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)
tmap_options(scale = 0.75)

About the data

Three spatial data objects of the Netherlands are contained in tmap: NLD_prov, NLD_muni, and NLD_dist, at respectively province, municipality and district level. The columns are the following data variables plus an additional geometry column which contains the geometries (see sf package):

names(NLD_muni)

Facets: one dimensional

Facets or small multiples are small visualizations (maps), one for each group. The groups can be defined using a categorical data variable. In the following plot, a facet is created for each province (a data variable in NLD_muni):

tm_shape(NLD_muni) +
  tm_polygons("employment_rate") +
tm_facets("province")

In this example, there is one facet variable. For these kind of plots, and the facets are either wrapped or stacked, with tm_facets_wrap() or tm_facets_stack(). The general purpose function tm_facets() determines the faceting type, which is in this example wrap.

Groups can also be defined by specifying multiple data variable names for one visual variable:

tm_shape(NLD_muni) +
  tm_polygons(
    fill = c("employment_rate", "dwelling_value", "income_high"),
    fill.scale = 
      list(tm_scale_intervals(values = "-brewer.reds"),
         tm_scale_intervals(values = "brewer.purples"),
         tm_scale_intervals(values = "brewer.blues")))

Because different data variables are used, tmap uses (by default) multiple scales, one for each variable. These can be changed by putting the tm_scale() functions in a list, as shown above. In case the same scale should be used, fill.free should be set to FALSE:

tm_shape(NLD_muni) +
  tm_polygons(
    fill = c("pop_0_14", "pop_25_44", "pop_65plus"),
    fill.legend = tm_legend("Percentage"),
    fill.free = FALSE)

Facets: two or three dimensions

Facets can also be defined for two or three dimensions, obtained via tm_facets_grid() These three dimensions correspond to rows, columns, and pages respectively. The most common use case is two just dimensions, rows and columns. In that case, tm_facets_grid() is used.

tm_shape(NLD_muni) +
  tm_polygons(
    fill = c("employment_rate", "dwelling_value", "income_high"),
    fill.scale = 
      list(tm_scale_intervals(values = "-brewer.reds"),
         tm_scale_intervals(values = "brewer.purples"),
         tm_scale_intervals(values = "brewer.blues"))) +
tm_facets_grid(columns = "province")

Facets as 'pages' (use case: animations)

(tm = tm_shape(NLD_muni) +
  tm_polygons(
    fill = c("pop_0_14", "pop_15_24", "pop_25_44", "pop_45_64", "pop_65plus"),
    fill.legend = tm_legend("Percentage"),
    fill.free = FALSE) +
tm_facets_pagewise())

will produce 5 plots, one for each age class (defined using the fill argument).

This plot can be exported as animation via tmap_animation.

tmap_animation(tm, filename = "NLD_pop_age_class.gif")

r-tmap/tmap documentation built on Feb. 28, 2025, 7:54 a.m.