Considereing the number of the coordinate in the heatmap - r

Question

I'm trying to make a heatmap considering the value of the point (variable 'x'). But when I run my code I only have the heatmap considering the points, and not its values.
Here is my code:
head(dengue)
lat long x
1 7791000 598157.0 156
2 7790677 598520.0 307
3 7790795 598520.0 153
4 7790153 598808.0 135
5 7790935 598813.0 1888
6 7790765 598881.7 1169
library(ggplot2)
library(ggsn)
hmap <- ggplot(dengue, aes(x=long, y=lat)) +
stat_density2d(aes(fill = ..level..), alpha=0.8, geom="polygon") +
geom_point(colour="red") +
geom_path(data=map.df,aes(x=long, y=lat,group=group), colour="grey50") +
scale_fill_gradientn(colours=rev(brewer.pal(5,"Spectral"))) +
coord_fixed() +
scalebar(location="bottomright",y.min=7781600.0, y.max=7812898.0,
x.min=597998.4, x.max=619721.2,
dist=2, transform = F,
st.dist=.04,dist_unit="km") +
blank() +
guides(fill=guide_legend(title=""))
north2(hmap, x=.7, y=.9, symbol=16)
And here is the map that I got:
Any hint on how can I make a heatmap considering the values of the points (variable 'x'), and not just its coordinates?

Answer 1

There was a post here that describes the adaptation of the MASS package's kde2d function to take into account the weights of points.
library(MASS)
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")
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))
}
This is not natively embedded in ggplot2 as far as I'm aware, but you could preprocess your data outside ggplot to get the data you can put into stat_contour:
# Reading in your example data
zz <- " lat long x
1 7791000 598157.0 156
2 7790677 598520.0 307
3 7790795 598520.0 153
4 7790153 598808.0 135
5 7790935 598813.0 1888
6 7790765 598881.7 1169"
df <- read.table(text = zz)
# Doing the weighted 2d kde
wdf <- kde2d.weighted(df$lat, df$long, df$x)
wdf <- data.frame(lat = wdf$x[row(wdf$z)],
long = wdf$y[col(wdf$z)],
value = wdf$z[T])
# Plotting the result:
ggplot(df, aes(lat, long)) +
stat_contour(data = wdf, aes(z = value, fill = stat(level)), geom = "polygon") +
geom_text(aes(label = x)) # to show the weights
As you can see, the contours are a bit cut off at ugly points, but I suppose this could be amended by playing around with the lims argument of the kde2d.weighted().