Treemap Charts in ggplot2

Basic tree map

treemapify includes an example dataset containing statistics about the G-20 group of major world economies.

In a treemap, each tile represents a single observation, with the area of the tile proportional to a variable. Let’s start by drawing a treemap with each tile representing a G-20 country. The area of the tile will be mapped to the country’s GDP, and the tile’s fill colour mapped to its HDI (Human Development Index). geom_treemap() is the basic geom for this purpose.

library(plotly)
library(treemapify)
library(ggplot2)

p <- ggplot(G20, aes(area = gdp_mil_usd, fill = hdi)) +
  geom_treemap()

p

This plot isn’t very useful without the knowing what country is represented by each tile. geom_treemap_text() can be used to add a text label to each tile. It uses the ggfittext package to resize the text so it fits the tile. In addition to standard text formatting aesthetics you would use in geom_text(), like fontface or colour, we can pass additional options specific for ggfittext. For example, we can place the text in the centre of the tile with place = "centre", and expand it to fill as much of the tile as possible with grow = TRUE.

Adding legend

library(plotly)
library(treemapify)
library(ggplot2)

p <- ggplot(G20, aes(area = gdp_mil_usd, fill = hdi, label = country)) +
  geom_treemap() +
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
                    grow = TRUE)

p

Note that several tiles in the top right corner have no labels. geom_treemap_text() will hide text labels that cannot fit a tile without being shrunk below a minimum size, by default 4 points. This can be adjusted with the min.size argument.

Subgrouping tiles

geom_treemap() supports subgrouping of tiles within a treemap by passing a subgroup aesthetic. Let’s subgroup the countries by region, draw a border around each subgroup withgeom_treemap_subgroup_border(), and label each subgroup with geom_treemap_subgroup_text(). geom_treemap_subgroup_text() takes the same arguments for text placement and resizing as geom_treemap_text().

library(plotly)
library(treemapify)
library(ggplot2)

p <- ggplot(G20, aes(area = gdp_mil_usd, fill = hdi, label = country,
                subgroup = region)) +
  geom_treemap() +
  geom_treemap_subgroup_border() +
  geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5, colour =
                             "black", fontface = "italic", min.size = 0) +
  geom_treemap_text(colour = "white", place = "topleft", reflow = T)

p

Up to three nested levels of subgrouping are supported with the subgroup2 and subgroup3 aesthetics. Borders and text labels for these subgroups can be drawn with geom_treemap_subgroup2_border(), etc. Note that ggplot2 draws plot layers in the order that they are added. This means it is possible to accidentally hide one layer of subgroup borders with another. Usually, it’s best to add the border layers in order from deepest to shallowest, i.e. geom_treemap_subgroup3_border() then geom_treemap_subgroup2_border() then geom_treemap_subgroup_border().

library(plotly)
library(treemapify)
library(ggplot2)

p <- ggplot(G20, aes(area = 1, label = country, subgroup = hemisphere,
                subgroup2 = region, subgroup3 = econ_classification)) +
  geom_treemap() +
  geom_treemap_subgroup3_border(colour = "blue", size = 1) +
  geom_treemap_subgroup2_border(colour = "white", size = 3) +
  geom_treemap_subgroup_border(colour = "red", size = 5) +
  geom_treemap_subgroup_text(
    place = "middle",
    colour = "red",
    alpha = 0.5,
    grow = T
  ) +
  geom_treemap_subgroup2_text(
    colour = "white",
    alpha = 0.5,
    fontface = "italic"
  ) +
  geom_treemap_subgroup3_text(place = "top", colour = "blue", alpha = 0.5) +
  geom_treemap_text(colour = "white", place = "middle", reflow = T)

p

As demonstrated, there is no assurance that the resulting plot will look good.

Like any ggplot2 plot, treemapify plots can be faceted, scaled, themed, etc.

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)