locations. Then plot on the previous map.

cities <- get('world.cities')
cities <- cities[cities$country.etc == 'Japan', ]
head(cities)
##         name country.etc    pop   lat   long capital
## 189 Abashiri       Japan  42324 44.02 144.27       0
## 221    Abiko       Japan 132577 35.88 140.03       0
## 481     Ageo       Japan 220766 35.95 139.61       0
## 514      Ago       Japan  23876 34.33 136.82       0
## 571     Agui       Japan  24227 34.95 136.91       0
## 629   Aikawa       Japan  43599 35.55 139.29       0
p + geom_point(data = cities, aes(x = long, y = lat),
               colour = 'blue', size = 0.1)

Because map plot created by ggfortify has a setting of aes(x = long, y = lat), you don’t have to specify x and y aethetics in this case.

p + geom_point(data = cities, colour = 'blue', size = 0.1)

Plotting with {sp} package

Also, ggfortify can supports geospatial instances defined in sp package. Actually some functions are defined in ggplot2. Following table shows where each function is defined.

class fortify autoplot
Line ggplot2 ggfortify
Lines ggplot2 ggfortify
Polygon ggplot2 ggfortify
Polygons ggplot2 ggfortify
SpatialLines ggfortify ggfortify
SpatialLinesDataFrame ggplot2 ggfortify
SpatialPoints ggfortify ggfortify
SpatialPointsDataFrame ggfortify ggfortify
SpatialPolygons ggplot2 ggfortify
SpatialPolygonsDataFrame ggplot2 ggfortify

Following example creates SpatialPointsDataFrame of some Japanese city’s populatios, then plot it via autoplot. Note that you geom is specified by the original sp class. SpatialPoints is plot using geom_point for example.

library(sp)
df <- data.frame(long = c(139.691704, 135.519711),
                 lat = c(35.689521, 34.686316),
                 label = c('Tokyo', 'Osaka'),
                 population = c(1335, 886))
coordinates(df) <- ~ long + lat
class(df)
## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"
autoplot(df, p = p, colour = 'red', size = 10)

Also, you can use other columns as aethetics.

autoplot(df, p = p, colour = 'red', size = 'population') +
  scale_size_area()

Plotting with other packages

autoplot has p keyword to take existing ggplot instance. Below example plots SpatialPointsDataFrame on the ggmap.

library(ggmap)
bbox <- c(130.0, 30.0, 145.0, 45.0)
map <- get_openstreetmap(bbox = bbox, scale = 47500000)
p <- ggmap(map)
autoplot(df, p = p, colour = 'red', size = 'population') + 
  scale_size_area() +
  theme(legend.justification = c(1, 0), legend.position = c(1, 0))

ggmap_output