Treemaps
2023-02-28
Treemaps
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. 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 if a certain color is particularly relevant. 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.
Installation
Install the release version oftreemapify from CRAN:
install.packages("treemapify")
devtools::install_github("wilkox/treemapify")
The G20 dataset
treemapify includes an example dataset containing
statistics about the G-20 group of major world economies.
library(ggplot2)
library(treemapify)
G20## region country gdp_mil_usd hdi econ_classification
## 1 Africa South Africa 384315 0.629 Developing
## 2 North America United States 15684750 0.937 Advanced
## 3 North America Canada 1819081 0.911 Advanced
## 4 North America Mexico 1177116 0.775 Developing
## 5 South America Brazil 2395968 0.730 Developing
## 6 South America Argentina 474954 0.811 Developing
## 7 Asia China 8227037 0.699 Developing
## 8 Asia Japan 5963969 0.912 Advanced
## 9 Asia South Korea 1155872 0.909 Advanced
## 10 Asia India 1824832 0.554 Developing
## 11 Asia Indonesia 878198 0.629 Developing
## 12 Eurasia Russia 2021960 0.788 Developing
## 13 Eurasia Turkey 794468 0.722 Developing
## 14 Europe European Union 16414483 0.876 Advanced
## 15 Europe Germany 3400579 0.920 Advanced
## 16 Europe France 2608699 0.893 Advanced
## 17 Europe United Kingdom 2440505 0.875 Advanced
## 18 Europe Italy 2014079 0.881 Advanced
## 19 Middle East Saudi Arabia 727307 0.782 Developing
## 20 Oceania Australia 1541797 0.938 Advanced
## hemisphere
## 1 Southern
## 2 Northern
## 3 Northern
## 4 Northern
## 5 Southern
## 6 Southern
## 7 Northern
## 8 Northern
## 9 Northern
## 10 Northern
## 11 Southern
## 12 Northern
## 13 Northern
## 14 Northern
## 15 Northern
## 16 Northern
## 17 Northern
## 18 Northern
## 19 Northern
## 20 SouthernDrawing a simple treemap
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.
ggplot(G20, aes(area = gdp_mil_usd, fill = hdi)) +
geom_treemap()
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.
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
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 with geom_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.
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)
Note that ‘Argentina’ has been hidden. 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.
Like any ‘ggplot2’ plot, ‘treemapify’ plots can be faceted, scaled, themed, etc.
ggplot(G20, aes(area = gdp_mil_usd, fill = region, label = country)) +
geom_treemap() +
geom_treemap_text(grow = T, reflow = T, colour = "black") +
facet_wrap( ~ econ_classification) +
scale_fill_brewer(palette = "Set1") +
theme(legend.position = "bottom") +
labs(
title = "The G-20 major economies",
caption = "The area of each country is proportional to its relative GDP
within the economic group (advanced or developing)",
fill = "Region"
)
Animated treemaps
The default algorithm for laying out the tiles is the
squarified algorithm. This tries to minimise the tiles’
aspect ratios, making sure there are no long and flat or tall and skinny
tiles. While ‘squarified’ treemaps are aesthetically pleasing, the
downside is that the position of tiles within the plot area can change
dramatically with even small changes to the dataset. This makes it
difficult to compare treemaps side-by-side, or create animated
treemaps.
By providing the fixed = TRUE option to
treemapify geoms, an alternative layout algorithm is used
that will always position the tiles based on the order of observations
in the data frame. It’s very important that the same value for fixed is
passed to all ‘treemapify’ geoms, otherwise different layers of the plot
might not share the same layout.
With the help of fixed = TRUE, and with the tweenr and
gganimate packages, it becomes possible to create animated treemaps
showing e.g. change over time.
library(tweenr)
library(gganimate)
G20_alt <- G20
G20_alt$gdp_mil_usd <- sample(G20$gdp_mil_usd, nrow(G20))
G20_alt$hdi <- sample(G20$hdi, nrow(G20))
tweened <- tween_states(list(G20, G20_alt, G20), tweenlength = 8,
statelength = 5, ease = 'cubic-in-out', nframes = 31)
animated_plot <- ggplot(tweened, aes(area = gdp_mil_usd, fill = hdi,
label = country, subgroup = region,
frame = .frame)) +
geom_treemap(fixed = T) +
geom_treemap_subgroup_border(fixed = T) +
geom_treemap_subgroup_text(place = "centre", grow = T, alpha = 0.5,
colour = "black", fontface = "italic", min.size = 0,
fixed = T) +
geom_treemap_text(colour = "white", place = "topleft", reflow = T, fixed = T)animation::ani.options(interval = 1/10)
gganimate(animated_plot, "man/figures/animated_treemap.gif", title_frame = F,
ani.width = 200, ani.height = 200)
animated_treemap