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High quality statistic analysis in GRASS GIS is possible thanks to an interface to the most powerful statistics analysis package around: '''''R''''' (http://www.r-project.org).
{{toc|right}}


Support for:
=== Overview ===
* The [http://cran.r-project.org/web/packages/spgrass6/ '''spgrass6'''] ''R'' addon package provides a convenient R ←→ GRASS GIS 6 interface
* The [http://cran.r-project.org/web/packages/rgrass7/ '''rgrass7'''] ''R'' addon package provides a convenient R ←→ GRASS GIS 7 interface


Using R in GRASS GIS directly can 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.


=== Installation ===
* ''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.


See [[R_statistics/Installation]]
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.


===== Open tickets =====
=== Installation ===


* 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)
==== Installation of R core software ====


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


Start the ''R'' help browser:
Fedora:
help.start()
  sudo dnf install R-core
  # further packages needed in order to locally compile "rgrass7"
  sudo dnf install proj-epsg proj-nad proj-devel gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel


* Select '''Packages''' and then '''spgrass6'''.
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


=== Running ===
==== Installation of the rgrass7 package  ====
: ''by Roger Bivand''
To install the R package '''rgrass7''' on newer versions of R, you simply start R and install the package directly with:


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:
<source lang="rsplus">
install.packages("rgrass7", dependencies = TRUE)
</source>


==== Startup ====
This will install '''''rgrass7''''' and all its dependencies. To use the package, you first need to load it:
* 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
<source lang="rsplus">
GRASS> g.region -d
library("rgrass7")
</source>


Launch R from the GRASS prompt
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]]
GRASS> R


Load the ''spgrass6'' library:
=== How to use ===
> library(spgrass6)


Get the GRASS environment (mapset, region, map projection, etc.); you can display the metadata for your location by printing G:
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.
> G <- gmeta6()


==== Listing of existing maps ====
==== Using R within a GRASS GIS session ====


List available vector maps:
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]].
execGRASS("g.list", parameters = list(type = "vect"))


List selected vector maps (wildcard):
==== Using GRASS GIS functionality within a R session ====
execGRASS("g.list", parameters = list(type = "vect", pattern = "precip*"))


Save selected vector maps into R vector:
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.
my_vmaps <- execGRASS("g.list", parameters = list(type = "vect", pattern = "precip*"))
attributes(my_vmaps)
attributes(my_vmaps)$resOut


List available raster maps:
=== Examples ===
execGRASS("g.list", parameters = list(type = "rast"))


List selected raster maps (wildcard):
* [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.
execGRASS("g.list", parameters = list(type = "rast", pattern = "lsat7_2000*"))
* 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.


==== Reading in data ====
=== Getting help ===
Read in two raster maps (Spearfish sample dataset):
> spear <- readRAST6(c("geology", "elevation.dem"),
            cat=c(TRUE, FALSE), ignore.stderr=TRUE,
            plugin=NULL)


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


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:
<source lang="rsplus">
> image(spear, attr = 2, col = terrain.colors(20))
?rgrass7
</source>


Add a title to the plot:
Similarly, to get help for a specific function, e.g., for the function ''readRAST()'', you type:
> title("Spearfish elevation")


[[Image:R_stats_elev.png|center]]
<source lang="rsplus">
?readRAST
</source>


In addition, we can show what is going on inside the objects read into R:
You can also start the ''R'' help page in your browser:
> '''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>


<source lang="rsplus">
help.start()
</source>


> '''summary(spear)'''
Now, to get the information about the package, select the '''Packages''' entry and then '''rgrass7'''.
<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 ====
==== Support from the community ====
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:
Primary support for ''R'' + GRASS and the ''rgrass7'' package is through the [http://lists.osgeo.org/mailman/listinfo/grass-stats grass-stats] mailing list.
> '''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>


=== Useful links ===


Create a box plot of geologic types at different elevations:
==== R ====
> '''boxplot(spear$elevation.dem ~ spear$geology, medlwd = 1)'''


[[Image:R_stats_boxplot_geo.png|center]]
* [http://r-spatial.org/ R][http://r-spatial.org/ -spatial main web page]
* [https://cran.r-project.org/web/views/Spatial.html CRAN Task View: Analysis of Spatial Data]
* [https://cengel.github.io/rspatial/2_spDataTypes.nb.html Introduction to Spatial Data Types in R]
* [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)


==== Exporting data back to GRASS ====
==== Related ====
Finally, a SpatialGridDataFrame object is written back to a GRASS raster map:


First prepare some data: (square root of elevation)
* [https://rpy2.bitbucket.io/ Python interface to the R Programming Language]: can be used to run R in GRASS Python scripts.
> spear$sqdem <- sqrt(spear$elevation.dem)


==== Articles & books ====


Export data from ''R'' back into a GRASS raster map:
* 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]
> writeRAST6(spear, "sqdemSP", zcol="sqdem", ignore.stderr=TRUE)
* [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
 
* [http://www.grassbook.org/ GRASS Book], see last chapter
 
* [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).
Check that it imported into GRASS ok:
* [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)
> '''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
 
    R CMD BATCH batch.R
 
The result is (shorted here):
 
    cat batch.Rout
   
    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
 
 
=== 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'' unless it is rpy 2.x), then the following R-CRAN packages:
* gstat, spgrass6 (as above)
install.packages(c("gstat","spgrass6"))
* maptools
install.packages("maptools")
* automap (optional), with gpclib (or rgeos)
install.packages("automap")
install.packages("rgeos")
 
=== 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://grassold.osgeo.org/statsgrass/grass6_r_interface.html
 
* Connecting R to RDBMS: http://grassold.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
==== Older (but still useful) links ====


=== Workshop material ===
* [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)


* 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)'''
=== Note for users of the legacy GRASS GIS 6 ===


=== Articles ===
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.


* [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).
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.
* [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]]

Revision as of 14:31, 13 February 2018

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.

Installation

Installation of R core software

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"
 sudo dnf install proj-epsg proj-nad proj-devel gdal-devel sqlite-devel xml2 libxml2-devel R-core-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.

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