Difference between revisions of "R statistics"
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''Note: the ...-devel packages are needed if you want to install extra packages incl. '''rgrass7''' on your computer.'' | ''Note: the ...-devel packages are needed if you want to install extra packages incl. '''rgrass7''' on your computer.'' |
Revision as of 08:42, 9 August 2020
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" sudo dnf install proj-epsg proj-nad proj-devel 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
- Temporal data processing wiki; a tutorial about time series processing with GRASS GIS and R.
- A short guide on how to get a GRASS function output in R.
- Importing GLCF MODIS woody plant cover in a GRASS GIS database using an R script.
- Use R to obtain gbif data and import it in a GRASS GIS database.
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
- R-spatial main web page
- CRAN Task View: Analysis of Spatial Data
- Introduction to Spatial Data Types in R
- R Classes and Methods for Spatial Data -
sp
package - Simple Features for R -
sf
package. - Applied Spatial Data Science with R (blog post with examples)
Related
- Python interface to the R Programming Language: can be used to run R in GRASS Python scripts.
Articles & books
- Neural Networks with GRASS and R DOI: 10.1016/j.ecolmodel.2006.03.015
- 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 Book, see last chapter
- OSGeo Journal vol. 1 May 2007 (R. Bivand. Using the R— GRASS interface. OSGeo Journal, 1:31-33, May 2007. ISSN 1614-8746).
- GRASS News vol.3, June 2005 (R. Bivand. Interfacing GRASS 6 and R. GRASS Newsletter, 3:11-16, June 2005. ISSN 1614-8746)
Older (but still useful) links
- Using GRASS GIS 6 and R
- Connecting R to RDBMS
- R. Bivand, 2007: 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. Download workshop material (includes a R session)
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.