R for Macroecology GIS + R = Awesome. R and GIS My point of view The best approach is to do everything in R, interacting with GIS software only when.

R for Macroecology

GIS + R = Awesome

R and GIS My point of view

The best approach is to do everything in R, interacting with GIS software only when necessary

But, You can also take the opposite approach, through

integrated R functionality within GRASS GIS

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

Geographic data in R Data types

Vector Raster

Packages maptools raster

Package maptools readShapePoly() reads in a GIS shape file

Can be plotted Various functions for converting among

formats Merge polygons

Package raster the raster package has everything you need

for handling rasters

Read, write, plot, all kinds of queries and manipulations

What is a raster object? A raster contains

A vector of values A size (nrow, ncol) Spatial information (extent, projection, datum)

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”)> matclass : 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! Normal objects are stored in memory, for fast

access

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

Advantages and disadvantages

The structure of a raster object Stored as a big vector

123456789....n

1 2 3 4 5 6 7 8

9 . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . n

ncol = 8

Create a new raster> newRaster = raster(nrows = 10,ncols = 6,xmn = 0,xmx =

6,ymn = 50,ymx = 60,crs = "+proj=longlat +datum=WGS84")

> newRasterclass : 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)

> newRaster[22][1] 22

> newRaster[2,4][1] 10

> getValues(newRaster)[12][1] 12

Getting values from a raster

Plotting a raster 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)

colors rbg(), rainbow(), heat.colors(), terrain.colors(), topo.colors()

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 zoom()

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 Many of R’s functions are written in R!

You can define your own variations on core functions

> lmfunction (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()

...}

Timing your code 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-STime difference of 0.375 secs

Handling giant rasters 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

But – if you are doing lots of queries on a raster, it can be much faster to convert it to a matrix first

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

RasterSlow accessLittle RAM usage

1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17 1819 20 21 22 23 2425 26 27 28 29 3031 32 33 34 35 3637 38 39 40 41 4243 44 45 46 47 4849 50 51 52 53 5455 56 57 58 59 60

MatrixFast accessHeavy RAM usageRows and columns match raster

123456....60

VectorFast accessHeavy RAM usageEntries in same order as raster

Raster data in different formats

1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17 1819 20 21 22 23 2425 26 27 28 29 3031 32 33 34 35 3637 38 39 40 41 4243 44 45 46 47 4849 50 51 52 53 5455 56 57 58 59 60

123456....60

as.matrix() getValues()

Raster data in different formats

1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17 1819 20 21 22 23 2425 26 27 28 29 3031 32 33 34 35 3637 38 39 40 41 4243 44 45 46 47 4849 50 51 52 53 5455 56 57 58 59 60

123456....60

as.matrix() getValues()raster()

setValues()

Raster data in different formats

1 2 3 4 5 6 7 8 9 10 11 1213 14 15 16 17 1819 20 21 22 23 2425 26 27 28 29 3031 32 33 34 35 3637 38 39 40 41 4243 44 45 46 47 4849 50 51 52 53 5455 56 57 58 59 60

123456....60

as.matrix() getValues()raster()

setValues()

as.matrix(,by.row = T)

as.vector(t())

Hard drive management It can be a pain to have large rasters clogging

up your hard drive

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

Fortunately, that’s easy!

unzipping and deleting R function unzip()

You point it to the file you want unzipped, and it does it

Then read in the unzipped file

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 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) unzip(filename) do stuff file.remove(filename)

Command lines Anything you can run on a command line can

be done from R, using shell()

Do file manipulations, run scripts, run executables

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

File formats 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

write.shapefile() (package shapefiles) Also has functions for shp, shx and dbf specifically

Arc scripts from R 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 GRASS is a free GIS

R commands can be accessed from within GRASS using the spgrass6 package in R

Lets you do any statistical thing you’d like from within a (fairly) standard GIS