The raster package in R allows users to work with geographic grid data. It contains functions for reading raster files into R, performing operations on raster layers like cropping and aggregation, and visualizing raster maps. Common sources of global climate data that can be accessed in R include WorldClim, the Global Summary of Day from NOAA, and datasets available on the CGIAR website.

1. Climate data in R
with the raster package
Jacob van Etten
Alberto Labarga

2. Packages
There are many packages specifically created
for R that allow you to do specialized tasks.
One of these packages is raster, created by
Robert Hijmans and Jacob van Etten (mainly
the former, though).
The raster package allows you to work with
geographical grid (raster) data.

3. Get raster in RStudio
Click on the “Packages” tab in the lower right
corner.
Click “Install Packages”.
Type “raster” and click on “Install”.
Leave “Install dependencies” checked. This will
also get some other essential packages.

4. Load the package
With the following command, we load the
package into R. Make sure you put this in the first
line of your new script.
library(raster)
help(package="raster")
The second function gives you an overview of the
functions in the package.

5. The raster() function
The main function to read raster data into R is
called (very conveniently) raster.
?raster
Let’s make a raster!
r1 <- raster()
r1
As you can see, there are no values in the
raster. Next thing to solve.

6. Adding values
How many values do we need to fill the
raster? The function ncell() will tell us.
n <- ncell(r1)
Let’s make a vector with n random values
between 0 and 1 with the function runif().
vals<- runif(n)
And we add the values to the raster.
values(r1) <- vals

7. Raster graphics
We make a picture of the raster we just made.
plot(r1, main=“My first raster map in R”)
Now let’s take a look at the different options
that plot() gives.
?plot
Click “Plot a Raster* object”.
Also, take a look at the examples and try some
if you want.

8. Real data
Let’s get some real data to play with.
http://goo.gl/4488T
This is a raster representing current conditions
(a bit over 1 MB).
Unzip the file, and put it in a (new) folder.
Now make this folder your working directory
in R.
setwd(“D:/yourfolder”)

9. Getting raster data into R
Reading this data into R is really easy now.
r2 <- raster(“current_bio_1_1.asc”)
What class is this raster?
class(r2)
Plot this raster.

10. Cutting an area of interest
The function extents requires a vector of 4 values:
{xmin, xmax, ymin, ymax}. For instance:
newExtent <- extent(c(60, 100, 0, 40))
Or choose your own area of interest, for instance
using Google Earth.
Then cut the new extent out of r2 and visualize.
r3 <- crop(r2, newExtent)
plot(r3)

11. Raster algebra
It is very convenient to calculate with rasters.
Try this and visualize the result.
r4 <- r3 + sqrt(r3)
What happens when you do the following and
why?
r5 <- r2 + r3

12. Some operations
Aggregating cells means the grid becomes
coarser. By default the function aggregate()
will take the mean of the cells it will
aggregate.
r6 <- aggregate(r2, fact=2)
Now take a look at the examples under
?aggregate and try to understand what
happens.

13. Interpolation
See if you can work this out for yourself.
Take a look at the first example of
?interpolate

14. Sources of data
For an overview of a lot of relevant climate
and weather data, visit this website:
http://iridl.ldeo.columbia.edu/

15. Moreover...
Worldclim data are global climate data (get it
using the raster package, getData function)
NCDC-NOAA – Global Summary of Day,
weather data from thousands of stations
(weatherData package)
CCAFS data

16. Worldclim
Precipitation at 10 minute resolution
wc <- getData(“worldclim”, var=“prec”, res=10)
plot(wc)

17. Global Summary of Day
Available from:
ftp://ftp.ncdc.noaa.gov/pub/data/gsod/
These data are massive.
Use the weatherData package to download
these data.