Here I am using the gDistance function in the rgeos topology library. I am using a brute force double loop but it is surprisingly fast. It takes less than 2 seconds for 142 points and 10 polygons. I am sure that there is a more elgant way to perform the looping.
require(rgeos)
# CREATE SOME DATA USING meuse DATASET
data(meuse)
coordinates(meuse) <- ~x+y
pts <- meuse[sample(1:dim(meuse)[1],142),]
data(meuse.grid)
coordinates(meuse.grid) = c("x", "y")
gridded(meuse.grid) <- TRUE
meuse.grid[["idist"]] = 1 - meuse.grid[["dist"]]
polys <- as(meuse.grid, "SpatialPolygonsDataFrame")
polys <- polys[sample(1:dim(polys)[1],10),]
plot(polys)
plot(pts,pch=19,cex=1.25,add=TRUE)
# LOOP USING gDistance, DISTANCES STORED IN LIST OBJECT
Fdist <- list()
for(i in 1:dim(pts)[1]) {
pDist <- vector()
for(j in 1:dim(polys)[1]) {
pDist <- append(pDist, gDistance(pts[i,],polys[j,]))
}
Fdist[[i]] <- pDist
}
# RETURN POLYGON (NUMBER) WITH THE SMALLEST DISTANCE FOR EACH POINT
( min.dist <- unlist(lapply(Fdist, FUN=function(x) which(x == min(x))[1])) )
# RETURN DISTANCE TO NEAREST POLYGON
( PolyDist <- unlist(lapply(Fdist, FUN=function(x) min(x)[1])) )
# CREATE POLYGON-ID AND MINIMUM DISTANCE COLUMNS IN POINT FEATURE CLASS
pts@data <- data.frame(pts@data, PolyID=min.dist, PDist=PolyDist)
# PLOT RESULTS
require(classInt)
( cuts <- classIntervals(pts@data$PDist, 10, style="quantile") )
plotclr <- colorRampPalette(c("cyan", "yellow", "red"))( 20 )
colcode <- findColours(cuts, plotclr)
plot(polys,col="black")
plot(pts, col=colcode, pch=19, add=TRUE)
The min.dist vector represents the row number of the polygon. For instance you could subset the nearest polygons by using this vector as such.
near.polys <- polys[unique(min.dist),]
The PolyDist vector contain the actual Cartesian minimum distances in the projection units of the features.