Package gghdr helps to visualize Highest Density Regions (HDR) in one
and two dimensions. HDRs are useful in displaying multimodality in the
distribution. This work draws inspiration from the the package
hdrcde developed by Rob
Hyndman and provide a framework for
displaying HDRs under ggplot2 framework.
You could install the stable version on CRAN:
install.packages("gghdr")You could install the development version from Github using:
# install.packages("remotes")
remotes::install_github("Sayani07/gghdr")There are several statistical methods to summarize a distribution by
region of the sample space covering certain probability. For example, in
a traditional boxplot, the central box bounded by the interquartile
range represents 50% coverage and whiskers represent 99% coverage for
large samples. The method of summarizing a distribution using highest
density regions is useful for analysing multimodal distributions. We
illustrate this by exploring the data set faithful which contains the
waiting time and duration of eruptions for the old faithful geyser in
the Yellowstone National Park, USA.
library(ggplot2)
ggplot(faithful, aes(y=eruptions)) + geom_boxplot()
We can use geom_hdr_boxplot to display the same variable. Along with
displaying the 99% and 50% highest density regions, it also shows the
local mode in each of the regions. This shows that eruption times are
likely to be around 4.5 minutes or 2 minutes, but rarely for around 3
minutes. This insight was not apparent in the above boxplot.
library(gghdr)
library(ggplot2)
ggplot(faithful, aes(y = eruptions)) +
geom_hdr_boxplot(prob = c(.5, 0.99), fill = "blue") +
theme_minimal()
It can be interesting to supplement a scatterplot with marginal
distributions of one or both variables to enhance insights into the
relationship between the two variables. This is possible through
geom_hdr_rug. This shows two clear clusters, one with shorter waiting
times and shorter eruptions (around 2 minutes) and another with longer
waiting times and longer eruptions (around 4.5 minutes). The
geom_hdr_rug adds to this information by displaying the highest
density region of eruption time covering 50% and 99%.
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting)) +
geom_hdr_rug(aes(x = eruptions), prob = c(0.99, 0.5), fill = "blue")
The previous example can be extended to allow displaying the scatterplot
with points coloured according to the bivariate highest density regions
using hdr_bin.hdr_bin can also be mapped to only the x-axis or
y-axis to show the marginal distribution of any one variable. This
figure enriches the information in the scatterplot by emphasizing the
highest bivariate density regions covering 50%, 90%, 99%, and more than
99% coverage.
ggplot(data = faithful, aes(x = waiting, y=eruptions)) +
geom_point(aes(colour = hdr_bin(x = waiting, y = eruptions))) +
scale_colour_viridis_d(direction = -1) 
You can read more about gghdr in the vignette.
