ggplot2 - Modify geom_density2d to accept weights as a parameter?

Question

This is my first post to the R-community, so pardon me if it is silly. I would like to use the functions geom_density2d and stat_density2d in ggplot2 to plot kernel density estimates, but the problem is that they can't handle weighted data. From what I understand, these two functions call the function kde2d from package MASS to make the kernel density estimate. And the kde2d doesn't take data weights as a parameter.

Now, I have found this altered version of kde2d http://www.inside-r.org/node/226757, which takes weights as a parameter and is based on the source code of kde2d. The code of this function:

  kde2d.weighted <- function (x, y, w, h, n = 25, lims = c(range(x), range(y))) {
  nx <- length(x)
  if (length(y) != nx) 
    stop("data vectors must be the same length")
  if (length(w) != nx & length(w) != 1)
    stop("weight vectors must be 1 or length of data")
  gx <- seq(lims[1], lims[2], length = n) # gridpoints x
  gy <- seq(lims[3], lims[4], length = n) # gridpoints y
  if (missing(h)) 
    h <- c(bandwidth.nrd(x), bandwidth.nrd(y));
  if (missing(w)) 
    w <- numeric(nx)+1;
  h <- h/4
  ax <- outer(gx, x, "-")/h[1] # distance of each point to each grid point in x-direction
  ay <- outer(gy, y, "-")/h[2] # distance of each point to each grid point in y-direction
  z <- (matrix(rep(w,n), nrow=n, ncol=nx, byrow=TRUE)*matrix(dnorm(ax), n, nx)) %*% t(matrix(dnorm(ay), n, nx))/(sum(w) * h[1] * h[2]) # z is the density
  return(list(x = gx, y = gy, z = z))
}

I would like to make the functions geom_density2d and stat_density2d call kd2d.weighted instead of kde2d, and by that making them accept weighted data.

I have never changed any functions in existing R packages so my question is what is the easiest way doing this?

Answer 1

You can actually pass your own density data to geom_contour which would probably be the easiest. Let's start with a sample dataset by adding weights to the geyser data.

library("MASS")
data(geyser, "MASS")
geyserw <- transform(geyser,
   weight = sample(1:5, nrow(geyser), replace=T)
)

Now we use your weighted function to calculate the density and turn it into a data.frame

dens <- kde2d.weighted(geyserw$duration, geyserw$waiting, geyserw$weight)
dfdens <- data.frame(expand.grid(x=dens$x, y=dens$y), z=as.vector(dens$z))

Now we plot the data

ggplot(geyserw, aes(x = duration, y = waiting)) +
    geom_point() + xlim(0.5, 6) + ylim(40, 110) +
    geom_contour(aes(x=x, y=y, z=z), data= dfdens)

And that should do it