GIS + R = Awesome

R and GIS
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My point of view
The best approach is to do everything in R, interacting with
GIS software only when necessary
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But,
You can also take the opposite approach, through integrated R functionality within GRASS GIS

Today’s plan
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Geographic data types in R
Handling huge files
R in GIS

Geographic data in R
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Data types
Vector
Raster
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Packages maptools raster

Package maptools
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 readShapePoly() reads in a GIS shape file
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Can be plotted
Various functions for converting among formats
Merge polygons

Package raster
 the raster package has everything you need for handling rasters
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Read, write, plot, all kinds of queries and manipulations

What is a raster object?
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A raster contains
A vector of values
A size (nrow, ncol)
Spatial information (extent, projection, datum)
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A raster can have some of these things missing (for example, no data values, or no projection)

What is a raster object?
> mat = raster(“MAT.tif”)
> mat class : RasterLayer dimensions : 2882, 2880, 8300160 (nrow, ncol, ncell) resolution : 0.004166667, 0.004166667 (x, y) extent : 0, 12, 48, 60.00833 (xmin, xmax, ymin, ymax) projection : +proj=longlat +ellps=WGS84 +datum=WGS84
+no_defs +towgs84=0,0,0 values : C:/Users/brody/Documents/Teaching/R for
Macroecology/Week 4/MAT.tif min : ? max : ?
Where’s the data?

Raster objects are different!
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Normal objects are stored in memory, for fast access
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Raster objects are not always
When you define a raster object R looks at the summary information and remembers the hard drive locations
Small rasters often do reside in memory
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Advantages and disadvantages

The structure of a raster object
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Stored as a big vector ncol = 8
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Create a new raster
> newRaster = raster(nrows = 10,ncols = 6,xmn = 0,xmx =
6,ymn = 50,ymx = 60,crs = "+proj=longlat
+datum=WGS84")
> newRaster class : RasterLayer dimensions : 10, 6, 60 (nrow, ncol, ncell) resolution : 1, 1 (x, y) extent : 0, 6, 50, 60 (xmin, xmax, ymin, ymax) projection : +proj=longlat +datum=WGS84 +ellps=WGS84
+towgs84=0,0,0 values : none

Create a new raster
> newRaster = setValues(newRaster,1:60)
> plot(newRaster)

Getting values from a raster
> newRaster[22]
[1] 22
> newRaster[2,4]
[1] 10
> getValues(newRaster)[12]
[1] 12

Plotting a raster
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 plot() xlim and ylim control plotting window (just like usual) col specifies the color palette (this works a bit differently) subsample (defaults to TRUE) determines whether or not to plot every pixel (if TRUE, only plots at most maxpixel pixels)
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 colors rbg() , rainbow() , heat.colors() , terrain.colors() , topo.colors()
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I also like the colors in fBasics package
Can also use image()
Similar, but no scale bar

Plotting examples plot(newRaster,col = rgb(seq(0,1,0.2),0.5,0.5)) plot(newRaster,maxpixels = 7) plot(newRaster,xlim = c(2,5),ylim = c(52,59),col
= rainbow(50))

A few useful ways to explore rasters
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 zoom()
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Opens a new active plotting window with the selected region click()
Queries a value, if xy = TRUE, also returns the x and y coordinates

Practice with this stuff

Seeing a function
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Many of R’s functions are written in R!
> lm function (formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = FALSE, qr
= TRUE, singular.ok = TRUE, contrasts = NULL, offset,
...)
{ ret.x <- x ret.y <- y cl <- match.call()
...
}
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You can define your own variations on core functions

Timing your code
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We are getting to the point where running time can be limiting
There are often multiple possible solutions, it can be worth your time to try to find the fastest
Sys.time()
> S = Sys.time()
> x = rnorm(1000000)
> E = Sys.time()
> E-S
Time difference of 0.375 secs

Handling giant rasters
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Huge rasters can be a pain to work with
They can regularly be larger than your RAM
R solves this by not reading into RAM
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But – if you are doing lots of queries on a raster, it can be much faster to convert it to a matrix first
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Do what you want to do, and then use rm() to free up the memory that the matrix was using

Raster data in different formats
Raster
Slow access
Little RAM usage
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Matrix
Fast access
Heavy RAM usage
Rows and columns match raster
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Vector
Fast access
Heavy RAM usage
Entries in same order as raster

Raster data in different formats as.matrix()
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Raster data in different formats as.matrix()
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Raster data in different formats as.matrix()
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,by.row = T) as.vector(t()) getValues()
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Hard drive management
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It can be a pain to have large rasters clogging up your hard drive
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It would be nice to be able to store them zipped, unzip them automatically when you need them in R, and then delete the unzipped data
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Fortunately, that’s easy!

unzipping and deleting
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R function unzip()
You point it to the file you want unzipped, and it does it
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Then read in the unzipped file
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When you are done with it, use file.remove() to delete it, leaving just the compressed data behind, to save space
BE VERY CAREFUL!

Remote data
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You can do even better – if data are stored at stable
URLs, you can download, unzip, work on and delete data all from R
 download.file(url,filename)
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 unzip(filename) do stuff file.remove(filename)

Command lines
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Anything you can run on a command line can be done from R, using shell()
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Do file manipulations, run scripts, run executables

Linking R and GIS
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File formats
Generating Arc scripts in R
Running R within GRASS GIS

File formats
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 writeRaster() tif, img, grd, asc, sdat, rst, nc, envi, bil
For me, I have found that tif seems to produce hassle-free integration with Arc and others
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 write.shapefile() (package shapefiles)
Also has functions for shp, shx and dbf specifically

Arc scripts from R
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It’s probably not the best way, but if there is something you want to do in Arc, you can generate the script using
R

R and GRASS
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GRASS is a free GIS
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R commands can be accessed from within GRASS using the spgrass6 package in R
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Lets you do any statistical thing you’d like from within a
(fairly) standard GIS