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I have a set of raster images which represent a specific month over the years, and I want to make a timeline of the average values of an area using a shapefile.

How do I extract the values from the rasters and import them in R in a manner that I can use them?

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2 Answers 2

up vote 8 down vote accepted

Here is example code. It is fairly straight forward to adapt this code to work in a loop for processing all of your rasters. If your rasters share a common extent and resolution you can create a raster stack and loop through the bands in the stack. To create a vector containing all rasters in a directory, in a specific format, you can use "list.files" and then pass this vector to stack.


rlist=list.files(getwd(), pattern="img$", full.names=TRUE) 

r <- stack(rlist)   

    # Add required libraries

    # Set working directory, raster, in and out shapefiles
    rdata <- "Year2012.img"

    # Read polygon feature class shapefile
    sdata <- readOGR(dsn=getwd(), layer=inshp)

    # Read raster
    r <- raster(rdata)

    # Extract raster values to list object
    r.vals <- extract(r, sdata)

    # Use list apply to calculate mean for each polygon
    r.mean <- lapply(r.vals, FUN=mean)

    # Join mean values to polygon data
    sdata@data <- data.frame(sdata@data, m2012=r.mean)

    # Write results
    writeOGR(sdata, getwd(), outshp, driver="ESRI Shapefile", check_exists=TRUE, 
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+1 - excellent solution, well laid out and complete answer. –  Simbamangu Jul 10 '12 at 4:10
+1 nice solution! –  WAF Aug 30 '13 at 15:09

Read the shapefile into a SpatialPolygonsDataFrame (readOGR function from package rgdal)

Read the raster into a Raster object (raster function from package raster)

Use extract(raster, spdf) to get the grid cells under each polygon. Then run mean on them.

Repeat over your set of raster images...

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