Thematic choropleth maps are used to display quantities of some variable within areas, such as mapping median income across a city’s neighborhoods. However, we often think in bivariate terms - “how do race and income vary together?”. Maps that captures this, known as bivariate choropleth maps, are often perceived as difficult to create and interpret. The goal of
biscale is to implement a consistent approach to bivariate mapping entirely within
R. The package’s workflow is based on a 2019 tutorial written by Timo Grossenbacher and Angelo Zehr.
biscale also contains a set of bivariate mapping palettes, including Joshua Stevens’ classive color schemes.
The easiest way to get
biscale is to install it from CRAN:
Alternatively, the development version of
biscale can be accessed from GitHub with
# install.packages("remotes") ::install_github("chris-prener/biscale")remotes
Since the package does not directly use functions from
sf, it is a suggested dependency rather than a required one. However, the most direct approach to using
biscale is with
sf objects, and we therefore recommend users install
sf. Windows and macOS users should be able to install
sf without significant issues unless they are building from source. Linux users will need to install several open source spatial libraries to get
sf itself up and running.
The other suggested dependency that users may want to consider installing is
cowplot. All of the examples in the package documentation utilize it to construct final map images that combine the map with the legend. Like
sf, it is suggested because none of the functions in
If you want to use them, you can either install these packages individually (faster) or install all of the suggested dependencies at once (slower, will also give you a number of other packages you may or may not want):
## install just cowplot and sf install.packages(c("cowplot", "sf")) ## install all suggested dependencies install.packages("biscale", dependencies = TRUE)
All functions within
biscale use the prefix
bi_ to leverage the auto-completion features of RStudio and other IDEs.
biscale contains a data set of U.S. Census tracts for the City of St. Louis in Missouri. Both median income and the percentage of white residents are included, both of which can be used to demonstrate the package’s functionality.
Once data are loaded, bivariate classes can be applied with the
# load dependencies library(biscale) # create classes <- bi_class(stl_race_income, x = pctWhite, y = medInc, style = "quantile", dim = 3)data
dim argument is used to control the extent of the legend - do you want to produce a two-by-two map (
dim = 2), a three-by-three map (
dim = 3), or a four-by-four map (
dim = 4)? Note that support for four-by-four mapping is new as of v1.0.0!
Classes can be applied with the
style parameter using four approaches for calculating breaks:
"jenks". The default
"quantile" approach will create relatively equal “buckets” of data for mapping, with a break created at the median (50th percentile) for a two-by-two map or at the 33rd and 66th percentiles for a three-by-three map. For a four-by-four map, breaks are created at the 25th, 50th (median), and 75th percentiles.
With the sample data, this creates a very broad range for the percent white measure in particular. Using one of the other approaches to calculating breaks yields a narrower range for the breaks and produces a map that does not overstate the percent of white residents living on the north side of St. Louis:
Users should therefore choose methods for calculating breaks carefully as well as the number of dimensions mapped.
biscale now supports larger dimension maps as well as custom breaks for maps. For an overview of these options, see the “Options for Breaks and Legends” vignette.
Once breaks are created, we can use
bi_scale_fill() as part of our
# create map <- ggplot() + map geom_sf(data = data, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) + bi_scale_fill(pal = "GrPink", dim = 3) + labs( title = "Race and Income in St. Louis, MO", subtitle = "Gray Pink (GrPink) Palette" + ) bi_theme()
This requires that the variable
bi_class, created with
bi_class(), is used as the fill variable in the aesthetic mapping. We also want to remove the legend from the plot since it will not accurately communicate the complexity of the bivariate scale.
The dimensions of the scale must again be supplied for
bi_scale_fill() (they should match the dimensions given for
bi_class()!), and a palette must be given. For reference, the top image above uses the
"DkBlue" palette, the map images in the breaks section above use the
"DkViolet" palette, and the map constructed in this section (and displayed below) uses the
"GrPink" palette. Note that, as of v1.0.0, the number of options for palettes has been expanded and there is increased support for custom palettes. See the “Bivariate Palettes” vignette or
?bi_pal for more details.
If you are mapping point data, there is an alternative function
bi_scale_color() that works the same way as
The example above also includes
bi_theme(), which is based on the theme designed by Timo Grossenbacher and Angelo Zehr. This theme creates a simple, clean canvas for bivariate mapping that removes any possible distracting elements.
show.legend = FALSE so that we can add (manually) our own bivariate legend. The legend itself can be created with the
<- bi_legend(pal = "GrPink", legend dim = 3, xlab = "Higher % White ", ylab = "Higher Income ", size = 8)
The palette and dimensions should match what has been used for both
bi_class() (in terms of dimensions) and
bi_scale_fill() (in terms of both dimensions and palette). The
size argument controls the font size used on the legend. Note that
plotmath is used to draw the arrows since Unicode arrows are font dependent. This happens internally as part of
bi_legend() - you don’t need to include them in your
With our legend drawn, we can then combine the legend and the map with a package like
cowplot. The values needed for this stage will be subject to experimentation depending on the shape of the map itself.
# combine map with legend <- ggdraw() + finalPlot draw_plot(map, 0, 0, 1, 1) + draw_plot(legend, 0.2, .65, 0.2, 0.2)
This approach allows us to customize legend’s placement and size to suit different map layouts. All of the maps shown as part of this vignette were produced using this approach. There are other approaches you could take as well that do not use
cowplot. Note that, because
R v3.5, it is not included as a suggested package (to maintain backward compatibility). Beginning with v1.0.0, there are additional options for constructing legends with
biscale. Please see the “Options for Breaks and Legends” vignette for more details!
The completed map, created with the sample code in this vignette, looks like this:
Ritself, welcome! Hadley Wickham’s R for Data Science is an excellent way to get started with data manipulation in the tidyverse, which
biscaleis designed to integrate seamlessly with.
R, we strongly encourage you check out the excellent new Geocomputation in R by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow.
biscale, you are encouraged to use the RStudio Community forums. Please create a
reprexbefore posting. Feel free to tag Chris (
@chris.prener) in any posts about
reprexand then open an issue on GitHub.
If you have features or suggestions you want to see implemented, please open an issue on GitHub (and ideally created a
reprex to go with it!). Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.