Raster package jacob
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The document discusses using the raster package in R to work with geographical grid data. It covers downloading and loading the raster package, creating raster objects and adding random values, reading in real climate data files, performing operations like cropping and aggregation, and sources for global climate data like WorldClim.
Raster package jacob
- 1. Climate data in R with the raster packageJacob van Etten
- 2. PackagesThere 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 RStudioClick 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 packageWith 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")Thesecondfunctiongivesyouanoverview of thefunctions in thepackage.
- 5. The raster() functionThe main function to read raster data into R is called (very conveniently) raster.?rasterLet’s make a raster!r1 <- raster()r1As you can see, there are no values in the raster. Next thing to solve.
- 6. Adding valuesHow 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 graphicsWe 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.?plotClick “Plot a Raster* object”.Also, take a look at the examples and try some if you want.
- 8. Real dataLet’s get some real data to play with.http://goo.gl/4488TThis 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 RReading 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 interestThe function extents requires a vector of 4 values: {xmin, xmax, ymin, ymax}. For instance:newExtent <- extent(c(60, 100, 0, 40))Orchooseyourownarea of interest, forinstanceusing Google Earth.Then cut the new extent out of r2 and visualize.r3 <- crop(r2, newExtent)plot(r3)
- 11. Raster algebraIt 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 operationsAggregating 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. InterpolationSee if you can work this out for yourself.Take a look at the first example of ?interpolate
- 14. Sources of dataFor 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. WorldclimPrecipitation at 10 minute resolutionwc <- getData(“worldclim”, var=“prec”, res=10)plot(wc)
- 17. Global Summary of DayAvailable from: ftp://ftp.ncdc.noaa.gov/pub/data/gsod/These data are massive.Use the weatherData package to download these data.