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Introducesgeom_pointdensity(): A cross between a scatter plot and a 2D density plot.

To install the package, type this command in R:

install.packages("ggpointdensity")# Alternatively, you can install the latest# development version from GitHub:if (!requireNamespace("devtools",quietly=TRUE))    install.packages("devtools")devtools::install_github("LKremer/ggpointdensity")

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 togive you an estimate of how many points are overlapping.geom_density2d() andgeom_bin2d() solve this issue, but they make it impossibleto investigate individual outlier points, which may be of interest.

geom_pointdensity() aims to solve this problem by combining the best of bothworlds: 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 withgeom_pointdensity():

library(ggplot2)library(dplyr)library(viridis)library(ggpointdensity)dat<- bind_rows(  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 (smoothingbandwidth) can be adjusted with theadjust argument, whereadjust = 0.5means 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 standardggplot2 featuressuch as facets...

# Facetting by groupggplot(data=dat,mapping= aes(x=x,y=y))+  geom_pointdensity()+  scale_color_viridis()+  facet_wrap(~group)

... or point shape and size:

dat_subset<- sample_frac(dat,.1)# smaller data setggplot(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 thatxlim() andylim() change the density since they remove data points.It may be better to usecoord_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))

You can re-use or modify the density estimates using ggplot2'safter_stat() function.

For instance, let's say you want to plot the density estimates on arelative instead of an absolute scale, i.e. scaled from 0 to 1.Of course this can be achieved by dividing the absolute density values by the maximum, but how do you access the density estimates on R code?The short answer is to useafter_stat(density) inside an aesthetics mapping like so:

ggplot(data=dat,       aes(x=x,y=y,color= after_stat(density/ max(density))))+  geom_pointdensity(size=.3)+  scale_color_viridis()+  labs(color="relative\ndensity")

For a more in-depth explanation onafter_stat(), check outthe relevant ggplot documentation.

Since plotting the relative density is a common use-case, we provide a little shortcut.Instead of the solution above you can simply useafter_stat(ndensity).This is especially useful when facetting data, since sometimes you want to inspect the point density separately for each facet:

ggplot(data=dat,       aes(x=x,y=y,color= after_stat(ndensity)))+  geom_pointdensity(size=.25)+  scale_color_viridis()+  facet_wrap(~group)+  labs(color="relative\ndensity")

Even though thefoo data group is not as dense asbar overall, this plot uses the whole color scale between 0 and 1 in both facets.

Lastly, you can useafter_stat() to affect other plot aesthetics such as point size:

ggplot(data=dat,       aes(x=x,y=y,size= after_stat(1/density^1.8)))+  geom_pointdensity(adjust=.2)+  scale_color_viridis()+  scale_size_continuous(range= c(.001,3))

Here the point size is proportional to1 / density ^ 1.8.

Lukas PM Kremer (@LPMKremer) and Simon Anders (@s_anders_m), 2019