In r-tmap/tmap: Thematic Maps

knitr::opts_chunk$set(
  collapse = TRUE,
  out.width = "100%",
  dpi = 300,
  fig.width = 7.2916667,
  comment = "#>"
)
hook_output <- knitr::knit_hooks$get("output")
knitr::knit_hooks$set(output = function(x, options) {
   lines <- options$output.lines
   if (is.null(lines)) {
     return(hook_output(x, options))  # pass to default hook
   }
   x <- unlist(strsplit(x, "\n"))
   more <- "..."
   if (length(lines)==1) {        # first n lines
     if (length(x) > lines) {
       # truncate the output, but add ....
       x <- c(head(x, lines), more)
     }
   } else {
     x <- c(more, x[lines], more)
   }
   # paste these lines together
   x <- paste(c(x, ""), collapse = "\n")
   hook_output(x, options)
 })

library(tmap)
library(dplyr)
library(sf)
tmap_options(scale = 0.75)

About the data

We use several spatial data object that are contained in tmap. The first is called World, which we only use for country borders. We'll only select the African countries:

metroAfrica = sf::st_intersection(metro, World[World$continent == "Africa", ])
Africa = World[World$continent == "Africa", ]

The second is land which is a stars object that contains land cover. The third object is metro an sf object with metropolitan areas. The fourth and final ovject is World_rivers.

Topographic map

tm_shape(land) +
  tm_raster("cover_cls",
    col.legend = tm_legend("Land use")) +
tm_shape(World_rivers) +
  tm_lines(
    lwd = "strokelwd", 
    lwd.scale = tm_scale_asis(values.scale = .5),
    col = "#A6CEE3") +
tm_shape(Africa, is.main = TRUE) +
  tm_borders() +
tm_shape(metroAfrica) +
  tm_symbols(fill = "red", shape = "pop2020", size = "pop2020",
    size.scale = tm_scale_intervals(
      breaks = c(1, 2, 5, 10, 15, 20, 25) * 1e6,
      values.range = c(0.2,2)
    ),
    size.legend = tm_legend("Population in 2020"),
    shape.scale = tm_scale_intervals(
      breaks = c(1, 2, 5, 10, 15, 20, 25) * 1e6,
      values = c(21, 23, 22, 21, 23, 22)
    ),
    shape.legend = tm_legend_combine("size")) +
  tm_labels("name") +
tm_credits("United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects.\nProduction of Global Land Cover Data - GLCNMO2008.", position = tm_pos_out("center", "bottom"))

Breaking down in layers

If this code chunk is overwhelming, let's that a look at the layer groups:

Land use layer

tm = 
tm_shape(land) +
  tm_raster("cover_cls",
    col.legend = tm_legend("Land use"))
tm

Adding rivers

tm = tm + 
tm_shape(World_rivers) +
  tm_lines(
    lwd = "strokelwd", 
    lwd.scale = tm_scale_asis(values.scale = .5),
    col = "#A6CEE3")
tm

African countries

Note that the first two layers are from a global data set (so not just Africa). Note that in the final plot this raster is cropped to the bounding box of Africa. We'll use is.main for the the spatial object Africa to do the actual cropping.

tm = tm +
tm_shape(Africa, is.main = TRUE) +
  tm_borders()
tm

African cities

tm = tm +
tm_shape(metroAfrica) +
  tm_symbols(fill = "red", shape = "pop2020", size = "pop2020",
    size.scale = tm_scale_intervals(
      breaks = c(1, 2, 5, 10, 15, 20, 25) * 1e6,
      values.range = c(0.2,2)
    ),
    size.legend = tm_legend("Population in 2020"),
    shape.scale = tm_scale_intervals(
      breaks = c(1, 2, 5, 10, 15, 20, 25) * 1e6,
      values = c(21, 23, 22, 21, 23, 22)
    ),
    shape.legend = tm_legend_combine("size")) +
  tm_labels("name")
tm