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This tutorial is the third part in a series of three:

After the presentation of the basic map concepts, and the flexible approach in layer implemented in ggplot2, this part illustrates how to achieve complex layouts, for instance with map insets, or several maps combined. Depending on the visual information that needs to be displayed, maps and their corresponding data might need to be arranged to create easy to read graphical representations. This tutorial will provide different approaches to arranges maps in the plot, in order to make the information portrayed more aesthetically appealing, and most importantly, convey the information better.

Getting started

Many R packages are available from CRAN, the Comprehensive R Archive Network, which is the primary repository of R packages. The full list of packages necessary for this series of tutorials can be installed with:

install.packages(c("cowplot", "googleway", "ggplot2", "ggrepel", 
    "ggspatial", "libwgeom", "sf", "rworldmap", "rworldxtra"))

We start by loading the basic packages necessary for all maps, i.e. ggplot2 and sf. We also suggest to use the classic dark-on-light theme for ggplot2 (theme_bw), which is more appropriate for maps:

library("ggplot2")
theme_set(theme_bw())
library("sf")

The package rworldmap provides a map of countries of the entire world; a map with higher resolution is available in the package rworldxtra. We use the function getMap to extract the world map (the resolution can be set to "low", if preferred):

library("rworldmap")
library("rworldxtra")
world <- getMap(resolution = "high")
class(world)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

The world map is available as a SpatialPolygonsDataFrame from the package sp; we thus convert it to a simple feature using st_as_sf from package sf:

world <- st_as_sf(world)
class(world)

## [1] "sf"         "data.frame"

General concepts

There are 2 solutions to combine sub-maps:

  • using Grobs (graphic objects, allow plots only in plot region, based on coordinates), which directly use ggplot2
  • using ggdraw (allows plots anywhere, including outer margins, based on relative position) from package cowplot

Example illustrating the difference between the two, and their use:

(g1  <- qplot(0:10, 0:10))

(g1_void <- g1 + theme_void() + theme(panel.border = element_rect(colour = "black", 
    fill = NA)))

Graphs from ggplot2 can be saved, like any other R object. They can then be reused later in other functions.

Using grobs, and annotation_custom:

g1 +
    annotation_custom(
        grob = ggplotGrob(g1_void),
        xmin = 0,
        xmax = 3,
        ymin = 5,
        ymax = 10
    ) +
    annotation_custom(
        grob = ggplotGrob(g1_void),
        xmin = 5,
        xmax = 10,
        ymin = 0,
        ymax = 3
    )

Using ggdraw (note: used to build on top of initial plot; could be left empty to arrange subplots on a grid; plots are “filled” with their plots, unless the plot itself has a constrained ratio, like a map):

ggdraw(g1) +
    draw_plot(g1_void, width = 0.25, height = 0.5, x = 0.02, y = 0.48) +
    draw_plot(g1_void, width = 0.5, height = 0.25, x = 0.75, y = 0.09)

Several maps side by side or on a grid

Having a way show in a visualization, a specific area can be very useful. Many scientists usually create maps for each specific area individually. This is fine, but there are simpler ways to display what is needed for a report, or publication.

This exmaple is using two maps side by side, including the legend of the first one. It illustrates how to use a custom grid, which can be made a lot more complex with different elements.

First, simplify REGION for the legend:

levels(world$REGION)[7] <- "South America"

Prepare the subplots, #1 world map:

(gworld <- ggplot(data = world) +
     geom_sf(aes(fill = REGION)) +
     geom_rect(xmin = -102.15, xmax = -74.12, ymin = 7.65, ymax = 33.97, 
         fill = NA, colour = "black", size = 1.5) +
     scale_fill_viridis_d(option = "plasma") +
     theme(panel.background = element_rect(fill = "azure"),
         panel.border = element_rect(fill = NA)))

And #2 Gulf map :

(ggulf <- ggplot(data = world) +
     geom_sf(aes(fill = REGION)) +
     annotate(geom = "text", x = -90, y = 26, label = "Gulf of Mexico", 
         fontface = "italic", color = "grey22", size = 6) +
     coord_sf(xlim = c(-102.15, -74.12), ylim = c(7.65, 33.97), expand = FALSE) +
     scale_fill_viridis_d(option = "plasma") +
     theme(legend.position = "none", axis.title.x = element_blank(), 
         axis.title.y = element_blank(), panel.background = element_rect(fill = "azure"), 
         panel.border = element_rect(fill = NA)))

The command ggplotGrob signals to ggplot to take each created map, and how to arrange each map. The argument coord_equal can specify the length, ylim, and width, xlim, for the entire plotting area. Where as in annotation_custom, each maps’ xmin, xmax, ymin, and ymax can be specified to allow for complete customization.

#Creating a faux empty data frame
df <- data.frame()
plot1<-ggplot(df) + geom_point() + xlim(0, 10) + ylim(0, 10)
plot2<-ggplot(df) + geom_point() + xlim(0, 10) + ylim(0, 10)


ggplot() +
  coord_equal(xlim = c(0, 3.3), ylim = c(0, 1), expand = FALSE) +
  annotation_custom(ggplotGrob(plot1), xmin = 0, xmax = 1.5, ymin = 0, 
                    ymax = 1) +
  annotation_custom(ggplotGrob(plot2), xmin = 1.5, xmax = 3, ymin = 0, 
                    ymax = 1) +
  theme_void()

Below is the final map, using the same methodology as the exmaple plot above. Using ggplot to arrange maps, allows for easy and quick plotting in one function of R code.

ggplot() +
    coord_equal(xlim = c(0, 3.3), ylim = c(0, 1), expand = FALSE) +
    annotation_custom(ggplotGrob(gworld), xmin = 0, xmax = 2.3, ymin = 0, 
        ymax = 1) +
    annotation_custom(ggplotGrob(ggulf), xmin = 2.3, xmax = 3.3, ymin = 0, 
        ymax = 1) +
    theme_void()

In the second approach, using cowplot::plot_grid to arrange ggplot figures, is quite versatile. Any ggplot figure can be arranged just like the figure above. There are many commands that allow for the map to have different placements, such as nrow=1 means that the figure will only occupy one row and multiple columns, and ncol=1 means the figure will be plotted on one column and multiple rows. The command rel_widths establishes the width of each map, meaning that the first map gworld will have a relative width of 2.3, and the map ggulf has the relative width of 1.

library("cowplot")
theme_set(theme_bw())

plot_grid(gworld, ggulf, nrow = 1, rel_widths = c(2.3, 1))

Some other commands can adjust the position of the figures such as adding align=v to align vertically, and align=h to align horiztonally.

Note also the existence of get_legend (cowplot), and that the legend can be used as any object.

This map can be save using,ggsave:

ggsave("grid.pdf", width = 15, height =  5)

Map insets

For map insets directly on the background map, both solutions are viable (and one might prefer one or the other depending on relative or absolute coordinates).

Map example using map of the 50 states of the US, including Alaska and Hawaii (note: not to scale for the latter), using reference projections for US maps. First map (continental states) use a 10/6 figure:

usa <- subset(world, ADMIN == "United States of America")
## US National Atlas Equal Area (2163)
## http://spatialreference.org/ref/epsg/us-national-atlas-equal-area/
(mainland <- ggplot(data = usa) +
     geom_sf(fill = "cornsilk") +
     coord_sf(crs = st_crs(2163), xlim = c(-2500000, 2500000), ylim = c(-2300000, 
         730000)))

Alaska map (note: datum = NA removes graticules and coordinates):

## Alaska: NAD83(NSRS2007) / Alaska Albers (3467)
## http://www.spatialreference.org/ref/epsg/3467/
(alaska <- ggplot(data = usa) +
     geom_sf(fill = "cornsilk") +
     coord_sf(crs = st_crs(3467), xlim = c(-2400000, 1600000), ylim = c(200000, 
         2500000), expand = FALSE, datum = NA))

Hawaii map:

## Hawaii: Old Hawaiian (4135)
## http://www.spatialreference.org/ref/epsg/4135/
(hawaii  <- ggplot(data = usa) +
     geom_sf(fill = "cornsilk") +
     coord_sf(crs = st_crs(4135), xlim = c(-161, -154), ylim = c(18, 
         23), expand = FALSE, datum = NA))

Using ggdraw from cowplot (tricky to define exact positions; note the use of the ratios of the inset, combined with the ratio of the plot):

(ratioAlaska <- (2500000 - 200000) / (1600000 - (-2400000)))

## [1] 0.575

(ratioHawaii  <- (23 - 18) / (-154 - (-161)))

## [1] 0.7142857

ggdraw(mainland) +
    draw_plot(alaska, width = 0.26, height = 0.26 * 10/6 * ratioAlaska, 
        x = 0.05, y = 0.05) +
    draw_plot(hawaii, width = 0.15, height = 0.15 * 10/6 * ratioHawaii, 
        x = 0.3, y = 0.05)

This plot can be saved using ggsave:

ggsave("map-us-ggdraw.pdf", width = 10, height = 6)

The same kind of plot can be created using grobs, with ggplotGrob, (note the use of xdiff/ydiff and arbitrary ratios):

mainland +
    annotation_custom(
        grob = ggplotGrob(alaska),
        xmin = -2750000,
        xmax = -2750000 + (1600000 - (-2400000))/2.5,
        ymin = -2450000,
        ymax = -2450000 + (2500000 - 200000)/2.5
    ) +
    annotation_custom(
        grob = ggplotGrob(hawaii),
        xmin = -1250000,
        xmax = -1250000 + (-154 - (-161))*120000,
        ymin = -2450000,
        ymax = -2450000 + (23 - 18)*120000
    )

This plot can be saved using ggsave:

ggsave("map-inset-grobs.pdf", width = 10, height = 6)

The print command can also be used place multiple maps in one plotting area.

To specify where each plot is displayed with the print function, the argument viewport needs to include the maximum width and height of each map, and the minimum x and y coordinates of where the maps are located in the plotting area. The argument just will make a position on how the secondary maps will be displayed. All maps are defaulted the same size, until the sizes are adjusted with width and height.

vp <- viewport(width = 0.37, height = 0.10, x = 0.20, y =0.25, just = c("bottom")) 
vp1<- viewport(width = 0.37, height = 0.10, x = 0.35, y =0.25, just = c("bottom")) 

Theprint function uses the previous specifications that were listed in each plots’ respective viewport, with vp=.

print(mainland)
print(alaska, vp=vp)
print(hawaii, vp=vp1)

Several maps connected with arrows

To bring about a more lively map arrangement, arrows can be used to bring the viewers eyes to specific areas in the plot. The next example will create a map with zoomed in areas, pointed to by arrows.

Firstly, we will create our main map, and then our zoomed in areas.

Site coordinates, same as Tutorial #1:

sites <- st_as_sf(data.frame(longitude = c(-80.15, -80.1), latitude = c(26.5, 
    26.8)), coords = c("longitude", "latitude"), crs = 4326, 
    agr = "constant")

Mainlaind map of Florida, #1:

(florida <- ggplot(data = world) +
     geom_sf(fill = "antiquewhite1") +
     geom_sf(data = sites, size = 4, shape = 23, fill = "darkred") +
     annotate(geom = "text", x = -85.5, y = 27.5, label = "Gulf of Mexico", 
         color = "grey22", size = 4.5) +
     coord_sf(xlim = c(-87.35, -79.5), ylim = c(24.1, 30.8)) +
     xlab("Longitude")+ ylab("Latitude")+
     theme(panel.grid.major = element_line(colour = gray(0.5), linetype = "dashed", 
         size = 0.5), panel.background = element_rect(fill = "aliceblue"), 
         panel.border = element_rect(fill = NA)))

A map for site A is created by layering the map and points we created earlier. ggplot layers geom_sf objects and plot them spatially.

(siteA <- ggplot(data = world) +
     geom_sf(fill = "antiquewhite1") +
     geom_sf(data = sites, size = 4, shape = 23, fill = "darkred") +
     coord_sf(xlim = c(-80.25, -79.95), ylim = c(26.65, 26.95), expand = FALSE) + 
     annotate("text", x = -80.18, y = 26.92, label= "Site A", size = 6) + 
     theme_void() + 
     theme(panel.grid.major = element_line(colour = gray(0.5), linetype = "dashed", 
         size = 0.5), panel.background = element_rect(fill = "aliceblue"), 
         panel.border = element_rect(fill = NA)))

A map for site B:

(siteB <- ggplot(data = world) + 
     geom_sf(fill = "antiquewhite1") +
     geom_sf(data = sites, size = 4, shape = 23, fill = "darkred") +
     coord_sf(xlim = c(-80.3, -80), ylim = c(26.35, 26.65), expand = FALSE) +
     annotate("text", x = -80.23, y = 26.62, label= "Site B", size = 6) + 
     theme_void() +
     theme(panel.grid.major = element_line(colour = gray(0.5), linetype = "dashed", 
         size = 0.5), panel.background = element_rect(fill = "aliceblue"), 
         panel.border = element_rect(fill = NA)))

Coordinates of the two arrows will need to be specified before plotting. The argumemnts x1, and x2 will plot the arrow line from a specific starting x-axis location,x1, and ending in a specific x-axis,x2. The same applies for y1 and y2, with the y-axis respectively:

arrowA <- data.frame(x1 = 18.5, x2 = 23, y1 = 9.5, y2 = 14.5)
arrowB <- data.frame(x1 = 18.5, x2 = 23, y1 = 8.5, y2 = 6.5)

Final map using (ggplot only). The argument geom_segment, will be the coordinates created in the previous script, to plot line segments ending with an arrow using arrow=arrow():

ggplot() +
    coord_equal(xlim = c(0, 28), ylim = c(0, 20), expand = FALSE) +
    annotation_custom(ggplotGrob(florida), xmin = 0, xmax = 20, ymin = 0, 
        ymax = 20) +
    annotation_custom(ggplotGrob(siteA), xmin = 20, xmax = 28, ymin = 11.25, 
        ymax = 19) +
    annotation_custom(ggplotGrob(siteB), xmin = 20, xmax = 28, ymin = 2.5, 
        ymax = 10.25) +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), data = arrowA, 
        arrow = arrow(), lineend = "round") +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), data = arrowB, 
        arrow = arrow(), lineend = "round") +
    theme_void()

This plot can be saved using ggsave:

ggsave("florida-sites.pdf", width = 10, height = 7)

ggdraw could also be used for a similar result, with the argument draw_plot:

ggdraw(xlim = c(0, 28), ylim = c(0, 20)) +
    draw_plot(florida, x = 0, y = 0, width = 20, height = 20) +
    draw_plot(siteA, x = 20, y = 11.25, width = 8, height = 8) +
    draw_plot(siteB, x = 20, y = 2.5, width = 8, height = 8) +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), data = arrowA, 
        arrow = arrow(), lineend = "round") +
    geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2), data = arrowB, 
        arrow = arrow(), lineend = "round")