‘treemapify’ provides ‘ggplot2’ geoms for drawing treemaps.
Install the release version of ‘treemapify’ from CRAN:
install.package("treemapify")
If you want the development version, install it from GitHub:
devtools::install_github("wilkox/treemapify")
‘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 AdvancedIn 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.
ggplot(G20, aes(area = gdp_mil_usd, fill = hdi, label = country)) +
geom_treemap() +
geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
grow = TRUE)
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"
)
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)