R statistics: Difference between revisions

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=== Overview ===
=== 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/ https://www.r-project.org]). This R ←→ GRASS GIS interface is provided by the [https://cran.r-project.org/package=rgrass rgrass] ''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.   
High quality statistical analyses in GRASS GIS are possible thanks to an interface to one of the most powerful statistical analysis packages around: '''''R''''' ([http://www.r-project.org/ https://www.r-project.org]). This R ←→ GRASS GIS interface is provided by the [https://cran.r-project.org/package=rgrass rgrass] ''R'' package. The possibility to directly interact with R strongly increases the statistical and geospatial analysis capabilities of GRASS GIS. 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.   


R can be used in conjunction with GRASS GIS in different ways:
R can be used in combination 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 [https://search.r-project.org/CRAN/refmans/rgrass/html/readVECT.html readVECT()] and [https://search.r-project.org/CRAN/refmans/rgrass/html/readRAST.html readRAST()] function provided by [http://cran.r-project.org/web/packages/rgrass/ rgrass]. Similarly, results can be written back using the [https://search.r-project.org/CRAN/refmans/rgrass/html/readRAST.html writeRAST()] and [https://search.r-project.org/CRAN/refmans/rgrass/html/readVECT.html writeVECT()] functions.
* ''Running R 'on top of' GRASS'': which involves transferring GRASS data to R to run statistical functions on the imported data, and possibly write the results back to GRASS. GRASS raster and vector data can be imported in R using the [https://search.r-project.org/CRAN/refmans/rgrass/html/readVECT.html read_VECT()] and [https://search.r-project.org/CRAN/refmans/rgrass/html/readRAST.html read_RAST()] functions provided by [http://cran.r-project.org/web/packages/rgrass/ rgrass]. Similarly, results can be written back using the [https://search.r-project.org/CRAN/refmans/rgrass/html/readRAST.html write_RAST()] and [https://search.r-project.org/CRAN/refmans/rgrass/html/readVECT.html write_VECT()] functions. When we read GRASS maps into R, they become R objects stored in memory, more specifically terra objects, i.e., SpatRaster and SpatVector objects that can then be coerced to other types of R spatial objects.
* ''Using R as a scripting language in GRASS''. GRASS functions can be run from R using the [https://search.r-project.org/CRAN/refmans/rgrass/html/execGRASS.html execGRASS()] function in the ''rgrass'' 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 R as a scripting language in GRASS'': GRASS functions can be run from R using the [https://search.r-project.org/CRAN/refmans/rgrass/html/execGRASS.html execGRASS()] function in the ''rgrass'' package. R scripting tools, such as powerful string-processing tools, 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.
* ''Using GRASS GIS as a geospatial library in R'': GRASS GIS can be used to extend the geospatial capabilities of R by: (1) offering an extensive and robust set of geospatial tools, and (2) providing a way to work with very large (larger-than-memory) spatial data sets. See Using 'GRASS GIS functionality within an 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.
In practice, one will often combine different approaches, with scripts running GRASS functions, reading resulting maps into R for further analysis, modeling and visualization, and creating new layers in R that are imported back into GRASS.


=== Current State ===
=== Current State ===
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Fedora:
Fedora:
   sudo dnf install R-core
   sudo dnf install R
   # further packages needed in order to locally compile "rgrass"
   # further packages needed in order to locally compile "rgrass"
   sudo dnf install proj-devel proj-data gdal-devel sqlite-devel xml2 libxml2-devel R-core-devel udunits2-devel
   sudo dnf install proj-devel proj-data gdal-devel sqlite-devel xml2 libxml2-devel udunits2-devel


Ubuntu:
Ubuntu:
Line 45: Line 45:


<source lang="rsplus">
<source lang="rsplus">
library("rgrass")
library(rgrass)
</source>
</source>


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


=== How to use ===
=== 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 ====
==== Using R within a GRASS GIS session ====
Line 58: Line 56:
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/rgrass]].  
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/rgrass]].  


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


=== Examples ===
=== Examples ===
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* [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-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.
* [https://tutorials.ecodiv.earth/toc/grass-r-gbif.html Use R to obtain gbif data] and import it in a GRASS GIS database.
* [https://veroandreo.github.io/grass_ncsu_2023/studio_index.html Using Satellite Data for Species Distribution Modeling with GRASS GIS and R]: Workshop at NCSU, April 2023.


=== Getting help ===
=== Getting help ===


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


<source lang="rsplus">
<source lang="rsplus">
Line 78: Line 77:
</source>
</source>


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


<source lang="rsplus">
<source lang="rsplus">
?readRAST
?read_RAST
</source>
</source>


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==== R ====
==== R ====


* [https://r-spatial.org/ R][https://r-spatial.org/ -spatial main web page]
* [https://r-spatial.org/ R][https://r-spatial.org/ r-spatial main web page]
* [https://cran.r-project.org/web/views/Spatial.html CRAN Task View: Analysis of Spatial Data]
* [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://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)
* [https://blog.dominodatalab.com/applied-spatial-data-science-with-r/ Applied Spatial Data Science with R] (blog post with examples)
* [https://rsbivand.github.io/rgrass/articles/coerce.html Coercion between object formats] (rgrass vignette)
* [https://rsbivand.github.io/rgrass/articles/use.html Use of GRASS interface] (rgrass vignette)
* [https://rsbivand.github.io/foss4g_2022/modernizing_220822.html Modernizing the R-GRASS interface: confronting barn-raised OSGeo libraries and the evolving R.*spatial package ecosystem] (Workshop at FOSS4G 2022, Florence)


==== Related ====
==== Related ====

Latest revision as of 19:58, 19 April 2023

Overview

High quality statistical analyses in GRASS GIS are possible thanks to an interface to one of the most powerful statistical analysis packages around: R (https://www.r-project.org). This R ←→ GRASS GIS interface is provided by the rgrass R package. The possibility to directly interact with R strongly increases the statistical and geospatial analysis capabilities of GRASS GIS. 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 combination with GRASS GIS in different ways:

  • Running R 'on top of' GRASS: which involves transferring GRASS data to R to run statistical functions on the imported data, and possibly write the results back to GRASS. GRASS raster and vector data can be imported in R using the read_VECT() and read_RAST() functions provided by rgrass. Similarly, results can be written back using the write_RAST() and write_VECT() functions. When we read GRASS maps into R, they become R objects stored in memory, more specifically terra objects, i.e., SpatRaster and SpatVector objects that can then be coerced to other types of R spatial objects.
  • Using R as a scripting language in GRASS: GRASS functions can be run from R using the execGRASS() function in the rgrass package. R scripting tools, such as powerful string-processing tools, 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 extend the geospatial capabilities of R by: (1) offering an extensive and robust set of geospatial tools, and (2) providing a way to work with very large (larger-than-memory) spatial data sets. See Using 'GRASS GIS functionality within an R session' below for more information.

In practice, one will often combine different approaches, with scripts running GRASS functions, reading resulting maps into R for further analysis, modeling and visualization, and creating new layers in R that are imported back into 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. rgrass on your computer.

Fedora: 

 sudo dnf install R
 # further packages needed in order to locally compile "rgrass"
 sudo dnf install proj-devel proj-data gdal-devel sqlite-devel xml2 libxml2-devel udunits2-devel

Ubuntu: 

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

Installation of the rgrass package

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

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

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

library(rgrass)

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

How to use

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/rgrass.

Using GRASS GIS functionality within an R session

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

Examples

Getting help

Manual pages

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

?rgrass

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

?read_RAST

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 rgrass.

Support from the community

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

Useful links

R

Related

Articles & books

Older (but still useful) links

Retrieved from "https://grasswiki.osgeo.org/w/index.php?title=R_statistics&oldid=26882"