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...