Plot kernel density estimation with the kernels over the individual observations in R

Question

Well to keep things short what I want to achieve is a plot like the right one:

I would like to obtain a standard KDE plot with its individual kernels plotted over the observations.

The best solution would be the one that considers all the different kernel functions (eg rectangular, triangular etc).

Answer 1

Well after reading this Answer I managed to come up with an solution.

# Create some input data
x<-c(19, 20, 10, 17, 16, 13, 16, 10,  7, 18)


# Calculate the KDE
kde<-density(x,kernel="gaussian",bw=bw.SJ(x)*0.2)

# Calcualte the singel kernels/pdf's making up the KDE of all observations
A.kernel<-sapply(x, function(i) {density(i,kernel="gaussian",bw=kde$bw)},simplify=F)
sapply(1:length(A.kernel), function(i){A.kernel[[i]][['y']]<<-(A.kernel[[i]][['y']])/length(x)},simplify=F)


# Plot everything together ensuring the right scale (the area of the single kernels is corrected) 
plot(kde)
rug(x,col=2,lwd=2.5)
sapply(A.kernel, function(i){
        lines(i,col="red")}
        )

The result looks like this: