This document explains plotting geospatial data using ggplot2 and {ggfortify}.
{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"
autoplot(jp)
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)
{sp} packageAlso, {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()
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))
