R statistics/rgrass

This page refers to the usage of R within a GRASS GIS 7 session and the use of GRASS GIS 7 within an R session. (see also R_statistics/spgrass6)

R within GRASS

Startup

• First start a GRASS GIS session. Then, at the GRASS command line start R (for a 'rstudio' session, see below)

In this example we will use the North Carolina sample dataset.

Reset the region settings to the defaults

GRASS 7.0.1svn (nc_spm_08_grass7):~ > g.region -d

Launch R from the GRASS prompt

GRASS 7.0.1svn (nc_spm_08_grass7):~ > R

Load the rgrass7 library:

> library(rgrass7)

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

> G <- gmeta()

gisdbase    /home/neteler/grassdata 
location    nc_spm_08_grass7 
mapset      user1 
rows        620 
columns     1630 
north       320000 
south       10000 
west        120000 
east        935000 
nsres       500 
ewres       500 
projection  +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1 

Listing of existing maps

List available vector maps:

> execGRASS("g.list", parameters = list(type = "vector"))

List selected vector maps (wildcard):

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

Save selected vector maps into R vector:

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

List available raster maps:

> execGRASS("g.list", parameters = list(type = "raster"))

List selected raster maps (wildcard):

> execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))

Reading in data

Read in two raster maps (North Carolina sample dataset):

> ncdata <- readRAST(c("geology_30m", "elevation"), 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, in this case a SpatialGridDataFrame object filled with data from two North Carolina layers. Here is a plot of the elevation data:

> image(ncdata, attr = 2, col = terrain.colors(20))

Add a title to the plot:

> title("North Carolina elevation")

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

> str(G)

List of 26
 $ DEBUG        : chr "0"
 $ LOCATION_NAME: chr "nc_spm_08_grass7"
 $ GISDBASE     : chr "/home/veroandreo/grassdata"
 $ MAPSET       : chr "PERMANENT"
 $ GUI          : chr "wxpython"
 $ projection   : chr "99"
 $ zone         : chr "0"
 $ n            : num 228500
 $ s            : num 215000
 $ w            : num 630000
 $ e            : num 645000
 $ t            : num 1
 $ b            : num 0
 $ nsres        : num 27.5
 $ nsres3       : num 10
 $ ewres        : num 37.5
 $ ewres3       : num 10
 $ tbres        : num 1
 $ rows         : int 491
 $ rows3        : int 1350
 $ cols         : int 400
 $ cols3        : int 1500
 $ depths       : int 1
 $ cells        : chr "196400"
 $ cells3       : chr "2025000"
 $ proj4        : chr "+proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334 +lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137 +"| __truncated__
 - attr(*, "class")= chr "gmeta"

> summary(ncdata)

Object of class SpatialGridDataFrame
Coordinates:
        min    max
[1,] 630000 645000
[2,] 215000 228500
Is projected: TRUE 
proj4string :
[+proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1]
Grid attributes:
  cellcentre.offset cellsize cells.dim
1          630018.8 37.50000       400
2          215013.7 27.49491       491
Data attributes:
   geology_30m      elevation     
 CZfg_217:70381   Min.   : 55.92  
 CZig_270:66861   1st Qu.: 94.78  
 CZbg_405:24561   Median :108.88  
 CZlg_262:19287   Mean   :110.38  
 CZam_862: 6017   3rd Qu.:126.78  
 CZbg_910: 4351   Max.   :156.25  
 (Other) : 4942                   

Summarizing data

We can create a table of cell counts:

> table(ncdata$geology_30m)

CZfg_217	CZlg_262	CZig_270	CZbg_405	CZve_583	CZam_720	CZg_766	CZam_862	CZbg_910	Km_921	CZbg_945	CZam_946	CZam_948
70381	19287	66861	24561	2089	467	691	6017	4351	1211	1	398	85

And compare with the equivalent GRASS module:

> execGRASS("r.stats", flags=c("c", "l"), parameters=list(input="geology_30m"), ignore.stderr=TRUE)

217 CZfg 70381
262 CZlg 19287
270 CZig 66861
405 CZbg 24561
583 CZve 2089
720 CZam 467
766 CZg 691
862 CZam 6017
910 CZbg 4351
921 Km 1211
945 CZbg 1
946 CZam 398
948 CZam 85

Create a box plot of geologic types at different elevations:

> boxplot(ncdata$elevation ~ ncdata$geology_30m, 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)

> ncdata$sqdem <- sqrt(ncdata$elevation)

Export data from R back into a GRASS raster map:

> writeRAST(ncdata, "sqdemNC", zcol="sqdem", ignore.stderr=TRUE)

Check that it imported into GRASS ok:

> execGRASS("r.info", parameters=list(map="sqdemNC"))

 +----------------------------------------------------------------------------+
 | Map:      sqdemNC                        Date: Sun Jul 19 13:06:34 2015    |
 | Mapset:   PERMANENT                      Login of Creator: veroandreo      |
 | Location: nc_spm_08_grass7                                                 |
 | DataBase: /home/veroandreo/grassdata                                       |
 | Title:     ( sqdemNC )                                                     |
 | Timestamp: none                                                            |
 |----------------------------------------------------------------------------|
 |                                                                            |
 |   Type of Map:  raster               Number of Categories: 0               |
 |   Data Type:    DCELL                                                      |
 |   Rows:         491                                                        |
 |   Columns:      400                                                        |
 |   Total Cells:  196400                                                     |
 |        Projection: Lambert Conformal Conic                                 |
 |            N:     228500    S: 215000.0002   Res: 27.49490794              |
 |            E:     645000    W:     630000   Res:  37.5                     |
 |   Range of data:    min = 7.47818253045085  max = 12.5000787351036         |
 |                                                                            |
 |   Data Description:                                                        |
 |    generated by r.in.bin                                                   |
 |                                                                            |
 |   Comments:                                                                |
 |                                                                            |
 +----------------------------------------------------------------------------+

Calling RStudio from a GRASS GIS session

If you are most used to run R through RStudio, but still want to have all GRASS data available for performing any analyses without loosing the possibility of still using GRASS command line, you can run:

GRASS> rstudio &

or, if you already are working on a certain RStudio project:

GRASS> rstudio /path/to/project/folder/ &

Then, you load rgrass7 library in your RStudio project

> library(rgrass7) 

and you are ready to go.

GRASS within R

In the first place, find out the path to the GRASS GIS library. This can be easily done with the following command (still outside of R; or through a system() call inside of R):

 grass70 --config path

It may report something like:

 /home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu

To call GRASS GIS 7 functionality from R, start R and use the initGRASS() function to define the GRASS settings:

library(rgrass7)
    
# initialisation and the use of North Carolina sample dataset
initGRASS(gisBase = "/home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu", home = tempdir(), 
          gisDbase = "/home/veroandreo/grassdata/",
          location = "nc_spm_08_grass7", mapset = "user1", SG="elevation",
          override = TRUE)

# set computational region to default (facultative)
system("g.region -dp")
# verify metadata
gmeta()

# get two raster maps into R space
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))

# calculate object summaries
summary(ncdata$geology_30m)
 CZfg_217 CZlg_262 CZig_270 CZbg_405 CZve_583 CZam_720  CZg_766 CZam_862 
     292       78      277      102        8        1        2       25 
 CZbg_910   Km_921 CZam_946     NA's 
      18        5        2  1009790 

R in GRASS in batch mode

Run the following script with

R CMD BATCH batch.R

library(rgrass7)
# initialisation and the use of north carolina dataset
initGRASS(gisBase = "/home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu", 
          home = tempdir(), 
          gisDbase = "/home/veroandreo/grassdata/",
          location = "nc_spm_08_grass7", mapset = "user1", SG="elevation",
          override = TRUE)
# set region to default
system("g.region -dp")
# verify
gmeta()
# read data into R
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
# summary of geology map
summary(ncdata$geology_30m)
proc.time()

The result is (shorted here):

   cat batch.Rout
   
   R version 3.2.1 (2015-06-18) -- "World-Famous Astronaut"
   Copyright (C) 2015 The R Foundation for Statistical Computing
   Platform: x86_64-redhat-linux-gnu (64-bit)
   ...
   > library(rgrass7)
   Loading required package: sp
   Loading required package: XML
   GRASS GIS interface loaded with GRASS version: (GRASS not running)
   > # initialisation and the use of north carolina dataset
   > initGRASS(gisBase = "/home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu", home = tempdir(), 
   +           gisDbase = "/home/veroandreo/grassdata/",
   +           location = "nc_spm_08_grass7", mapset = "user1", SG="elevation",
   +           override = TRUE)
   gisdbase    /home/veroandreo/grassdata/ 
   location    nc_spm_08_grass7 
   mapset      user1 
   rows        620 
   columns     1630 
   north       320000 
   south       10000 
   west        120000 
   east        935000 
   nsres       500 
   ewres       500 
   projection  +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
   +lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
   +rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1 
   
   > system("g.region -dp")
   projection: 99 (Lambert Conformal Conic)
   zone:       0
   datum:      nad83
   ellipsoid:  a=6378137 es=0.006694380022900787
   north:      320000
   south:      10000
   west:       120000
   east:       935000
   nsres:      500
   ewres:      500
   rows:       620
   cols:       1630
   cells:      1010600
   > gmeta()
   gisdbase    /home/veroandreo/grassdata/ 
   location    nc_spm_08_grass7 
   mapset      user1 
   rows        620 
   columns     1630 
   north       320000 
   south       10000 
   ...
   > ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
   ...
   > summary(ncdata$geology_30m)
   CZfg_217 CZlg_262 CZig_270 CZbg_405 CZve_583 CZam_720  CZg_766 CZam_862 
        292       78      277      102        8        1        2       25 
   CZbg_910   Km_921 CZam_946     NA's 
         18        5        2  1009790 
   > proc.time()
      user  system elapsed 
     8.061   2.016  10.048 

Troubleshooting

Running out of disk space

Linux: A common issue is that /tmp/ is used for temporary files albeit being often a small partition. To change that to a larger directory, you may add to your $HOME/.bashrc the entry:

# change TMP directory of R (note: of course also another directory than suggested here is fine)
mkdir -p $HOME/tmp
export TMP=$HOME/tmp

The drawback is that on modern Linux systems the /tmp/ is a fast RAM disk (based on tempfs) while HOME directories are often on slower spinning disks (unless you have a SSD drive). At least you no longer run out of disk space easily.