skip to content

Treemap Chart

2 min read

# scientific plotting
# r
# python
# matlab
Not in series

About the Treemap Chart

In information visualization and computing, treemapping is a method for displaying hierarchical data using nested figures, usually rectangles.

Treemaps display hierarchical (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. A leaf node’s rectangle has an area proportional to a specified dimension of the data.[1] Often the leaf nodes are colored to show a separate dimension of the data.

When the color and size dimensions are correlated in some way with the tree structure, one can often easily see patterns that would be difficult to spot in other ways, such as whether a certain color is particularly prevalent. A second advantage of treemaps is that, by construction, they make efficient use of space. As a result, they can legibly display thousands of items on the screen simultaneously.

Example data is from R package treemapify, you can obtain it by

library(treemapify)
write.csv(G20, "data.csv", row.names=FALSE)

Draw Treemap Chart with R

Basic Treemap

library(ggplot2)
library(treemapify)
 
data = read.csv("data.csv")
 
ggplot(data, aes(area = gdp_mil_usd, fill = hdi)) +
  geom_treemap()
image-20250906190543001

Treemap with Legend

ggplot(data, aes(area = gdp_mil_usd, fill = hdi, label = country)) +
  geom_treemap() +
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre", family = "Arial",
                    grow = TRUE)
image-20250906190649840

Subgrouping Tiles

ggplot(data, 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 =
                             "gray", fontface = "italic", min.size = 0) +
  geom_treemap_text(colour = "white", place = "topleft", reflow = T)
image-20250906191135687

Color by Region

ggplot(data, aes(area = gdp_mil_usd, fill = region, label = country)) +
  geom_treemap() +
  geom_treemap_text(grow = T, reflow = T, colour = "white", fontface = "italic") +
  theme(legend.position = "bottom")
image-20250906191537795

Two Panels

ggplot(data, aes(area = gdp_mil_usd, fill = hdi, label = country)) +
  geom_treemap() +
  geom_treemap_text(grow = T, reflow = T, colour = "white", fontface = "italic") +
  facet_wrap( ~ econ_classification)
image-20250906191818975

Draw Treemap Chart with Python

import pandas as pd
import matplotlib.pyplot as plt
import squarify
import matplotlib.colors as mcolors
 
df = pd.read_csv('data.csv')
 
# Color mapping: HDI → 0.5~1.0 maps to `Blues`
norm = mcolors.Normalize(vmin=0.5, vmax=1.0)
cmap = plt.cm.Blues
colors = cmap(norm(df['hdi']))
 
plt.figure(figsize=(12, 8))
squarify.plot(sizes=df['gdp_mil_usd'],
              label=df['country'],
              color=colors,
              alpha=0.85,
              text_kwargs={'fontsize': 9, 'weight': 'bold'})
 
plt.title('World GDP Treemap (colored by HDI)', fontsize=18, pad=20)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
plt.colorbar(sm, shrink=0.7, label='Human Development Index (HDI)')
plt.axis('off')
plt.tight_layout()
plt.show()
image-20250906192440078

Draw Treemap Chart with MATLAB

Download Treemap

data = readtable('data.csv');
 
rectangles = treemap(data.gdp_mil_usd);
labels = data.country;
 
fig=figure;
 
plotRectangles(rectangles,labels)
image-20250906193755840