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.
What’s New in v1.0.0?
New Features
bi_class()
now accepts factors for one or both of thex
andy
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 tobi_class()
.bi_pal()
,bi_legend()
,bi_scale_fill()
, andbi_scale_color()
functions all support four-by-four bivariate maps whendim = 4
. Note that the original five palettes do not support four-by-four mapping, but very close approximations (e.g.DkBlue2
instead ofDkBlue
) are now provided in their place. The legacy palettes are all still included in the package.- The number of built-in palettes has been greatly expanded!
- Palettes can now be flipped and rotated (or both!), so that each built-in palette can be displayed in four different configurations. This includes the built-in palettes and any custom palettes that are four-by-four or smaller. If you want to flip or rotate larger palettes, you should make those decisions while creating the palette itself.
- The workflow for allowing custom palettes has been overhauled to
simply the process - users can provide a named vector for the
pal
arguments in thebi_pal()
,bi_legend()
,bi_scale_fill()
, andbi_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 thatbiscale
does not provide palettes for larger maps. Instead, you should provide a custom palette.bi_class_breaks()
can be used withbi_legend()
to facilitate optionally adding break values to your legends. Likebi_class()
, this new function accepts both continuous and pre-made factors.
Breaking Changes
R
version 3.4 is no longer supported - please use at leastR
version 3.5- There is no default supplied for
bi_class()
’sstyle
argument sincebi_class()
now accepts factors as well. Users that relied on the default behavior ofbi_class()
will now receive an error asking you to specify astyle
for calculating breaks.
Deprecated Functions
bi_pal_manual()
now returns a warning that it has been deprecated and will be removed in a later release ofbiscale
(planned for the end of 2022). Please update your workflows to use the new approach to generating custom palettes.
Internal Improvements
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 inbiscale
.
Documentation Improvements
- Documentation updates have been made, including the addition of a number of new examples and vignettes. These include detailed articles on bivariate palettes, working with breaks and legends, and creating bivariate maps with raster data.
What’s New in the Development Version (v1.1.0.9000)?
These require the development version to be installed using
remotes::install_github()
, described in the next section.
New Features
- The
bi_legend()
function now has abase_family
argument, which can be paired with the suggestedshowtext
package to display non-Latin characters. See the “Options for Breaks and Legends” vignette for details on using this new feature.
Installation
Installing biscale
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")
Installing Suggested Dependencies
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)
Usage
Quick Overview
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:
Rotate the legend for better readability of labels.
library(grid)
legend <- bi_legend(pal = "GrPink",
dim = 3,
xlab = "Higher % White ",
ylab = "Higher Income ",
size = 20)
print(map)
print(legend, vp = grid::viewport(x = 0.4, y = .75, width = 0.2, height = 0.2, angle = -45))
The completed map, created with the sample code in this README, looks like this:
Contributor Code of Conduct
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.