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)

Multiple visual variables

Usually we specify a data-driven visual variable with one data variable (see vignette about visual variables). However, in several use cases, it is useful to use a few data variables for one visual variables.

There are two ways to use multiple data variables for one visual variable: for creating facets, and for multivariate mapping.

Creating facets

Recall from the vigentte about facets

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

A facet is create for each specified data variable. More options to select variables are available via the underlying function tm_vars(). For instance, variables 12 to 18 (so columns 12 to 18, disregarding the geometry column)

tm_shape(NLD_muni) +
  tm_polygons(
    fill = tm_vars(12:18))

Or the first 3 variables:

tm_shape(NLD_muni, fig.height = 8) +
  tm_polygons(
    fill = tm_vars(n = 3))

1. For creating facets. This is the standard way.
2. For multivariate mapping.

These cases can be divived into two g Before going through these cases, there is one important There are two

Multivariate mapping

There are (at least) two use cases:

RGB image

library(stars)
tif = system.file("tif/L7_ETMs.tif", package = "stars")
(L7 = read_stars(tif))

Note that the channels are included in the dimenison "band". We can use the argument dimvalues to select them:

tm_shape(L7) +
    tm_rgb(col = tm_vars(dimvalues = 3:1, multivariate = TRUE))

Alternatively, we can split the stars object:

(L7split = split(L7))

and plot it like this:

tm_shape(L7split) +
    tm_rgb(col = tm_vars(3:1, multivariate = TRUE))

Glyphs

Glyph are small charts plotted as symbols. See the [extention package tmap.glyphs.

library(tmap.glyphs)

ZH_muni = NLD_muni[NLD_muni$province == "Zuid-Holland", ]
ZH_muni$income_middle = 100 - ZH_muni$income_high - ZH_muni$income_low

tm_shape(ZH_muni) +
  tm_polygons() +
  tm_donuts(
    parts = tm_vars(c("income_low", "income_middle", "income_high"), multivariate = TRUE),
    fill.scale = tm_scale_categorical(values = "-pu_gn_div"),             
    size = "population",
    size.scale = tm_scale_continuous(ticks = c(50000, 100000, 250000, 500000))) 

The visual variable parts (introduced in tmap.glyphs) is specified as multivariate visual variable. It specifies the parts (slices) of the donut charts and uses this also for the fill color.

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