Introduces geom_pointdensity()
: A cross between a
scatter plot and a 2D density plot.
To install the package from R, use:
install.packages("ggpointdensity")
Alternatively, you can install the development version from GitHub:
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
::install_github("LKremer/ggpointdensity") devtools
There are several ways to visualize data points on a 2D coordinate
system: If you have lots of data points on top of each other,
geom_point()
fails to give you an estimate of how many
points are overlapping. geom_density2d()
and
geom_bin2d()
solve this issue, but they make it impossible
to investigate individual outlier points, which may be of interest.
geom_pointdensity()
aims to solve this problem by
combining the best of both worlds: individual points are colored by the
number of neighboring points. This allows you to see the overall
distribution, as well as individual points.
Generate some toy data and visualize it with
geom_pointdensity()
:
library(ggplot2)
library(dplyr)
library(viridis)
library(ggpointdensity)
<- bind_rows(
dat tibble(x = rnorm(7000, sd = 1),
y = rnorm(7000, sd = 10),
group = "foo"),
tibble(x = rnorm(3000, mean = 1, sd = .5),
y = rnorm(3000, mean = 7, sd = 5),
group = "bar"))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis()
Each point is colored according to the number of neighboring points.
The distance threshold to consider two points as neighbors (smoothing
bandwidth) can be adjusted with the adjust
argument, where
adjust = 0.5
means use half of the default bandwidth.
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = .1) +
scale_color_viridis()
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = 4) +
scale_color_viridis()
Of course you can combine the geom with standard ggplot2
features such as facets…
# Facetting by group
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
facet_wrap( ~ group)
… or point shape and size:
<- sample_frac(dat, .1) # smaller data set
dat_subset ggplot(data = dat_subset, mapping = aes(x = x, y = y)) +
geom_pointdensity(size = 3, shape = 17) +
scale_color_viridis()
Zooming into the axis works as well, keep in mind that
xlim()
and ylim()
change the density since
they remove data points. It may be better to use
coord_cartesian()
instead.
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
xlim(c(-1, 3)) + ylim(c(-5, 15))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
coord_cartesian(xlim = c(-1, 3), ylim = c(-5, 15))
Lukas Kremer and Simon Anders (2019)