如何在 R 中叠加密度图？

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.

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)

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:

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))

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:

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, e.g. lty, type, col, lwd, ...).

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"))

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()