除非您自己明确计算区域,否则没有简单的方法可以在不同的重叠区域进行着色。这里有一个功能,可以帮助计算,其中密度交换位置
densitysplit <- function(val, grp, N=200, x=seq(min(val), max(val), length.out=N)) {
grp <- factor(grp)
den <- Map(function(z) {
dx<-density(val[grp==z])
approxfun(dx$x, dx$y)(x)
}, levels(grp))
maxcat <- apply(do.call("cbind",den), 1, which.max)
data.frame(x=x, ymin=do.call("pmin", den), ymax=do.call("pmax", den),
top = levels(grp)[maxcat],
group = cumsum(c(1,diff(maxcat)!=0))
)
}
为您的数据区,你会做这样的事情
head(densitysplit(df$value, df$gender))
# x ymin ymax top group
# 1 8.00000 4.214081e-05 5.198326e-05 male 1
# 2 58.17085 4.485596e-05 5.433638e-05 male 1
# 3 108.34171 4.760983e-05 5.665547e-05 male 1
# 4 158.51256 5.039037e-05 5.893143e-05 male 1
# 5 208.68342 5.318724e-05 6.115595e-05 male 1
# 6 258.85427 5.598707e-05 6.332672e-05 male 1
这给了你,你需要使用geom_ribbon
绘制数据的数据。你可以做
ggplot(df, aes(value)) +
geom_ribbon(data=densitysplit(df$value, df$gender), aes(x, ymin=ymin, ymax=ymax, fill=top, group=group)) +
geom_density() +
geom_density(data=subset(df, gender=='male'), aes(value), colour="blue") +
geom_density(data=subset(df, gender=='female'), aes(value), colour="red")
![enter image description here](https://i.stack.imgur.com/qSsuv.png)