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Donut and pie maps

Source: vignettes/donuts.Rmd
donuts.Rmd

Glyphs in spatial data visualization are graphical symbols that represent data values at specific geographic locations. Each glyph can encode multiple data variables.

With the extension package tmap.glyphs (in development) glyphs can be created. Currently the donut, the pie and the flower glyphs are implemented. More will follow.

In this example we explore the proportion of low, middle and high incomes in Zuid-Holland, a province of the Netherlands (including The Hague and Rotterdam). We use the dataset NLD_muni from tmap, where we apply a filter and subsequently derive the percentage of middle income:

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

Pie maps

Pie maps are (just) a special case of donut maps.

tm_shape(ZH_muni) +
  tm_polygons() +
  tm_pies(parts = tm_vars(c("income_low", "income_middle", "income_high"), multivariate = TRUE),
    fill.scale = tm_scale_categorical(values = "-pu_gn_div"),             
    size = "population",
    lwd = 1,
    size.scale = tm_scale_continuous(ticks = c(50000, 100000, 250000, 500000)))

Explanation:

  • The visual variable parts is needed to determine the cut the pies. Note that a multivariate variable is required (see vignette).
  • The fill colors of the parts are specified via fill.scale (and not via parts). The used scale should be a categorical; each part represents a category.
  • The visual variable size represents the overall sizes of the pies.

Donut maps

Pies are just a specific case of the more general donuts.

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",
    lwd = 1,
    size.scale = tm_scale_continuous(ticks = c(50000, 100000, 250000, 500000)))

Via the argument options, several properties of the donuts can be specified:

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 = c("limegreen", "steelblue", "brown"), value.neutral = "grey50"),           
    size = "population",
    lwd = 1,
    size.scale = tm_scale_continuous(ticks = c(50000, 100000, 250000, 500000)),
    options = opt_tm_donuts(start = 45, direction = -1, inner = 0.2, fill_hole = "orange"))

Explanation:

  • value.neutral is used for legends of other visual variables (in this case size). See vignette on neutral values
  • For each tmap layer, options is used for option specific settings. The default list is obtained via opt_tm_<layer>.