如何在R中叠加密度图？

Question

I would like to overlay 2 density plots on the same device with R. How can I do that? I searched the web but I didn't find any obvious solution.

My idea would be to read data from a text file (columns) and then use

plot(density(MyData$Column1))
plot(density(MyData$Column2), add=T)

Or something in this spirit.

Answer 1

use lines for the second one:

plot(density(MyData$Column1))
lines(density(MyData$Column2))

make sure the limits of the first plot are suitable, though.

Answer 2

ggplot2 is another graphics package that handles things like the range issue Gavin mentions in a pretty slick way. It also handles auto generating appropriate legends and just generally has a more polished feel in my opinion out of the box with less manual manipulation.

library(ggplot2)

#Sample data
dat <- data.frame(dens = c(rnorm(100), rnorm(100, 10, 5))
                   , lines = rep(c("a", "b"), each = 100))
#Plot.
ggplot(dat, aes(x = dens, fill = lines)) + geom_density(alpha = 0.5)

Answer 3

Adding base graphics version that takes care of y-axis limits, add colors and works for any number of columns:

If we have a data set:

myData <- data.frame(std.nromal=rnorm(1000, m=0, sd=1),
                     wide.normal=rnorm(1000, m=0, sd=2),
                     exponent=rexp(1000, rate=1),
                     uniform=runif(1000, min=-3, max=3)
                     )

Then to plot the densities:

dens <- apply(myData, 2, density)

plot(NA, xlim=range(sapply(dens, "[", "x")), ylim=range(sapply(dens, "[", "y")))
mapply(lines, dens, col=1:length(dens))

legend("topright", legend=names(dens), fill=1:length(dens))

Which gives:

Answer 4

Just to provide a complete set, here's a version of Chase's answer using lattice :

dat <- data.frame(dens = c(rnorm(100), rnorm(100, 10, 5))
                   , lines = rep(c("a", "b"), each = 100))

densityplot(~dens,data=dat,groups = lines,
            plot.points = FALSE, ref = TRUE, 
            auto.key = list(space = "right"))

which produces a plot like this:

Answer 5

That's how I do it in base (it's actually mentionned in the first answer comments but I'll show the full code here, including legend as I can not comment yet...)

First you need to get the info on the max values for the y axis from the density plots. So you need to actually compute the densities separately first

dta_A <- density(VarA, na.rm = TRUE)
dta_B <- density(VarB, na.rm = TRUE)

Then plot them according to the first answer and define min and max values for the y axis that you just got. (I set the min value to 0)

plot(dta_A, col = "blue", main = "2 densities on one plot"), 
     ylim = c(0, max(dta_A$y,dta_B$y)))  
lines(dta_B, col = "red")

Then add a legend to the top right corner

legend("topright", c("VarA","VarB"), lty = c(1,1), col = c("blue","red"))

Answer 6

I took the above lattice example and made a nifty function. There is probably a better way to do this with reshape via melt/cast. (Comment or edit if you see an improvement.)

multi.density.plot=function(data,main=paste(names(data),collapse = ' vs '),...){
  ##combines multiple density plots together when given a list
  df=data.frame();
  for(n in names(data)){
    idf=data.frame(x=data[[n]],label=rep(n,length(data[[n]])))
    df=rbind(df,idf)
  }
  densityplot(~x,data=df,groups = label,plot.points = F, ref = T, auto.key = list(space = "right"),main=main,...)
}

Example usage:

multi.density.plot(list(BN1=bn1$V1,BN2=bn2$V1),main='BN1 vs BN2')

multi.density.plot(list(BN1=bn1$V1,BN2=bn2$V1))

Answer 7

Whenever there are issues of mismatched axis limits, the right tool in base graphics is to use matplot . The key is to leverage the from and to arguments to density.default . It's a bit hackish, but fairly straightforward to roll yourself:

set.seed(102349)
x1 = rnorm(1000, mean = 5, sd = 3)
x2 = rnorm(5000, mean = 2, sd = 8)

xrng = range(x1, x2)

#force the x values at which density is
#  evaluated to be the same between 'density'
#  calls by specifying 'from' and 'to'
#  (and possibly 'n', if you'd like)
kde1 = density(x1, from = xrng[1L], to = xrng[2L])
kde2 = density(x2, from = xrng[1L], to = xrng[2L])

matplot(kde1$x, cbind(kde1$y, kde2$y))

Add bells and whistles as desired ( matplot accepts all the standard plot / par arguments, eg lty , type , col , lwd , ...).

Answer 8

You can use the ggjoy package. Let's say that we have three different beta distributions such as:

set.seed(5)
b1<-data.frame(Variant= "Variant 1", Values = rbeta(1000, 101, 1001))
b2<-data.frame(Variant= "Variant 2", Values = rbeta(1000, 111, 1011))
b3<-data.frame(Variant= "Variant 3", Values = rbeta(1000, 11, 101))


df<-rbind(b1,b2,b3)

You can get the three different distributions as follows:

library(tidyverse)
library(ggjoy)


ggplot(df, aes(x=Values, y=Variant))+
    geom_joy(scale = 2, alpha=0.5) +
    scale_y_discrete(expand=c(0.01, 0)) +
    scale_x_continuous(expand=c(0.01, 0)) +
    theme_joy()