1. R for Macroecology GIS + R = Awesome

2. 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

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

4. Geographic data in R  Data types  Vector  Raster  Packages  maptools  raster

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

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

7. 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)

8. 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?

9. 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

10. The structure of a raster object  Stored as a big vector 123456789....n123456789....n 1 2 3 4 5 6 7 8 9................. n ncol = 8

11. 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

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

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

14. 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

15. 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))

16. 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

17. Practice with this stuff

18. Seeing a function  Many of R’s functions are written in R!  You can define your own variations on core functions > 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()... }

19. 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-S Time difference of 0.375 secs

20. 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

21. Raster data in different formats Raster Slow access Little RAM usage 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Matrix Fast access Heavy RAM usage Rows and columns match raster 1 2 3 4 5 6. 60 Vector Fast access Heavy RAM usage Entries in same order as raster

22. Raster data in different formats 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1 2 3 4 5 6. 60 as.matrix() getValues()

23. Raster data in different formats 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1 2 3 4 5 6. 60 as.matrix() getValues() raster() setValues()

24. Raster data in different formats 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 1 2 3 4 5 6. 60 as.matrix() getValues() raster() setValues() as.matrix(,by.row = T) as.vector(t())

25. 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!

26. 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!

27. 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)

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

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

30. 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

31. 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

32. 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