treemapify() returns a data frame of tile coordinates for a treemap layout
of a set of observations. This is only useful if you wish to draw the
treemap without the help of the ggplot2 geoms, or for some edge cases such
as creating interactive treemaps with 'R Shiny' (see e.g.
https://stackoverflow.com/q/45021775). The easiest way to draw a treemap
with the 'treemapify' package is to use the provided 'ggplot2' geoms, such
as geom_treemap().
data must be a tidy data frame, i.e. each row must represent a single
observation and each column a single variable. You must provide the name of
the variable that will be represented by the area of each treemap tile with
area. Optionally, you can also select up to three variables (with
subgroup, subgroup2 and subgroup3) to generate a layout in which the
tiles are clustered into subgroups nested up to three levels deep.
Four layout algorithms are provided. With the default 'squarified' algorithm
(layout = "squarified"), the priority is ensuring the tiles have an
aesthetically pleasing aspect ratio; that is, they are not too narrow or too
short. In this algorithm, tile placement proceeds from one corner, placing
the tiles in either rows or columns until all the tiles are placed. See
Bruls et al. (1999) for the full algorithm.
There are two variants on the 'squarified' algorithm. 'scol' forces tile placement to begin with a column, regardless of the effect on aspect ratio; 'srow' forces tile placement to been with a row. This will also apply to all subgroups. After the first row or column, the remaining tiles will be placed so as to optimise aspect ratios, as with the default algorithm.
With the 'fixed' layout algorithm (layout = "fixed"), the plot area is
divided into vertical columns, which are each filled with an equal number
of tiles beginning at the starting corner. Unlike the 'squarified'
algorithm, with the 'fixed' algorithm the relative positions of the tiles
are fixed by their order in the input data frame. This can result in
aesthetically unpleasing layouts, but it allows side-by-side comparisons or
animations to be created.
treemapify_fixed is an alias for treemapify(layout = "fixed").
Arguments
- data
A tidy data frame.
- area
Name of the variable (a column in
data) to be mapped to the area of treemap tiles.- subgroup, subgroup2, subgroup3
Optionally, names of variables (columns in
data) by which the tiles should be grouped, at up to three nested levels.- layout
The layout algorithm, one of either 'squarified' (the default), 'scol', 'srow' or 'fixed'. See Details for full details on the different layout algorithms.
- start
The corner in which to start placing the tiles. One of 'bottomleft' (the default), 'topleft', 'topright' or 'bottomright'.
- label, fill
Deprecated. Will be removed in later versions.
- group
Deprecated. Use
subgroupinstead. Will be removed in later versions.- fixed
Deprecated. Use
layout = "fixed"instead. Will be removed in later versions.- xlim, ylim
The boundaries of the treemap in the x and y dimensions. Must be a numeric vector of length two; both default to
c(0, 1).- ...
Additional arguments to be passed to
treemapify().
Details
treemapify is for generating a data frame of raw treemap coordinates. If
you want to draw a treemap with 'ggplot2', use geom_treemap() instead.
References
Bruls, M., Huizing, K., & van Wijk, J. (1999). Squarified Treemaps (pp. 33-42).Proceedings of the Joint Eurographics and IEEE TCVG Symposium on Visualization. https://vanwijk.win.tue.nl/stm.pdf
Examples
treemapify(G20, area = "gdp_mil_usd")
#> region country hdi econ_classification hemisphere ymax
#> 14 Europe European Union 0.876 Advanced Northern 0.5113668
#> 2 North America United States 0.937 Advanced Northern 1.0000000
#> 7 Asia China 0.699 Developing Northern 0.3561040
#> 8 Asia Japan 0.912 Advanced Northern 0.3561040
#> 15 Europe Germany 0.920 Advanced Northern 0.5681398
#> 16 Europe France 0.893 Advanced Northern 0.5681398
#> 17 Europe United Kingdom 0.875 Advanced Northern 0.5681398
#> 5 South America Brazil 0.730 Developing Southern 0.7290108
#> 12 Eurasia Russia 0.788 Developing Northern 0.8647700
#> 18 Europe Italy 0.881 Advanced Northern 1.0000000
#> 10 Asia India 0.554 Developing Northern 0.7141474
#> 3 North America Canada 0.911 Advanced Northern 0.7141474
#> 20 Oceania Australia 0.938 Advanced Southern 0.8762440
#> 4 North America Mexico 0.775 Developing Northern 1.0000000
#> 9 Asia South Korea 0.909 Advanced Northern 0.8458414
#> 11 Asia Indonesia 0.629 Developing Southern 0.8458414
#> 13 Eurasia Turkey 0.722 Developing Northern 0.9263225
#> 19 Middle East Saudi Arabia 0.782 Developing Northern 1.0000000
#> 6 South America Argentina 0.811 Developing Southern 0.9310513
#> 1 Africa South Africa 0.629 Developing Southern 1.0000000
#> ymin xmin xmax gdp_mil_usd
#> 14 0.0000000 0.0000000 0.4461327 16414483
#> 2 0.5113668 0.0000000 0.4461327 15684750
#> 7 0.0000000 0.4461327 0.7672295 8227037
#> 8 0.0000000 0.7672295 1.0000000 5963969
#> 15 0.3561040 0.4461327 0.6690342 3400579
#> 16 0.3561040 0.6690342 0.8400295 2608699
#> 17 0.3561040 0.8400295 1.0000000 2440505
#> 5 0.5681398 0.4461327 0.6531338 2395968
#> 12 0.7290108 0.4461327 0.6531338 2021960
#> 18 0.8647700 0.4461327 0.6531338 2014079
#> 10 0.5681398 0.6531338 0.8268406 1824832
#> 3 0.5681398 0.8268406 1.0000000 1819081
#> 20 0.7141474 0.6531338 0.7853311 1541797
#> 4 0.8762440 0.6531338 0.7853311 1177116
#> 9 0.7141474 0.7853311 0.9073179 1155872
#> 11 0.7141474 0.9073179 1.0000000 878198
#> 13 0.8458414 0.7853311 0.9225305 794468
#> 19 0.9263225 0.7853311 0.9225305 727307
#> 6 0.8458414 0.9225305 1.0000000 474954
#> 1 0.9310513 0.9225305 1.0000000 384315