Treemapify makes it easier to draw treemaps with R and ggplot2.
Walkthrough
Dependencies
Install ggplot2, reshape2, plyr and devtools if you don't have them already.
install.packages("ggplot2")
install.packages("reshape2")
install.packages("plyr")
install.packages("devtools")Install
Install treemapify.
library(devtools)
install_github("wilkox/treemapify")
library(treemapify)Some example data
For any treemap, you'll need a data frame containing at least the following columns:
- A numeric column, which will determine the area of each treemap rectangle; and
- Another column, either numeric or factor, which will determine the fill colour of each treemap rectangle.
For this example, we'll also use a couple of optional columns:
- A factor column, containing the labels for each rectangle; and
- A factor column, containing group names to group the rectangles.
Let's plot some economic data for the G-20 group of major world economies.
data(G20)
print(G20)
# Region Country Trade.mil.USD Nom.GDP.mil.USD HDI Population
# 1 Africa South Africa 208000 384315 0.629 53000000
# 2 North America United States 3969000 15684750 0.937 316173000
# 3 North America Canada 962600 1819081 0.911 34088000
# 4 North America Mexico 756800 1177116 0.775 112211789
# 5 South America Brazil 494800 2395968 0.730 201032714
# 6 South America Argentina 152690 474954 0.811 40117096
# 7 Asia China 3801000 8227037 0.699 1339724852
# 8 Asia Japan 1649800 5963969 0.912 127390000
# 9 Asia South Korea 1068700 1155872 0.909 50004441
# 10 Asia India 809400 1824832 0.554 1210193422
# 11 Asia Indonesia 384100 878198 0.629 237556363
# 12 Eurasia Russia 900600 2021960 0.788 143400000
# 13 Eurasia Turkey 370800 794468 0.722 72561312
# 14 Europe European Union 4567000 16414483 0.876 501259840
# 15 Europe Germany 2768000 3400579 0.920 81757600
# 16 Europe France 1226400 2608699 0.893 65447374
# 17 Europe United Kingdom 1127000 2440505 0.875 62041708
# 18 Europe Italy 953000 2014079 0.881 60325805
# 19 Middle East Saudi Arabia 518300 727307 0.782 27123977
# 20 Oceania Australia 522000 1541797 0.938 22328632
# Economic.classification
# 1 Developing
# 2 Advanced
# 3 Advanced
# 4 Developing
# 5 Developing
# 6 Developing
# 7 Developing
# 8 Advanced
# 9 Advanced
# 10 Developing
# 11 Developing
# 12 Developing
# 13 Developing
# 14 Advanced
# 15 Advanced
# 16 Advanced
# 17 Advanced
# 18 Advanced
# 19 Developing
# 20 AdvancedThe treemapify function
The treemapify function generates coordinates for a treemap in which each observation is represented by a rectangle. In this example, each observation is a country; the rectangle's area will be mapped to the country's nominal GDP, while the fill colour will be mapped to the Human Development Index (HDI). We'll also group the countries by region.
treeMapCoordinates <- treemapify(
G20,
area = "Nom.GDP.mil.USD",
fill = "HDI",
label = "Country",
group = "Region"
)
print(treeMapCoordinates)
# fill label xmin xmax ymin ymax group
# 1 0.876 European Union 0.00000 38.66972 0.00000 58.99641 Europe
# 2 0.920 Germany 38.66972 63.32079 0.00000 19.17284 Europe
# 3 0.893 France 38.66972 63.32079 19.17284 33.88097 Europe
# 4 0.875 United Kingdom 38.66972 63.32079 33.88097 47.64081 Europe
# 5 0.881 Italy 38.66972 63.32079 47.64081 58.99641 Europe
# 6 0.937 United States 0.00000 53.16491 58.99641 100.00000 North America
# 7 0.911 Canada 53.16491 63.32079 58.99641 83.89092 North America
# 8 0.775 Mexico 53.16491 63.32079 83.89092 100.00000 North America
# 9 0.699 China 63.32079 100.00000 0.00000 31.17403 Asia
# 10 0.912 Japan 63.32079 100.00000 31.17403 53.77280 Asia
# 11 0.554 India 63.32079 80.66598 53.77280 68.39502 Asia
# 12 0.909 South Korea 80.66598 91.65265 53.77280 68.39502 Asia
# 13 0.629 Indonesia 91.65265 100.00000 53.77280 68.39502 Asia
# 14 0.730 Brazil 63.32079 84.19372 68.39502 84.34892 South America
# 15 0.811 Argentina 84.19372 88.33138 68.39502 84.34892 South America
# 16 0.788 Russia 63.32079 81.27630 84.34892 100.00000 Eurasia
# 17 0.722 Turkey 81.27630 88.33138 84.34892 100.00000 Eurasia
# 18 0.938 Australia 88.33138 100.00000 68.39502 86.75942 Oceania
# 19 0.782 Saudi Arabia 88.33138 100.00000 86.75942 95.42241 Middle East
# 20 0.629 South Africa 88.33138 100.00000 95.42241 100.00000 AfricaThe "xmin", "xmax", "ymin" and "ymax" columns give the boundaries of each rectangle.
For publication-quality graphics, it's recommended that you stop at this point and use the coordinates to draw your own plot with custom parameters. For quick-and-dirty exploratory graphics, however, treemapify provides an additional helper function.
The ggplotify function
As the name suggests, this function takes output from treemapify and produces a ggplot object containing a graphical layout of the treemap.
treeMapPlot <- ggplotify(treeMapCoordinates)
print(treeMapPlot)The function will try to scale labels to fit rectangles. It's not perfect.
Because ggplot is perfect, it will automatically figure out whether to use a discrete or continuous fill scale:
treeMapCoordinates <- treemapify(
G20,
area = "Nom.GDP.mil.USD",
fill = "Economic.classification",
label = "Country",
group = "Region"
)
treeMapPlot <- ggplotify(treeMapCoordinates)
print(treeMapPlot)And because the output is a ggplot object, you can add custom scales and other tweaks.
treeMapPlot <- treeMapPlot + scale_fill_manual(values = c("blue", "green"))
treeMapPlot <- treeMapPlot + theme(legend.text = element_text(family = "serif"))
treeMapPlot <- treeMapPlot + ggtitle("The G-20 in stunning Technicolor")
print(treeMapPlot)For full control over the graphic output, you really should build the ggplot object yourself from the output of treemapify.
Credit
treemapify uses the Squarified Treemap algorithm of Mark Bruls, Kees Huizing and Jarke van Wijk.
The G20 dataset is from the Wikipedia article G-20 major economies, which is released under the Creative Commons Attribution-Share-Alike License 3.0.