R statistics: Difference between revisions

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(Installation moved to R_statistics/Installation)
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* 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.
* 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.
* 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.
* 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 ===
For the impatient just start it:
  > R
 
  #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
  #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://grassold.osgeo.org/statsgrass/grass_geostats.html (from 2001)


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

Revision as of 14:04, 19 July 2015

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).

Support for:

  • The spgrass6 R addon package provides a convenient R ←→ GRASS GIS 6 interface
  • The rgrass7 R addon package provides a convenient R ←→ GRASS GIS 7 interface

Using R in GRASS GIS directly can has two meanings:

  • 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.
  • 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

See R_statistics/Installation

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)

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()

Listing of existing maps

List available vector maps: 

execGRASS("g.list", parameters = list(type = "vect"))

List selected vector maps (wildcard): 

execGRASS("g.list", parameters = list(type = "vect", pattern = "precip*"))

Save selected vector maps into R vector: 

my_vmaps <- execGRASS("g.list", parameters = list(type = "vect", pattern = "precip*"))
attributes(my_vmaps)
attributes(my_vmaps)$resOut

List available raster maps: 

execGRASS("g.list", parameters = list(type = "rast"))

List selected raster maps (wildcard): 

execGRASS("g.list", parameters = list(type = "rast", pattern = "lsat7_2000*"))

Reading in data

Read in two raster maps (Spearfish sample dataset): 

> 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")

In addition, we can show what is going on inside the objects read into R: 

> str(G)

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"



> summary(spear)

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

Summarizing data

We can create a table of cell counts: 

> table(spear$geology)
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)

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


Create a box plot of geologic types at different elevations: 

> boxplot(spear$elevation.dem ~ spear$geology, medlwd = 1)

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"))

 +----------------------------------------------------------------------------+
 | 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                                                  |
 |                                                                            |
 |                                                                            |
 +----------------------------------------------------------------------------+

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

v.krige 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 grass-stats mailing list.

See also

  • Using R and GRASS with cygwin: It is possible to use Rterm inside the GRASS shell in cygwin, just as in Unix/Linux or OSX. You should not, however, start Rterm from a cygwin xterm, because Rterm is not expecting to be run in an xterm under Windows, and loses its input. If you use the regular cygwin bash shell, but need to start display windows, start X from within GRASS with startx &, and then start Rterm in the same cygwin shell, not in the xterm.
  • Spatial data in R (sp) is a R library that provides classes and methods for spatial data (points, lines, polygons, grids), and to new or existing spatial statistics R packages that use sp, depend on sp, or will become dependent on sp, such as maptools, rgdal, splancs, spgrass6, gstat, spgwr and many others.
  • RPy - Python interface to the R Programming Language

Workshop material

Articles

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