R statistics

Q: How do I enjoy high quality statistic analysis in GRASS?

A: Well, GRASS has got an interface to the most powerful statistics analysis package around: R (http://www.r-project.org)

The spgrass6 R addon toolbox provides the R ←→ GRASS interface.

Quick start

For the impatient just start it:

 > R
 
 #and install packages directly from the net
 pkgs <- c('akima', 'spgrass6', 'RODBC', 'VR')
 
 install.packages(pkgs, dependencies=TRUE, type='source') 

Once you have R in your system, take a look at http://grass.osgeo.org/statsgrass/grass6_r_install.html .

Installation

First of all you need to add R to your system, the R version must be >= 1.9.1.

R and many of its addon packages are pre-built and distributed through the CRAN network of mirrors. In addition many Linux distributions prepackage R and a number of the most popular addon toolboxes.

Source packages

From the R console first pick a local mirror:

chooseCRANmirror()

you can then see what it picked with

options("repos")

To permanently save the mirror site add it to ~/.Rprofile. For example:

options(repos=c(CRAN="http://cran.stat.auckland.ac.nz"))

and then run install.packages() as in the Quick Start section above.

For more information see http://cran.r-project.org/doc/manuals/R-admin.html

Linux

Debian and Ubuntu

R and a number of pre-build cran packages are already present in the main repositories. Start with:

apt-get install r-base r-cran-vr r-cran-rodbc r-cran-xml

• Debian and Ubuntu specific help is also available from the R-project website.

RPM based

• RedHat, Suse, Mandrake and similar distros: take the latest R RPM and install it

Mac OSX

• for install.packages() you might have to rely on building packages from source code. try:

install.packages(pkgname, type="source", dependencies = TRUE)

MS Windows

• -

Notes

• All the necessary functions for the GRASS 6 interface are now in packages on CRAN, so that on Linux/Unix (or Mac OSX) installing rgdal from source with PROJ4 and GDAL installed, or Windows installing from binary, the required packages are: sp; maptools (now includes spmaptools); rgdal (now includes spGDAL, spproj); spgrass6 - now all on CRAN.

Modules

• v.kridge

Special requirements: python-rpy2

(take from man page)

See also

• http://grass.ibiblio.org/statsgrass/index.php#grassR

• Using GRASS and R: http://grass.osgeo.org/statsgrass/grass6_r_interface.html

• Connecting R to RDBMS: http://grass.osgeo.org/statsgrass/r_and_dbms.html

• R-Statistics homepage

• R-spatial main web page

• R Spatial Projects at ASU

• http://r-spatial.sourceforge.net/xtra/xtra.RHnw.html#spgrass6

• Neural Networks with GRASS and R (posted by Markus Neteler on the grass-user mailing list) http://www.uam.es/proyectosinv/Mclim/pdf/MBenito_EcoMod.pdf

• A detailed example on the use of GRASS and R, with spearfish data: http://casoilresource.lawr.ucdavis.edu/drupal/node/438

• Using R and GRASS with cygwin: It is possible to use Rterm inside the GRASS shell in cygwin, just as in Unix/Linux or OSX. You should not, however, start Rterm from a cygwin xterm, because Rterm is not expecting to be run in an xterm under Windows, and loses its input. If you use the regular cygwin bash shell, but need to start display windows, start X from within GRASS with startx &, and then start Rterm in the same cygwin shell, not in the xterm.

• Spatial data in R (sp) is a R library that provides classes and methods for spatial data (points, lines, polygons, grids), and to new or existing spatial statistics R packages that use sp, depend on sp, or will become dependent on sp, such as maptools, rgdal, splancs, spgrass6, gstat, spgwr and many others.

• RPy - Python interface to the R Programming Language

Articles

• GRASS News vol.3, June 2005 (R. Bivand. Interfacing GRASS 6 and R. GRASS Newsletter, 3:11-16, June 2005. ISSN 1614-8746).
• OSGeo Journal vol. 1 May 2007 (R. Bivand. Using the R— GRASS interface. OSGeo Journal, 1:31-33, May 2007. ISSN 1614-8746).
• GRASS Book, last chapter