In this recipe, we will see how we can produce a three-dimensional visualization of a bivariate density plot. The two variables will represent two axes, and the estimated density will represent the third axis, which allows us to produce a three-dimensional visualization in turn.
For this recipe, we will use the airquality
data from the datasets
library. Particularly, we will use the Ozone
and Temp
variables:
attach(airquality)
We are required to call the MASS
library in order to use the kde2d()
bivariate kernel density estimation function:
library(MASS)
We are aiming to estimate the bivariate kernel density estimate and then visualize it. The estimation command is as follows:
# To fill the NA values in the variables and create new variable # Create new ozone variable Ozone_new <- Ozone #Fill the NA with median Ozone_new[is.na(Ozone_new)]<-median(Ozone,na.rm=T) #Create new Temp variable Temp_new <- Temp #Fill the NA with median Temp_new[is.na(Temp_new)]<- median(Temp,na.rm=T) #Bivariate kernel density estimation with automatic bandwidth density3d <- kde2d(Ozone_new,Temp_new) #Visualize the density in 3D space persp(density3d)
The steps involved in producing 3D density plot are as follows:
kde2d()
function. Within this function, the required arguments are the two x
and y
variables for which we want to estimate the density. Then, the bandwidth h
can be specified or it can be estimated automatically. We have used the default settings to estimate the bandwidth value.persp()
function to visualize the density in a 3D space.18.217.110.145