knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
biscale
implements a set of functions for bivariate thematic mapping based on the tutorial written by Timo Grossenbacher and Angelo Zehr as well as a set of bivariate mapping palettes, including Joshua Stevens' classic color schemes.
In addition to support for two-by-two, three-by-three, and four-by-four maps, the package also supports a range of methods for calculating breaks for bivariate maps.
bi_class()
now accepts factors for one or both of the x
and y
variables, allowing more flexibility for how breaks are calculated. If you want finer grained control over your categories, calculate them ahead of time and then pass the factors on to bi_class()
.bi_pal()
, bi_legend()
, bi_scale_fill()
, and bi_scale_color()
functions all support four-by-four bivariate maps when dim = 4
. Note that the original five palettes do not support four-by-four mapping, but very close approximations (e.g. DkBlue2
instead of DkBlue
) are now provided in their place. The legacy palettes are all still included in the package.pal
arguments in the bi_pal()
, bi_legend()
, bi_scale_fill()
, and bi_scale_color()
functions. All of these functions will validate your input to ensure that it maps correctly.bi_class()
can be used to calculate bivariate breaks for maps larger than four-by-four, though it will return a warning reminding you that these maps are hard to read and that biscale
does not provide palettes for larger maps. Instead, you should provide a custom palette.bi_class_breaks()
can be used with bi_legend()
to facilitate optionally adding break values to your legends. Like bi_class()
, this new function accepts both continuous and pre-made factors.R
version 3.4 is no longer supported - please use at least R
version 3.5bi_class()
's style
argument since bi_class()
now accepts factors as well. Users that relied on the default behavior of bi_class()
will now receive an error asking you to specify a style
for calculating breaks.bi_pal_manual()
now returns a warning that it has been deprecated and will be removed in a later release of biscale
(planned for the end of 2022). Please update your workflows to use the new approach to generating custom palettes.sf
is now a suggested package instead of an imported package, and several dependencies have been removed in the process of re-factoring all of the code in biscale
.These require the development version to be installed using remotes::install_github()
, described in the next section.
bi_legend()
function now has a base_family
argument, which can be paired with the suggested showtext
package to display non-Latin characters. See the "Options for Breaks and Legends" vignette for details on using this new feature.The easiest way to get biscale
is to install it from CRAN:
install.packages("biscale")
Alternatively, the development version of biscale
can be accessed from GitHub with remotes
:
# install.packages("remotes") remotes::install_github("chris-prener/biscale")
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 biscale
call cowplot
directly.
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)
Creating bivariate plots in the style described by Grossenbacher and Zehr requires a number of dependencies in addition to biscale
- ggplot2
for plotting and sf
for working with spatial objects in R
. We'll also use cowplot
in these examples:
# load dependencies library(biscale) library(ggplot2) library(cowplot) library(sf)
The biscale
package comes with some sample data from St. Louis, MO that you can use to check out the bivariate mapping workflow. Our first step is to create our classes for bivariate mapping. biscale
currently supports a both two-by-two and three-by-three tables of classes, created with the bi_class()
function:
# create classes data <- bi_class(stl_race_income, x = pctWhite, y = medInc, style = "quantile", dim = 3)
The default method for calculating breaks is "quantile"
, which will provide breaks at 33.33% and 66.66% percent (i.e. tercile breaks) for three-by-three palettes. Other options are "equal"
, "fisher"
, and "jenks"
. These are specified with the optional style
argument. The dim
argument is used to adjust whether a two-by-two and three-by-three tables of classes is returned.
Once breaks are created, we can use bi_scale_fill()
as part of our ggplot()
call:
# create map map <- ggplot() + 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 = "Dark Blue (DkBlue) Palette" ) + bi_theme()
There are a variety of other options for palettes. See the "Bivarite Palettes" vignette or ?bi_pal
for more details. The bi_theme()
function applies a simple theme without distracting elements, which is preferable given the already elevated complexity of a bivariate map. We need to specify the dimensions of the palette for bi_scale_fill()
as well.
To add a legend to our map, we need to create a second ggplot
object. We can use bi_legend()
to accomplish this, which allows us to easily specify the fill palette, the x and y axis labels, and their size along with the dimensions of the palette:
legend <- bi_legend(pal = "GrPink", dim = 3, xlab = "Higher % White ", ylab = "Higher Income ", size = 8)
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 xlab
and ylab
arguments!
With our legend drawn, we can then combine the legend and the map with cowplot
. The values needed for this stage will be subject to experimentation depending on the shape of the map itself.
# combine map with legend finalPlot <- ggdraw() + draw_plot(map, 0, 0, 1, 1) + draw_plot(legend, 0.2, .65, 0.2, 0.2)
The completed map, created with the sample code in this README, looks like this:
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.
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