R statistics/rgrass: Difference between revisions

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Load the ''rgrass7'' library:
Load the ''rgrass7'' library:
> library(rgrass7)
<source lang="rsplus">
library(rgrass7)
</source>


If you plan to follow the example using the North Carolina sample dataset, load the ''rgdal'' library:
If you plan to follow the example using the North Carolina sample dataset, load the ''rgdal'' library:
> library(rgdal)
<source lang="rsplus">
library(rgdal)
</source>


Get the GRASS environment (mapset, region, map projection, etc.); you can display the metadata for your location by printing G:
Get the GRASS environment (mapset, region, map projection, etc.); you can display the metadata for your location by printing G:
> G <- gmeta()
<source lang="rsplus">
<pre>
G <- gmeta()
gisdbase    /home/neteler/grassdata  
gisdbase    /home/neteler/grassdata  
location    nc_spm_08_grass7  
location    nc_spm_08_grass7  
Line 58: Line 62:
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
+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  
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1  
</pre>
</source>


=== Listing of existing maps ===
=== Listing of existing maps ===


List available vector maps:
List available vector maps:
> execGRASS("g.list", parameters = list(type = "vector"))
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "vector"))
</source>


List selected vector maps (wildcard):
List selected vector maps (wildcard):
> execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
</source>


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


List available raster maps:
List available raster maps:
> execGRASS("g.list", parameters = list(type = "raster"))
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "raster"))
</source>


List selected raster maps (wildcard):
List selected raster maps (wildcard):
> execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))
</source>


=== Reading in data from GRASS ===
=== Reading in data from GRASS ===
Line 87: Line 101:
  # - geology_30m is a categorical map (i.e., it comes with classes)
  # - geology_30m is a categorical map (i.e., it comes with classes)
  # - elevation is a continuous surface
  # - elevation is a continuous surface
> ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
<source lang="rsplus">
 
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
</source>
(A warning may appear since in the "geology_30m" map two labels are not unique - as found in the original data.)
(A warning may appear since in the "geology_30m" map two labels are not unique - as found in the original data.)


We can verify the new R object:
We can verify the new R object:
> str(ncdata)
<source lang="rsplus">
Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots
str(ncdata)
  ..@ data      :'data.frame': 16616 obs. of  2 variables:
Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots
..@ data      :'data.frame': 16616 obs. of  2 variables:
</source>


and also check the data structure in more detail:
and also check the data structure in more detail:
> str(ncdata@data)
<source lang="rsplus">
'data.frame': 16616 obs. of  2 variables:
str(ncdata@data)
  $ geology_30m: Factor w/ 12 levels "CZfg_217","CZlg_262",..: NA NA NA NA NA NA NA NA NA NA ...
'data.frame': 16616 obs. of  2 variables:
  $ elevation  : num  NA NA NA NA NA NA NA NA NA NA ...
$ geology_30m: Factor w/ 12 levels "CZfg_217","CZlg_262",..: NA NA NA NA NA NA NA NA NA NA ...
 
$ elevation  : num  NA NA NA NA NA NA NA NA NA NA ...
</source>


The metadata are now 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:
The metadata are now 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, "elevation", col = terrain.colors(20))
<source lang="rsplus">
image(ncdata, "elevation", col = terrain.colors(20))
</source>


Add a title to the plot:
Add a title to the plot:
> title("North Carolina elevation")
<source lang="rsplus">
title("North Carolina elevation")
</source>


[[Image:ncdata.png|center]]
[[Image:ncdata.png|center]]


In addition, we can show what is going on inside the objects read into R:
In addition, we can show what is going on inside the objects read into R:
> str(G)
<source lang="rsplus">
<pre>
str(G)
List of 26
List of 26
  $ DEBUG        : chr "0"
  $ DEBUG        : chr "0"
Line 142: Line 164:
  $ 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__
  $ 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"
  - attr(*, "class")= chr "gmeta"
</pre>


> summary(ncdata)
summary(ncdata)
<pre>
Object of class SpatialGridDataFrame
Object of class SpatialGridDataFrame
Coordinates:
Coordinates:
Line 169: Line 189:
  CZbg_910: 4351  Max.  :156.25   
  CZbg_910: 4351  Max.  :156.25   
  (Other) : 4942                   
  (Other) : 4942                   
</pre>
</source>


==== Example 2 ====
==== Example 2 ====
Line 175: Line 195:
Here an example for a single map transfer from GRASS GIS to R:
Here an example for a single map transfer from GRASS GIS to R:


<source lang="bash">
<source lang="rsplus">
library(rgrass7)
library(rgrass7)
execGRASS("g.region", raster = "elevation", flags = "p")
execGRASS("g.region", raster = "elevation", flags = "p")
Line 186: Line 206:


We can create a table of cell counts:
We can create a table of cell counts:
> table(ncdata$geology_30m)
<source lang="rsplus">
table(ncdata$geology_30m)
</source>
{| class="wikitable" border="1"
{| class="wikitable" border="1"
!CZfg_217  
!CZfg_217  
Line 218: Line 240:


And compare with the equivalent GRASS module:
And compare with the equivalent GRASS module:
> execGRASS("r.stats", flags=c("c", "l"), parameters=list(input="geology_30m"), ignore.stderr=TRUE)
<source lang="rsplus">
execGRASS("r.stats", flags=c("c", "l"), parameters=list(input="geology_30m"), ignore.stderr=TRUE)
</source>
<pre>
<pre>
217 CZfg 70381
217 CZfg 70381
Line 236: Line 260:


Create a box plot of geologic types at different elevations:
Create a box plot of geologic types at different elevations:
> boxplot(ncdata$elevation ~ ncdata$geology_30m, medlwd = 1)
<source lang="rsplus">
boxplot(ncdata$elevation ~ ncdata$geology_30m, medlwd = 1)
</source>


[[Image:boxplot_geo.png|center]]
[[Image:boxplot_geo.png|center]]
Line 280: Line 306:


First prepare some data:  (square root of elevation)
First prepare some data:  (square root of elevation)
> ncdata$sqdem <- sqrt(ncdata$elevation)
<source lang="rsplus">
ncdata$sqdem <- sqrt(ncdata$elevation)
</source>


Export data from ''R'' back into a GRASS raster map:
Export data from ''R'' back into a GRASS raster map:
> writeRAST(ncdata, "sqdemNC", zcol="sqdem", ignore.stderr=TRUE)
<source lang="rsplus">
writeRAST(ncdata, "sqdemNC", zcol="sqdem", ignore.stderr=TRUE)
</source>


Check that it imported into GRASS ok:
Check that it imported into GRASS ok:
> execGRASS("r.info", parameters=list(map="sqdemNC"))
<source lang="rsplus">
execGRASS("r.info", parameters=list(map="sqdemNC"))
</source>


<pre>
<pre>
Line 321: Line 353:
any analyses without loosing the possibility of still using GRASS command line, you can run:
any analyses without loosing the possibility of still using GRASS command line, you can run:


<pre>
<source lang="bash">
GRASS> rstudio &
GRASS> rstudio &
</pre>
</source>


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


<pre>
<source lang="bash">
GRASS> rstudio /path/to/project/folder/ &
GRASS> rstudio /path/to/project/folder/ &
</pre>
</source>


Then, you load rgrass7 library in your RStudio project
Then, you load rgrass7 library in your RStudio project
 
<source lang="rsplus">
> library(rgrass7)  
library(rgrass7)
</source>


and you are ready to go.
and you are ready to go.

Revision as of 17:37, 15 September 2016

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)

Terminology

Using R in conjunction with GRASS GIS can have two meanings:

  • Using R within GRASS GIS session, i.e. you start R (or RStudio) from the GRASS GIS command line. You may like this variant if you are primarily a GIS user.
  • Using GRASS GIS within a R session, i.e. you connect to a GRASS GIS location/mapset from within R (or RStudio). You may like this variant if you are primarily a R user.


References: see "Overview" in R_statistics

Installation

See R_statistics/Installation

R within GRASS

Using R within GRASS GIS session, i.e. you start R (or RStudio) from the GRASS GIS command line.

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)

If you plan to follow the example using the North Carolina sample dataset, load the rgdal library: 

library(rgdal)

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 from GRASS

Example 1

Read in two GRASS raster maps (the maps "geology_30m" and "elevation" from the North Carolina sample data location) into the R current session as a new object "ncdata" (containing then two SpatialGridDataFrames as well as metadata): 

# the cat parameter indicates which raster values to be returned as factors
# - geology_30m is a categorical map (i.e., it comes with classes)
# - elevation is a continuous surface

ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))

(A warning may appear since in the "geology_30m" map two labels are not unique - as found in the original data.)

We can verify the new R object: 

str(ncdata)
Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots
 ..@ data       :'data.frame':	16616 obs. of  2 variables:

and also check the data structure in more detail: 

str(ncdata@data)
'data.frame':	16616 obs. of  2 variables:
 $ geology_30m: Factor w/ 12 levels "CZfg_217","CZlg_262",..: NA NA NA NA NA NA NA NA NA NA ...
 $ elevation  : num  NA NA NA NA NA NA NA NA NA NA ...

The metadata are now 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, "elevation", 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

Example 2

Here an example for a single map transfer from GRASS GIS to R: 

library(rgrass7)
execGRASS("g.region", raster = "elevation", flags = "p")
ncdata <- readRAST("elevation", cat=FALSE)
summary(ncdata)
spplot(ncdata, col = terrain.colors(20))

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)

Querying maps

Sometimes you may want to query GRASS GIS maps from your R session.

As an example, here the transfer of raster pixel values at the position of vector points: 

# set the computational region first to the raster map:
execGRASS("g.region", raster = "elev_state_500m", flags = "p")

# query raster map at vector points, transfer result into R
goutput <- execGRASS("r.what", map="elev_state_500m", points="geodetic_pts", separator=",", intern=TRUE)
str(goutput)
chr [1:29939] "571530.81289275,265739.968425953,,187.8082200648" ...

# parse it
con <- textConnection(goutput)
go1 <- read.csv(con, header=FALSE)
str(go1)
'data.frame':	29939 obs. of  4 variables:
 $ V1: num  571531 571359 571976 572391 573011 ...
 $ V2: num  265740 265987 267049 267513 269615 ...
 $ V3: logi  NA NA NA NA NA NA ...
 $ V4: Factor w/ 22738 levels "-0.0048115728",..: 6859 6642 6749 6411 6356 6904 7506 7224 6908 7167 ...
summary(go1)
       V1               V2            V3                V4       
 Min.   :121862   Min.   :  7991   Mode:logical   0      :  723  
 1st Qu.:462706   1st Qu.:162508   NA's:29939     *      :  293  
 Median :610328   Median :204502                  0.3048 :   47  
 Mean   :588514   Mean   :200038                  0.6096 :   44  
 3rd Qu.:717610   3rd Qu.:247633                  1.524  :   42  
 Max.   :946743   Max.   :327186                  0.9144 :   23  
                                                  (Other):28767

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:                                                                |
 |                                                                            |
 +----------------------------------------------------------------------------+

Using RStudio in 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.

RStudio used in GRASS GIS 7 session

GRASS within R

Using GRASS GIS within a R session, i.e. you connect to a GRASS GIS location/mapset from within R (or RStudio).

Startup

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): 

 # OSGeo4W users: nothing to do

 # Linux, Mac OSX users:
 grass70 --config path

It may report something like: 

 /usr/local/grass-7.0.1

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

## MS-Windows users:
library(rgrass7)
# initialisation and the use of North Carolina sample dataset
initGRASS(gisBase = "C:/OSGeo4W/apps/grass/grass-7.1.svn",
         gisDbase = "C:/Users/marissa/GRASSdata/",
         location = "nc_spm_08_grass7", mapset = "user1", SG="elevation")

## Linux, Mac OSX users:
library(rgrass7)
# initialisation and the use of North Carolina sample dataset
initGRASS(gisBase = "/usr/local/grass-7.0.1", home = tempdir(), 
          gisDbase = "/home/veroandreo/grassdata/",
          location = "nc_spm_08_grass7", mapset = "user1", SG="elevation")

Note: the optional SG parameter is a 'SpatialGrid' object to define the ‘DEFAULT_WIND’ of the temporary location. 

# set computational region to default (optional)
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.

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