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'''Q:''' How do I enjoy high quality statistic analysis in GRASS?
{{toc|right}}


'''A:''' Well, GRASS has got an interface to the most powerful statistics analysis package around: '''''R''''' (http://www.r-project.org)
=== Overview ===
: The [http://cran.r-project.org/web/packages/spgrass6/ '''spgrass6'''] ''R'' addon package provides the R ←→ GRASS interface.


Using R in GRASS GIS directly has two meanings:
High quality statistic analysis in GRASS GIS is possible thanks to an interface to one of the most powerful statistics analysis package around: '''''R''''' ([http://www.r-project.org/ http://www.r-project.org]). This R ←→ GRASS GIS interface is provided by the [https://cran.r-project.org/package=rgrass7 rgrass7] ''R'' addon package. The possibility to directly interact with R strongly increases the statistical and geospatial analysis capabilities of GRASS. See [https://cran.r-project.org/web/views/Spatial.html CRAN Task View: Analysis of Spatial Data] for an overview of the R packages and functions that can be used for reading, visualizing, and analyzing spatial data. 


* The '''first''' is that R is run "on top of" GRASS, transferring GRASS data to R to run statistical functions on the imported data as R objects in memory, and possibly transfer the results back to GRASS.
R can be used in conjunction with GRASS GIS in different ways:
* The '''second''' is to leave the data mostly in GRASS, and to use R as a scripting language "on top of" GRASS with execGRASS() - in this case, little data is moved to R, so memory constraints are not important, but R functionality is available.


=== Quick start ===
* ''Running R 'on top of' GRASS'', transferring GRASS data to R to run statistical functions on the imported data as R objects in memory, and possibly transfer the results back to GRASS. GRASS raster and vector data can be imported in R using the [https://rdrr.io/rforge/rgrass7/man/readVECT.html readVECT()] and [https://rdrr.io/rforge/rgrass7/man/readRAST.html readRAST()] function provided by [http://cran.r-project.org/web/packages/rgrass7/ rgrass7]. Similarly, results can be written back using the [https://rdrr.io/rforge/rgrass7/man/readRAST.html writeRAST()] and [https://rdrr.io/rforge/rgrass7/man/readVECT.html writeVECT()] functions.
* ''Using R as a scripting language in GRASS''. GRASS functions can be run from R using the [https://rdrr.io/rforge/rgrass7/man/execGRASS.html execGRASS()] function in the ''rgrass7'' package. R scripting tools, such as powerful string-processing tools and functions for manipulating file names, can be used to 'glue' different functions and tools together.
* ''Using GRASS GIS as a geospatial library in R''. GRASS GIS can be used to extent the geospatial capability of R by: (1) offering an extensive and robust set of geospatial tools, and (2) provides a way to work with very large (larger-than-memory) spatial data sets. See Using 'GRASS GIS functionality within a R session' below for more information.


For the impatient just start it:
In practice, one will often combine different approaches, with scripts running GRASS functions, importing resulting layers in R for further analysis and visualization, and creating new layers in R that are imported back in GRASS.


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


or to get all packages for spatial analysis in one:
R Windows binary packages distributed by the Comprehensive R Archive Network (CRAN) using OSGeo software (chiefly PROJ, GDAL and GEOS) use custom built binaries compatible with the build train used by R and are static linked; at present both 32-bit and 64-bit binaries are deployed. CRAN packages for macOS are also static linked to custom built binaries, again using the build train used by R. Static linkage is used to avoid having to deploy a package manager for external software on which R packages depend, since CRAN as a package manager already supports over 15000 packages with three binary versions (devel, release, old release) for Windows and macOS.
 
  > R
 
  #To automatically install the spatial task view, the ctv package needs to be installed, e.g., via
  install.packages("ctv")
  library("ctv")
 
  install.views("Spatial")
  #or
  update.views("Spatial")
 
 
Once you have '''''R''''' and '''''spgrass6''''' on your system, have a look at this tutorial:
: http://grass.osgeo.org/statsgrass/grass_geostats.html


=== Installation ===
=== Installation ===


First of all you need to install '''''R''''' onto your system.
==== Installation of R core software ====


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.
(see also [[R statistics/Installation]])


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.
''Note: the ...-devel packages are needed if you want to install extra packages incl. '''rgrass7''' on your computer.''


==== Source packages ====
Fedora:
  sudo dnf install R-core
  # further packages needed in order to locally compile "rgrass7"
  # F29
  sudo dnf install proj-epsg proj-nad proj-devel gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel udunits2-devel
  # F33+
  sudo dnf install proj-devel proj-datumgrid proj-datumgrid-europe proj-datumgrid-north-america proj-datumgrid-world proj-datumgrid-oceania gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel udunits2-devel


From the R console first pick a local mirror:
Ubuntu:
chooseCRANmirror()
  sudo apt-get install r-base
  # further packages needed in order to locally compile "rgrass7"
  sudo apt-get install proj-bin proj-data libproj-dev libgdal-dev libsqlite3-dev libxml2-dev r-base-dev


you can then see what it picked with
==== Installation of the rgrass7 package  ====
options("repos")
To install the R package '''rgrass7''' on newer versions of R, you simply start R and install the package directly with:


To permanently save the mirror site add it to <tt>~/.Rprofile</tt>. For example:
<source lang="rsplus">
options(repos=c(CRAN="<nowiki>http://cran.stat.auckland.ac.nz</nowiki>"))
install.packages("rgrass7", dependencies = TRUE)
</source>


and then run <tt>install.packages()</tt> as in the Quick Start section above.
This will install '''''rgrass7''''' and all its dependencies. To use the package, you first need to load it:


For more information see http://cran.r-project.org/doc/manuals/R-admin.html
<source lang="rsplus">
 
library("rgrass7")
==== Linux ====
</source>
 
===== 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
 
Once those are installed start "<tt>R</tt>" at the command prompt and install the libraries not packaged by the OS:
: ''n.b. <tt>r-cran-sp</tt> is now shipped as an official Debian/Ubuntu package and can be installed with <tt>apt</tt> as above''


install.packages("sp")
If you are using Rstudio, you can install the rgrass7 package in the usual way (tool → packages). For further instructions and for trouble shooting, see [[R_statistics/Installation]]
install.packages("gstat")


Debian/Lenny ships with ''R'' 2.7.1 which is too old for the modern ''rgdal'' package. So we have to fetch an old one from the archive and build it from the Linux command line:
=== How to use ===
$ wget http://cran.r-project.org/src/contrib/Archive/rgdal/rgdal_0.6-24.tar.gz
$ R CMD INSTALL -l /usr/local/lib/R/site-library rgdal_0.6-24.tar.gz


If you are using a newer version of R than that you can install the ''rgdal'' CRAN package directly:
You can use Using R in conjunction with GRASS GIS in two different ways: (1) run R within a GRASS GIS session, and (2) run GRASS GIS within a R session.
  install.packages("rgdal")


And finally, back inside the ''R'' session:
==== Using R within a GRASS GIS session ====
install.packages("spgrass6")


* [http://cran.r-project.org/bin/linux/debian/README Debian] and [http://cran.r-project.org/bin/linux/ubuntu/README Ubuntu] specific help is also available from the R-project website.
If you are primarily a GIS user who wants to run e.g., some statistical tests not available in GRASS, you probably want to run R from within a GRASS GIS session. To do so, first start GRASS GIS and then start R (or RStudio) from the GRASS GIS command line. For more information and examples, see [[R_statistics/rgrass7]].  


You can also use the CRAN Debian package repository: (pick one; adjust distribution as needed [here "Debian/testing"])
==== Using GRASS GIS functionality within a R session ====
deb http://debian.cran.r-project.org/cran2deb/debian-i386 testing/
deb http://debian.cran.r-project.org/cran2deb/debian-amd64 testing/


===== RPM based =====
If you are primarily a R user, who wants to take advantage of the advanced geospatial functions in GRASS, you probably want to use GRASS GIS within a R session. To connect to a GRASS GIS database from within R (or Rstudio), see the instructions on [[R_statistics/rgrass7]]. If you are a first time GRASS GIS User, you may want to check out the information for [https://grass.osgeo.org/grass7/ first time users] first.


* '''RedHat, Fedora, openSuse, Mandriva''' and similar distros: take the latest '''''R''''' RPM and install it
=== Examples ===


''R'' and a number of pre-build cran packages are already present in the main repositories. Start with:
* [https://grasswiki.osgeo.org/wiki/Temporal_data_processing/GRASS_R_raster_time_series_processing Temporal data processing wiki]; a tutorial about time series processing with GRASS GIS and R.
* A short guide on how to [https://tutorials.ecodiv.earth/toc/from_grass_to_r.html get a GRASS function output in R].
* [https://tutorials.ecodiv.earth/toc/grass-import-glcf.html Importing GLCF MODIS woody plant cover] in a GRASS GIS database using an R script.
* [https://tutorials.ecodiv.earth/toc/grass-r-gbif.html Use R to obtain gbif data] and import it in a GRASS GIS database.


# su
=== Getting help ===
# yum install R-core R-XML
# exit


Once those are installed, start as normal user "<tt>R</tt>" at the command prompt and install the libraries not packaged by the distro provider:
==== Manual pages ====
If you are in R and have loaded the rgrass7 package, you can get the help page of ''rgrass7'' by typing:


<source lang="bash">
<source lang="rsplus">
R
?rgrass7
install.packages("spgrass6", dependencies = TRUE)
</source>
</source>


==== Mac OSX ====
Similarly, to get help for a specific function, e.g., for the function ''readRAST()'', you type:
 
Start a R session, then


* for install.packages() you might have to rely on building packages from source code. try:
<source lang="rsplus">
<source lang="bash">
?readRAST
R
install.packages("spgrass6", type="source", dependencies = TRUE)
</source>
</source>


'''Startup of GRASS from within R:'''
You can also start the ''R'' help page in your browser:
 
First you need to find the path to the GRASS binaries: Control-click on the GRASS.app and you'll get a popup menu; select "Show Package Contents" - this opens you to the directory structure. Go to Contents->MacOS which would be "GISBASE"; So, in my case, the "gisBase" parameter is "/HD/Applications/Grass-6.4/Contents/MacOS". If you Command-click at the top of the window on the folder icon beside "MacOS" (from the line above this one), you can see the full path.
 
Now we can run GRASS from within a R session:


<source lang="bash">
<source lang="rsplus">
initGRASS(gisBase ='/Applications/GRASS/GRASS-6.4.app/Contents/MacOS',
help.start()
          location = 'geostat2012_ll', mapset = 'user1',
          gisDbase = '/Users/Lars/Documents/Biologi/grassdata', override = TRUE)
</source>
</source>


===== Troubleshooting =====
Now, to get the information about the package, select the '''Packages''' entry and then '''rgrass7'''.  
If you get an error message when trying to call GRASS from R containing this line: dyld: Library not loaded: /usr/local/lib/libintl.8.dylib you need to establish a link from /Applications/Grass/GRASS-7.0.app/Contents/MacOS/lib/libintl.8.dylib to /usr/local/lib. This can be done through Terminal with the command:


<source lang="bash">
==== Support from the community ====
sudo ln -s /Applications/Grass/GRASS-7.0.app/Contents/MacOS/lib/libintl.8.dylib /usr/local/lib/
</source>


Note: The path to the GRASS-x.x.app must reflect your own configuration.
Primary support for ''R'' + GRASS and the ''rgrass7'' package is through the [http://lists.osgeo.org/mailman/listinfo/grass-stats grass-stats] mailing list.


==== MS Windows ====
=== Useful links ===


===== Installation =====
==== R ====
Run:
install.packages("spgrass6")


or install Task View 'Spatial' - Analysis of Spatial Data
* [http://r-spatial.org/ R][http://r-spatial.org/ -spatial main web page]
install.packages("ctv")
* [https://cran.r-project.org/web/views/Spatial.html CRAN Task View: Analysis of Spatial Data]
  library("ctv")
* [https://cengel.github.io/rspatial/2_spDataTypes.nb.html Introduction to Spatial Data Types in R]
install.views("Spatial")
* [https://github.com/edzer/sp R Classes and Methods for Spatial Data] - <code>sp</code> package
* [https://github.com/r-spatial/sf Simple Features for R] - <code>sf</code> package.
* [https://blog.dominodatalab.com/applied-spatial-data-science-with-r/ Applied Spatial Data Science with R] (blog post with examples)


===== GRASS 6: Calling R from GRASS =====
==== Related ====
On Windows, the easiest way is calling  R in a running GRASS 6 session by specifying the full path to the R startup executable. Note that all "\" must be changed to "/", furthermore quote white spaces in the path:


  GRASS 6.4> C:/Users/"Catherine user"/Documents/R/R-2.15.1/bin/i386/R.exe
* [https://rpy2.bitbucket.io/ Python interface to the R Programming Language]: can be used to run R in GRASS Python scripts.
  R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
  ...
  library(spgrass6)


In some cases, you can use this path:
==== Articles & books ====
- For 32 bits:  <source lang="bash">C:/"Program Files"/R/R-2.15.1/bin/i386/R.exe --save</source>
- For 64 bits: <source lang="bash">C:/"Program Files"/R/R-2.15.1/bin/x64/R.exe --save</source> 


The best solution is then to add this path to the %PATH% entry in the grass64.bat-starting script.
* Neural Networks with GRASS and R [https://dx.doi.org/10.1016/j.ecolmodel.2006.03.015 DOI: 10.1016/j.ecolmodel.2006.03.015]
 
* [http://www.asdar-book.org/ Applied Spatial Data Analysis with R]. Roger S. Bivand, Edzer Pebesma and V. Gómez-Rubio. UseR! Series, Springer. 2nd ed. 2013, xviii+405 pp., Softcover. ISBN: 978-1-4614-7617-7
===== GRASS 6: calling GRASS from R =====
* [http://www.grassbook.org/ GRASS Book], see last chapter
On Windows, the second easiest way is calling GRASS from R with ''initGRASS()'' from the R package spgrass6(). After GRASS has been initialized for R, you have access to GRASS commands from within R (you may need to adjust the path):
* [http://www.osgeo.org/journal OSGeo Journal] vol. 1 May 2007 (R. Bivand. Using the R— GRASS interface. ''OSGeo Journal'', 1:31-33, May 2007. ISSN 1614-8746).
 
* [http://grass.osgeo.org/newsletter/grassnews3.html GRASS News vol.3], June 2005 (R. Bivand. Interfacing GRASS 6 and R. ''GRASS Newsletter'', 3:11-16, June 2005. ISSN 1614-8746)
  spgrass6()
  loc <- initGRASS("C:\\Program Files\\GRASS 6.4", home=tempdir())
 
 
For an example, see http://geomorphometry.org/content/geomorphometry-r-saga-ilwis-grass
 
===== GRASS 6 Usage III (taken from the grass-stats-ML) =====
 
http://lists.osgeo.org/pipermail/grass-stats/2010-September/001274.html
 
at the moment there is following for a Grass-R-connection implemented in WinGRASS 64
 
(1) the WinGrass64-installer searches during installation for a installed R and writes -
if found - the R-installation-path to %PATH% in the grass64.bat-starting script
(see http://trac.osgeo.org/grass/browser/grass/branches/develbranch_6/mswindows/GRASS-Installer.nsi#L660)
 
--> this means that you FIRST have to install R and then winGRASS!
 
(2) you can start "GRASS-command-line" - it's a windows-command-line, not a msys-rxvt-terminal
(you can find this starting option under Programs -> GRASS64 -> Grass command line; but not as a desktop icon)
 
with this starting option you start a Grass-session in the good old text mode.
if you type R in the command line, then you start R inside a GRASS-session like in Linux.
 
===== GRASS 7 Usage =====
 
In WinGRASS 7 ([http://grass.osgeo.org/download/software.php#g70x standalone installer]) the [http://code.google.com/p/batchfiles/ Windows batchfiles for use with R] are now integrated for a smooth WinGRASS-R-integration. The R-installation-path is dynamically loaded into PATH.
 
Start WinGRASS 7, bring the WinGRASS-windows console in front and type ''R'' for opening a R-session (command line mode) inside a GRASS-session.
 
[[Image:wingrass7_R_integration.png|600px|center]]
 
Start WinGRASS 7, bring the WinGRASS-windows console in front and type ''RGui'' for opening a R-session (GUI mode) inside a GRASS-session.
 
[[Image:wingrass7_RGui_integration.png|600px|center]]
 
===== Open tickets =====
 
* Ticket {{trac|1103}} (new enhancement) WinGrass64 - windows-commandline not released: a Grass-session with wxGui, command-line and R inside a Grass-session would be possible (as already does in WinGrass7)
* Ticket {{trac|1149}} (new enhancement) WinGrass - load R-installation-path dynamically into PATH:  Wingrass would recognize also an upgraded R-installation (which does not at the moment)
 
=== Command help ===
 
Start the ''R'' help browser:
help.start()
 
* Select '''Packages''' and then '''spgrass6'''.
 
=== Running ===
: ''by Roger Bivand''
 
The ''R'' interface for GRASS 5.4 was provided by a CRAN package called ''grass''. Changes going forward to the current GRASS 6 release meant that the interface had to be rewritten, and this provided the opportunity to adapt it to the ''sp'' CRAN package classes. Because GRASS provides the same kinds of data as ''sp'' classes handle, and relies on much of the same open source infrastructure (PROJ.4, GDAL, OGR), this step seemed sensible. Wherever possible ''spgrass6'' tries to respect the [[current region]] in GRASS to avoid handling raster data with different resolutions or extents. ''R'' is assumed to be running within GRASS:
 
==== Startup ====
* Start GRASS. At the GRASS command line start ''R''.
: ''In this example we will use the sample Spearfish dataset.''
 
Reset the region settings to the defaults
GRASS> g.region -d
 
Launch R from the GRASS prompt
GRASS> R
 
Load the ''spgrass6'' library:
> library(spgrass6)
 
Get the GRASS environment (mapset, region, map projection, etc.); you can display the metadata for your location by printing G:
> G <- gmeta6()
 
==== Reading in data ====
Read in two raster maps:
> spear <- readRAST6(c("geology", "elevation.dem"),
+          cat=c(TRUE, FALSE), ignore.stderr=TRUE,
+          plugin=NULL)
 
 
The metadata are accessed and available, but are not (yet) used to structure the ''sp'' class objects, here a SpatialGridDataFrame object filled with data from two Spearfish layers. Here is a plot of the elevation data:
> image(spear, attr = 2, col = terrain.colors(20))
 
Add a title to the plot:
> title("Spearfish elevation")
 
[[Image:R_stats_elev.png|center]]
 
In addition, we can show what is going on inside the objects read into R:
> '''str(G)'''
<pre>
List of 26
$ GISDBASE    : chr "/home/rsb/topics/grassdata"
$ LOCATION_NAME: chr "spearfish57"
$ MAPSET      : chr "rsb"
$ DEBUG        : chr "0"
$ GRASS_GUI    : chr "text"
$ projection  : chr "1 (UTM)"
$ zone        : chr "13"
$ datum        : chr "nad27"
$ ellipsoid    : chr "clark66"
$ north        : num 4928010
$ south        : num 4913700
$ west        : num 589980
$ east        : num 609000
$ top          : num 1
$ bottom      : num 0
$ nsres        : num 30
$ nsres3      : num 30
$ ewres        : num 30
$ ewres3      : num 30
$ tbres        : num 1
$ rows        : int 477
$ rows3        : int 477
$ cols        : int 634
$ cols3        : int 634
$ depths      : int 1
$ proj4        : chr "+proj=utm +zone=13 +a=6378206.4 +rf=294.9786982 +no_defs +nadgrids=/home/rsb/topics/grass61/grass-6.1.cvs/etc/nad/conus"
</pre>
 
 
> '''summary(spear)'''
<pre>
Object of class SpatialGridDataFrame
Coordinates:
              min    max
coords.x1  589980  609000
coords.x2 4913700 4928010
Is projected: TRUE
proj4string : [+proj=utm +zone=13 +a=6378206.4 +rf=294.9786982 +no_defs +nadgrids=/home/rsb/topics/grass61/grass-6.1.cvs/etc/nad/conus]
Number of points: 2
Grid attributes:
  cellcentre.offset cellsize cells.dim
1            589995      30      634
2          4913715      30      477
Data attributes:
      geology      elevation.dem 
sandstone:74959  Min.  : 1066 
limestone:61355  1st Qu.: 1200 
shale    :46423  Median : 1316 
sand    :36561  Mean  : 1354 
igneous  :36534  3rd Qu.: 1488 
(Other)  :37636  Max.  : 1840 
NA's    : 8950  NA's  :10101 
</pre>
 
==== Summarizing data ====
We can create a table of cell counts:
> '''table(spear$geology)'''
{| class="wikitable" border="1"
!metamorphic
!transition
!igneous
!sandstone
!limestone
!shale
!sandy shale
!claysand
!sand
|-
|11693
|142
|36534
|74959
|61355
|46423
|11266
|14535
|36561
|}
 
And compare with the equivalent GRASS module:
> '''execGRASS("r.stats", flags=c("c", "l"), parameters=list(input="geology"), ignore.stderr=TRUE)'''
<pre>
1 metamorphic 11693
2 transition 142
3 igneous 36534
4 sandstone 74959
5 limestone 61355
6 shale 46423
7 sandy shale 11266
8 claysand 14535
9 sand 36561
* no data 8950
</pre>
 
 
Create a box plot of geologic types at different elevations:
> '''boxplot(spear$elevation.dem ~ spear$geology, medlwd = 1)'''
 
[[Image:R_stats_boxplot_geo.png|center]]
 
==== Exporting data back to GRASS ====
Finally, a SpatialGridDataFrame object is written back to a GRASS raster map:
 
First prepare some data:  (square root of elevation)
> spear$sqdem <- sqrt(spear$elevation.dem)
 
 
Export data from ''R'' back into a GRASS raster map:
> writeRAST6(spear, "sqdemSP", zcol="sqdem", ignore.stderr=TRUE)
 
 
Check that it imported into GRASS ok:
> '''execGRASS("r.info", parameters=list(map="sqdemSP"))'''
 
<pre>
+----------------------------------------------------------------------------+
| Layer:    sqdemSP                        Date: Sun May 14 21:59:26 2006    |
| Mapset:  rsb                            Login of Creator: rsb            |
| Location: spearfish57                                                      |
| DataBase: /home/rsb/topics/grassdata                                      |
| Title:    ( sqdemSP )                                                    |
|----------------------------------------------------------------------------|
|                                                                            |
|  Type of Map:  raster              Number of Categories: 255              |
|  Data Type:    FCELL                                                      |
|  Rows:        477                                                        |
|  Columns:      634                                                        |
|  Total Cells:  302418                                                    |
|        Projection: UTM (zone 13)                                          |
|            N:    4928010    S:    4913700  Res:    30                    |
|            E:    609000    W:    589980  Res:    30                    |
|  Range of data:    min =  32.649654 max = 42.895222                      |
|                                                                            |
|  Data Source:                                                            |
|                                                                            |
|                                                                            |
|                                                                            |
|  Data Description:                                                        |
|    generated by r.in.gdal                                                  |
|                                                                            |
|                                                                            |
+----------------------------------------------------------------------------+
</pre>
 
=== Calling GRASS functionality in R batch job ===
 
To call GRASS functionality within a R batch job, use the initGRASS() function to define the GRASS settings:
 
    library(spgrass6)
   
    # initialisation and the use of spearfish60 data
    initGRASS(gisBase = "/usr/local/grass-6.4.1", home = tempdir(),
              gisDbase = "/home/neteler/grassdata/",
              location = "spearfish60", mapset = "user1", SG="elevation.dem",
              override = TRUE)
   
    system("g.region -d")
    # verify
    gmeta6()
   
    spear <- readRAST6(c("geology", "elevation.dem"),
              cat=c(TRUE, FALSE), ignore.stderr=TRUE,
              plugin=NULL)
   
    summary(spear$geology)


Run this script with
==== Older (but still useful) links ====


    R CMD BATCH batch.R
* [https://web.archive.org/web/20090623093535/http://grass.osgeo.org/statsgrass/grass_geostats.html Using GRASS GIS 6 and R]
* [http://grassold.osgeo.org/statsgrass/r_and_dbms.html Connecting R to RDBMS]
* R. Bivand, 2007: [http://spatial.nhh.no/R/etc/FBK07 Interfacing R and OSGeo projects: status and perspectives]: Presentation with slides and scripts.
* M. Neteler and M. Metz, 2011: ''Introduction to GRASS GIS''. GEOSTAT 2011 Landau. [http://geostat-course.org/Topic_NetelerMetz_2011 Download workshop material] (includes a R session)


The result is (shorted here):
=== Note for users of the legacy GRASS GIS 6 ===


    cat batch.Rout
If you are still using GRASS GIS 6, see [[R_statistics/spgrass6]] for instructions and examples of using GRASS GIS in conjunction with R. However, you are strongly encouraged to upgrade to GRASS GIS 7. Not only does it offer many improvements and new functionalities (see this [https://trac.osgeo.org/grass/wiki/Grass7/NewFeatures overview of New features]), it also provides a smoother R-GRASS integration on the Windows platform.
   
    R version 2.10.0 (2009-10-26)
    Copyright (C) 2009 The R Foundation for Statistical Computing
    ISBN 3-900051-07-0
    ...
    > library(spgrass6)
    Loading required package: sp
    Loading required package: rgdal
    Geospatial Data Abstraction Library extensions to R successfully loaded
    Loaded GDAL runtime: GDAL 1.7.2, released 2010/04/23
    Path to GDAL shared files: /usr/local/share/gdal
    Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009
    Path to PROJ.4 shared files: (autodetected)
    Loading required package: XML
    GRASS GIS interface loaded with GRASS version: (GRASS not running)
    >
    > # initialisation and the use of spearfish60 data
    > initGRASS(gisBase = "/usr/local/grass-6.4.1", home = tempdir(), gisDbase = "/home/neteler/grassdata/",
    +          location = "spearfish60", mapset = "user1", SG="elevation.dem", override = TRUE)
    gisdbase    /home/neteler/grassdata/
    location    spearfish60
    mapset      user1
    rows        477
    columns    634
    north      4928010
    south      4913700
    west        589980
    east        609000
    nsres      30
    ewres      30
    projection  +proj=utm +zone=13 +a=6378206.4 +rf=294.9786982 +no_defs
    +nadgrids=/usr/local/grass-6.4.1/etc/nad/conus +to_meter=1.0
    Warning messages:
    1: In dir.create(gisDbase) : '/home/neteler/grassdata' already exists
    2: In dir.create(loc_path) :
      '/home/neteler/grassdata//spearfish60' already exists
    >
    > system("g.region -d")
    > # verify
    > gmeta6()
    gisdbase    /home/neteler/grassdata/
    location    spearfish60
    mapset      user1
    rows        477
    columns    634
    north      4928010
    ...
    >
    > spear <- readRAST6(c("geology", "elevation.dem"),
    +          cat=c(TRUE, FALSE), ignore.stderr=TRUE,
    +          plugin=NULL)
    >
    > summary(spear$geology)
    metamorphic  transition    igneous  sandstone  limestone      shale
          11693        142      36534      74959      61355      46423
    sandy shale    claysand        sand        NA's
          11266      14535      36561        8950
    >
    >
    > proc.time()
      user  system elapsed
      2.891  0.492  3.412


 
About 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 a 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.
=== GRASS Modules ===
 
==== v.krige ====
 
{{cmd|v.krige|version=70}} is a GRASS python script which performs kriging operations in the GRASS environment, using R functions for the back-end interpolation. It is present in GRASS 6.5svn, and further developed in GRASS 7svn. It requires a number of dependencies: '''python-rpy2''' (''needs to be "Rpy2", "Rpy" will not do''), then the following R-CRAN packages:
* gstat, spgrass6 (as above)
* maptools
install.packages("maptools")
* automap (optional), with gpclib (or rgeos)
install.packages("automap")
install.packages("rgeos")
 
 
(''merge further info from man page here'')
 
=== Getting Support ===
 
* Primary support for ''R'' + GRASS and the ''spgrass6'' package is through the [http://lists.osgeo.org/mailman/listinfo/grass-stats grass-stats] mailing list.
 
=== See also ===
 
* R. Bivand, 2007: [http://spatial.nhh.no/R/etc/FBK07 Interfacing R and OSGeo projects: status and perspectives] (Presentation with slides and scripts)
 
* 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
 
* [http://www.r-project.org R-Statistics homepage]
 
* [http://r-spatial.sourceforge.net/ R-spatial main web page]
 
* [http://geodacenter.asu.edu/r-spatial-projects R Spatial Projects at ASU]
 
* 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.
 
* [http://r-spatial.sourceforge.net/ Spatial data in R] (<code>sp</code>) 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 <code>sp</code>, such as <code>maptools</code>, <code>rgdal</code>, <code>splancs</code>, '''<code>spgrass6</code>''', <code>gstat</code>, <code>spgwr</code> and many others.
 
* [http://rpy.sourceforge.net/ RPy] - Python interface to the R Programming Language
 
=== Workshop material ===
 
* M. Neteler and M. Metz, 2011: ''Introduction to GRASS GIS''. GEOSTAT 2011 Landau. [http://geostat-course.org/Topic_NetelerMetz_2011 Download workshop material] '''(includes a R session)'''
 
=== Articles ===
 
* [http://grass.osgeo.org/newsletter/grassnews3.html GRASS News vol.3], June 2005 (R. Bivand. Interfacing GRASS 6 and R. ''GRASS Newsletter'', 3:11-16, June 2005. ISSN 1614-8746).
* [http://www.osgeo.org/journal OSGeo Journal] vol. 1 May 2007 (R. Bivand. Using the R— GRASS interface. ''OSGeo Journal'', 1:31-33, May 2007. ISSN 1614-8746).
* [http://www.grassbook.org GRASS Book, last chapter]


[[Category:Installation]]
[[Category:Installation]]

Latest revision as of 14:16, 24 May 2021

Overview

High quality statistic analysis in GRASS GIS is possible thanks to an interface to one of the most powerful statistics analysis package around: R (http://www.r-project.org). This R ←→ GRASS GIS interface is provided by the rgrass7 R addon package. The possibility to directly interact with R strongly increases the statistical and geospatial analysis capabilities of GRASS. See CRAN Task View: Analysis of Spatial Data for an overview of the R packages and functions that can be used for reading, visualizing, and analyzing spatial data.

R can be used in conjunction with GRASS GIS in different ways:

  • Running R 'on top of' GRASS, transferring GRASS data to R to run statistical functions on the imported data as R objects in memory, and possibly transfer the results back to GRASS. GRASS raster and vector data can be imported in R using the readVECT() and readRAST() function provided by rgrass7. Similarly, results can be written back using the writeRAST() and writeVECT() functions.
  • Using R as a scripting language in GRASS. GRASS functions can be run from R using the execGRASS() function in the rgrass7 package. R scripting tools, such as powerful string-processing tools and functions for manipulating file names, can be used to 'glue' different functions and tools together.
  • Using GRASS GIS as a geospatial library in R. GRASS GIS can be used to extent the geospatial capability of R by: (1) offering an extensive and robust set of geospatial tools, and (2) provides a way to work with very large (larger-than-memory) spatial data sets. See Using 'GRASS GIS functionality within a R session' below for more information.

In practice, one will often combine different approaches, with scripts running GRASS functions, importing resulting layers in R for further analysis and visualization, and creating new layers in R that are imported back in GRASS.

Current State

R Windows binary packages distributed by the Comprehensive R Archive Network (CRAN) using OSGeo software (chiefly PROJ, GDAL and GEOS) use custom built binaries compatible with the build train used by R and are static linked; at present both 32-bit and 64-bit binaries are deployed. CRAN packages for macOS are also static linked to custom built binaries, again using the build train used by R. Static linkage is used to avoid having to deploy a package manager for external software on which R packages depend, since CRAN as a package manager already supports over 15000 packages with three binary versions (devel, release, old release) for Windows and macOS.

Installation

Installation of R core software

(see also R statistics/Installation)

Note: the ...-devel packages are needed if you want to install extra packages incl. rgrass7 on your computer.

Fedora:

 sudo dnf install R-core
 # further packages needed in order to locally compile "rgrass7"
 # F29
 sudo dnf install proj-epsg proj-nad proj-devel gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel udunits2-devel
 # F33+
 sudo dnf install proj-devel proj-datumgrid proj-datumgrid-europe proj-datumgrid-north-america proj-datumgrid-world proj-datumgrid-oceania gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel udunits2-devel

Ubuntu:

 sudo apt-get install r-base
 # further packages needed in order to locally compile "rgrass7"
 sudo apt-get install proj-bin proj-data libproj-dev libgdal-dev libsqlite3-dev libxml2-dev r-base-dev

Installation of the rgrass7 package

To install the R package rgrass7 on newer versions of R, you simply start R and install the package directly with:

install.packages("rgrass7", dependencies = TRUE)

This will install rgrass7 and all its dependencies. To use the package, you first need to load it:

library("rgrass7")

If you are using Rstudio, you can install the rgrass7 package in the usual way (tool → packages). For further instructions and for trouble shooting, see R_statistics/Installation

How to use

You can use Using R in conjunction with GRASS GIS in two different ways: (1) run R within a GRASS GIS session, and (2) run GRASS GIS within a R session.

Using R within a GRASS GIS session

If you are primarily a GIS user who wants to run e.g., some statistical tests not available in GRASS, you probably want to run R from within a GRASS GIS session. To do so, first start GRASS GIS and then start R (or RStudio) from the GRASS GIS command line. For more information and examples, see R_statistics/rgrass7.

Using GRASS GIS functionality within a R session

If you are primarily a R user, who wants to take advantage of the advanced geospatial functions in GRASS, you probably want to use GRASS GIS within a R session. To connect to a GRASS GIS database from within R (or Rstudio), see the instructions on R_statistics/rgrass7. If you are a first time GRASS GIS User, you may want to check out the information for first time users first.

Examples

Getting help

Manual pages

If you are in R and have loaded the rgrass7 package, you can get the help page of rgrass7 by typing:

?rgrass7

Similarly, to get help for a specific function, e.g., for the function readRAST(), you type:

?readRAST

You can also start the R help page in your browser:

help.start()

Now, to get the information about the package, select the Packages entry and then rgrass7.

Support from the community

Primary support for R + GRASS and the rgrass7 package is through the grass-stats mailing list.

Useful links

R

Related

Articles & books

Older (but still useful) links

Note for users of the legacy GRASS GIS 6

If you are still using GRASS GIS 6, see R_statistics/spgrass6 for instructions and examples of using GRASS GIS in conjunction with R. However, you are strongly encouraged to upgrade to GRASS GIS 7. Not only does it offer many improvements and new functionalities (see this overview of New features), it also provides a smoother R-GRASS integration on the Windows platform.

About 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 a 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.