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"

    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)

# 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))