definition - mistake - related - code


A Treemap displays hierarchical data as a set of nested rectangles. Each group is represented by a rectangle, which area is proportional to its value. Using color schemes and or interactivity, it is possible to represent several dimensions: groups, subgroups etc.

Here is an example describing the world population of 250 countries. The world is divided in continent (group), continent are divided in regions (subgroup), and regions are divided in countries. In this tree structure, countries are considered as leaves: they are at the end of the branches.

# libraries

# Load dataset from github
data <- read.table("", header=T, sep=";")
data[ which(data$value==-1),"value"] <- 1
colnames(data) <- c("Continent", "Region", "Country", "Pop")

# Plot
p <- treemap(data,
            # data
            index=c("Continent", "Region", "Country"),
            # Main

            # Borders:
            border.col=c("black", "grey", "grey"),             
            # Labels
            fontsize.labels=c(0.7, 0.4, 0.3),
            fontcolor.labels=c("white", "white", "black"),
            align.labels=list( c("center", "center"), c("left", "top"), c("right", "bottom")),                                  
            overlap.labels=0.5#, inflate.labels=T   

Note: You can read more about this story here, with many more way to visualize this dataset. Data source: wikipedia and formating by 1 and 2.

What for

Treemaps are used to show two types of information simultaneously:

Treemaps have the advantage to make efficient use of space, what makes them useful to represent a big amount of data.


The main variation of treemaps concerns the use of interactivity. It is advised to use it if you have more than 2 or 3 levels of organization to display. Indeed, treemap get cluttered in this situation otherwise.

In the figure below, clicking on a group zooms on it a reveals the underlying structure. Hint: click on the title to come back to the previous level of the hierarchy.


d3tree2( p ,  rootname = "General" )

Common mistakes


Build your own

The R and Python graph galleries are 2 websites providing hundreds of chart example, always providing the reproducible code. Click the button below to see how to build the chart you need with your favorite programing language.

R graph gallery Python gallery


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A work by Yan Holtz for