Average values of a point dataset to a grid dataset

Question

I am relatively new to ggplot, so please forgive me if some of my problems are really simple or not solvable at all.

What I am trying to do is generate a \"Heat Map\

Answer 1

I think what you want is something along these lines. I predict that this homebrew is going to be terribly inefficient for large datasets, but it works on a small example dataset. I would look into kernel densities and maybe the raster package. But maybe this suits you well...

The following snippet of code calculates the mean value of cadmium concentration of a grid of points overlaying the original point dataset. Only points closer than 1000 m are considered.

library(sp)
library(ggplot2)
loadMeuse()

# Generate a grid to sample on
bb = bbox(meuse)
grd = spsample(meuse, type = "regular", n = 4000)
# Come up with mean cadmium value
# of all points < 1000m.
mn_value = sapply(1:length(grd), function(pt) {
  d = spDistsN1(meuse, grd[pt,])
  return(mean(meuse[d < 1000,]$cadmium))
})

# Make a new object
dat = data.frame(coordinates(grd), mn_value)
ggplot(aes(x = x1, y = x2, fill = mn_value), data = dat) + 
   geom_tile() + 
   scale_fill_continuous(low = "white", high = muted("blue")) + 
   coord_equal()

which leads to the following image:

An alternative approach is to use an interpolation algorithm. One example is kriging. This is quite easy using the automap package (spot the self promotion :), I wrote the package):

library(automap)
kr = autoKrige(cadmium~1, meuse, meuse.grid)
dat = as.data.frame(kr$krige_output)

ggplot(aes(x = x, y = y, fill = var1.pred), data = dat) + 
   geom_tile() + 
   scale_fill_continuous(low = "white", high = muted("blue")) + 
   coord_equal()

which leads to the following image:

However, without knowledge as to what your goal is with this map, it is hard for me to see what you want exactly.

Answer 2

This slideshow offers another approach--see page 18 for a description of the approach and page 21 for a view of what the results looked like for the slide-maker.

Note however that the slide-maker used the sp package and the spplot function rather than ggplot2 and its plotting functions.