Extract Raster Pixels Values Using Vector Polygons in R

Question

I have been struggling with this for hours. I have a shapefile (called "shp") containing 177 polygons ie 177 counties. This shapefile is overlaid on a raster. My raster (called "ras") is made of pixels having different pollution values.

Now I would like to extract all pixel values and their number of occurrences for each polygon.

This is exactly what the QGIS function "zonal histogram" is doing. But I would like to do the exact same thing in R.

I tried the extract() function and I managed to get a mean value per county, which is already a first step, but I would like to make a pixels distribution (histogram).

Could someone give me a hand ?

Many thanks,

Marie-Laure

Answer 1

Thanks a lot for your help. Next time I promise I will be careful and explain my issue more in details.

With your help I managed to find a solution. I also used this website : http://zevross.com/blog/2015/03/30/map-and-analyze-raster-data-in-r/

For information, first I had to uninstall the "tidyr" package because there was a conflict with the extract function.

In case it can help someone, here is the final code :

# Libraries loading
library(raster) 
library(rgdal)
library(sp)

# raster layer import
ras=raster("C:/*.tif")

# shapefile layer import
shp<-shapefile("C:/*.shp")

# Extract the values of the pixels raster per county
ext <- extract(ras, shp, method='simple')

# Function to tabulate pixel values by region & return a data frame
tabFunc                            <- function(indx, extracted, region, regname) {
  dat                              <- as.data.frame(table(extracted[[indx]]))
  dat$name                         <- region[[regname]][[indx]]
  return(dat)
}

# run through each county & compute a table of the number
# of raster cells by pixel value. ("CODE" is the county code) 
tabs <- lapply(seq(ext), tabFunc, ext, shp, "CODE")

# assemble into one data frame
df <- do.call(rbind, tabs)  

# to see the data frame in R
print(df)

# table export 
write.csv(df,"C:/*.csv", row.names = FALSE)

Answer 2

Always please include a minimal, self-contained, reproducible example. That makes it easy to answer, and easy for others to learn from. Also, making such an example will allow you to answer your own question in most cases. Here is one (almost literally from ?raster::extract, so not difficult to make)

library(raster)
r <- raster(ncol=36, nrow=18, vals=rep(1:9, 72))
cds1 <- rbind(c(-180,-20), c(-160,5), c(-60, 0), c(-160,-60), c(-180,-20))
cds2 <- rbind(c(80,0), c(100,60), c(120,0), c(120,-55), c(80,0))
polys <- spPolygons(cds1, cds2)

Now you can do

v <- extract(r, polys)
par(mfrow=c(1,2))
z <- lapply(v, hist)

Or more fancy

mains <- c("first", "second")
par(mfrow=c(1,2))
z <- lapply(1:length(v), function(i) hist(v[[i]], main=mains[i]))

Or do you want a barplot

z <- lapply(1:length(v), function(i) barplot(table(v[[i]]), main=mains[i]))