R statistics/rgrass: Difference between revisions

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'''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]])
== Installation ==
 
See [[R_statistics/Installation]]


== Terminology ==
== Terminology ==


Using R in conjunction with GRASS GIS can have two meanings:
See "Overview" in [[R_statistics]]
 
* 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.
 
<!-- not quite clear to the casual user..:
Keep in mind:
* if you already have a GRASS location/mapset, use R inside GRASS and do not use initGRASS();
* if the GRASS location/mapset is only a throwaway one, use initGRASS() and run GRASS inside R.
-->
 
'''References''': see "Overview" in [[R_statistics]]
 
== Installation ==
 
See [[R_statistics/Installation]]


== R within GRASS ==
== R within GRASS ==


Using '''R within GRASS GIS session''', i.e. you start R (or RStudio) from the GRASS GIS command line.
In this example, we will use '''R within a GRASS GIS session''', i.e. start R (or RStudio) from the GRASS GIS terminal or command line interface.


=== Startup ===
=== Startup ===


* First start a GRASS GIS session. Then, at the GRASS command line start ''R'' (for a 'rstudio' session, see below)
* First start a GRASS GIS session. Then, at the GRASS command line start ''R'' (for an 'rstudio' session, see below)
: ''In this example we will use the [http://grass.osgeo.org/download/sample-data/ North Carolina sample dataset].''
: ''In this example we will use the [https://grass.osgeo.org/download/data/#NorthCarolinaDataset North Carolina sample dataset].''


Reset the region settings to the defaults
Reset the region settings to the defaults
<source lang="bash">
<source lang="bash">
GRASS 7.0.1svn (nc_spm_08_grass7):~ > g.region -d
GRASS (nc_spm_08_grass7):~ > g.region -d
</source>
</source>


Launch R from the GRASS prompt
Launch R from the GRASS prompt
<source lang="bash">
<source lang="bash">
GRASS 7.0.1svn (nc_spm_08_grass7):~ > R
GRASS (nc_spm_08_grass7):~ > R
</source>
</source>


Load the ''rgrass7'' library:
Load the ''rgrass'' library:
<source lang="rsplus">
<source lang="rsplus">
library(rgrass7)
library(rgrass)
</source>
 
If you plan to follow the example using the North Carolina sample dataset, load the ''rgdal'' library:
<source lang="rsplus">
library(rgdal)
</source>
</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:
<source lang="rsplus">
<source lang="rsplus">
G <- gmeta()
gmeta()
gisdbase    /home/neteler/grassdata  
gisdbase    /home/mneteler/grassdata  
location    nc_spm_08_grass7  
location    nc_spm_08_grass7  
mapset      user1  
mapset      user1  
Line 63: Line 45:
nsres      500  
nsres      500  
ewres      500  
ewres      500  
projection  +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
projection:
+lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
  PROJCRS["NAD83(HARN) / North Carolina",
+rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1
    BASEGEOGCRS["NAD83(HARN)",
        DATUM["NAD83 (High Accuracy Reference Network)",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4152]],
    CONVERSION["SPCS83 North Carolina zone (meters)",
        METHOD["Lambert Conic Conformal (2SP)",
            ID["EPSG",9802]],
        PARAMETER["Latitude of false origin",33.75,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8821]],
        PARAMETER["Longitude of false origin",-79,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8822]],
        PARAMETER["Latitude of 1st standard parallel",36.1666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8823]],
        PARAMETER["Latitude of 2nd standard parallel",34.3333333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8824]],
        PARAMETER["Easting at false origin",609601.22,
            LENGTHUNIT["metre",1],
            ID["EPSG",8826]],
        PARAMETER["Northing at false origin",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8827]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["United States (USA) - North Carolina - counties of Alamance; Alexander; Alleghany; Anson; Ashe; Avery; Beaufort; Bertie; Bladen; Brunswick; Buncombe; Burke; Cabarrus; Caldwell; Camden; Carteret; Caswell; Catawba; Chatham; Cherokee; Chowan; Clay; Cleveland; Columbus; Craven; Cumberland; Currituck; Dare; Davidson; Davie; Duplin; Durham; Edgecombe; Forsyth; Franklin; Gaston; Gates; Graham; Granville; Greene; Guilford; Halifax; Harnett; Haywood; Henderson; Hertford; Hoke; Hyde; Iredell; Jackson; Johnston; Jones; Lee; Lenoir; Lincoln; Macon; Madison; Martin; McDowell; Mecklenburg; Mitchell; Montgomery; Moore; Nash; New Hanover; Northampton; Onslow; Orange; Pamlico; Pasquotank; Pender; Perquimans; Person; Pitt; Polk; Randolph; Richmond; Robeson; Rockingham; Rowan; Rutherford; Sampson; Scotland; Stanly; Stokes; Surry; Swain; Transylvania; Tyrrell; Union; Vance; Wake; Warren; Washington; Watauga; Wayne; Wilkes; Wilson; Yadkin; Yancey."],
        BBOX[33.83,-84.33,36.59,-75.38]],
    ID["EPSG",3358]]
</source>
</source>


Line 78: Line 99:
<source lang="rsplus">
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
execGRASS("g.list", parameters = list(type = "vector", pattern = "precip*"))
precip
precip_30ynormals
precip_30ynormals_3d
</source>
</source>


Line 85: Line 109:
attributes(my_vmaps)
attributes(my_vmaps)
attributes(my_vmaps)$resOut
attributes(my_vmaps)$resOut
[1] "precip"              "precip_30ynormals"    "precip_30ynormals_3d"
</source>
</source>


Line 95: Line 120:
<source lang="rsplus">
<source lang="rsplus">
execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))
execGRASS("g.list", parameters = list(type = "raster", pattern = "lsat7_2002*"))
lsat7_2002_10
lsat7_2002_20
lsat7_2002_30
lsat7_2002_40
lsat7_2002_50
lsat7_2002_61
lsat7_2002_62
lsat7_2002_70
lsat7_2002_80
</source>
</source>


=== Reading in data from GRASS ===
=== Reading data from GRASS ===


==== Example 1 ====
==== Example 1: raster maps ====


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):
Read two GRASS raster maps, "geology_30m" and "elevation", from the North Carolina sample data location into the R current session as a new object "ncdata". This object is a SpatRaster layer from terra package:
<source lang="rsplus">
<source lang="rsplus">
# the cat parameter indicates which raster values to be returned as factors
ncdata <- read_RAST(c("geology_30m", "elevation"))
# - 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))
</source>
</source>
(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:
<source lang="rsplus">
<source lang="rsplus">
str(ncdata)
ncdata
Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots
class      : SpatRaster
  ..@ data       :'data.frame': 16616 obs. of 2 variables:
dimensions  : 450, 500, 2  (nrow, ncol, nlyr)
resolution  : 30, 30  (x, y)
extent      : 630000, 645000, 215000, 228500 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=33.75 +lon_0=-79 +lat_1=36.1666666666667 +lat_2=34.3333333333333 +x_0=609601.22 +y_0=0 +ellps=GRS80 +units=m +no_defs
sources    : file1da12a752d65.grd 
              file1da12112d9ea4.grd 
names       : geology_30m, elevation
min values  :         217,  55.92321
max values :         948, 156.25197
</source>
</source>


and also check the data structure in more detail:
Now, let's plot one of the raster layers within the SpatRaster object. We'll load terra library to have all functionality available:
<source lang="rsplus">
<source lang="rsplus">
str(ncdata@data)
library(terra)
'data.frame': 16616 obs. of  2 variables:
plot(ncdata$elevation, main="North Carolina elevation")
$ 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>
</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:
[[Image:ncdata_new.png|center]]
 
In addition, we can show what is going on inside the objects read into R:
<source lang="rsplus">
<source lang="rsplus">
image(ncdata, "elevation", col = terrain.colors(20))
summary(ncdata)
  geology_30m      elevation    
Min.  :217.0  Min.  : 56.01 
1st Qu.:217.0  1st Qu.: 94.77 
Median :270.0  Median :108.86 
Mean  :311.2  Mean  :110.36 
3rd Qu.:270.0  3rd Qu.:126.80 
Max.  :948.0  Max.  :156.07   
</source>
</source>


Add a title to the plot:
==== Example 2: single raster map ====
<source lang="rsplus">
title("North Carolina elevation")
</source>


[[Image:ncdata.png|center]]
Here an example to transfer a single raster map from GRASS GIS to R:


In addition, we can show what is going on inside the objects read into R:
<source lang="rsplus">
<source lang="rsplus">
str(G)
library(rgrass)
List of 26
library(terra)
$ DEBUG        : chr "0"
execGRASS("g.region", raster = "elevation", flags = "p")
$ LOCATION_NAME: chr "nc_spm_08_grass7"
ncdata <- read_RAST("elevation")
$ 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)
summary(ncdata)
Object of class SpatialGridDataFrame
plot(ncdata)
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                 
</source>
</source>


==== Example 2 ====
==== Example 3: vector maps ====


Here an example for a single map transfer from GRASS GIS to R:
Here an example to transfer a single vector map from GRASS GIS to R. In this case, vector maps from GRASS will be converted into SpatVector objects from the terra package in R:


<source lang="rsplus">
<source lang="rsplus">
library(rgrass7)
# needed prerequisite
execGRASS("g.region", raster = "elevation", flags = "p")
library(rgrass)
ncdata <- readRAST("elevation", cat=FALSE)
library(terra)
summary(ncdata)
execGRASS("v.info", map="schools_wake", layer="1")
spplot(ncdata, col = terrain.colors(20))
vInfo("schools_wake")
myschools <- read_VECT("schools_wake")
print(summary(myschools))
plot(myschools)
</source>
</source>


=== Summarizing data ===
==== Example 4: Summarizing data ====


We can create a table of cell counts:
We can create a table of cell counts:
<source lang="rsplus">
<source lang="rsplus">
table(ncdata$geology_30m)
freq(ncdata$geology_30m)
</source>
</source>
{| class="wikitable" border="1"
<pre>
!CZfg_217
  layer value count
!CZlg_262
1      1  217 80618
!CZig_270
2      1  262 22076
!CZbg_405
3      1  270 76597
!CZve_583
4      1  405 28190
!CZam_720  
5      1  583 2401
!CZg_766
6      1  720  536
!CZam_862
7      1  766   786
!CZbg_910    
8     1  862  6858
!Km_921
9     1  910  4996
!CZbg_945
10     1  921  1392
!CZam_946
11    1  945    1
!CZam_948
12    1  946  452
|-
13    1   948    97
|70381
</pre>
|19287
|66861   
|24561   
|2089      
|467      
|691      
|6017
|4351
|1211       
|1    
|398     
|85
|}


And compare with the equivalent GRASS module:
And compare with the equivalent GRASS module:
Line 248: Line 231:
</source>
</source>
<pre>
<pre>
217 CZfg 70381
217 CZfg 80618
262 CZlg 19287
262 CZlg 22076
270 CZig 66861
270 CZig 76597
405 CZbg 24561
405 CZbg 28190
583 CZve 2089
583 CZve 2401
720 CZam 467
720 CZam 536
766 CZg 691
766 CZg 786
862 CZam 6017
862 CZam 6858
910 CZbg 4351
910 CZbg 4996
921 Km 1211
921 Km 1392
945 CZbg 1
945 CZbg 1
946 CZam 398
946 CZam 452
948 CZam 85
948 CZam 97
</pre>
</pre>


<!--- it does no work with terra
Create a box plot of geologic types at different elevations:
Create a box plot of geologic types at different elevations:
<source lang="rsplus">
<source lang="rsplus">
Line 269: Line 253:


[[Image:boxplot_geo.png|center]]
[[Image:boxplot_geo.png|center]]
--->


=== Querying maps ===
==== Example 5: Querying maps ====


Sometimes you may want to query GRASS GIS maps from your R session.
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:
 
As an example, here the transfer of raster pixel values at the position of vector points:


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


Line 316: Line 299:
Export data from ''R'' back into a GRASS raster map:
Export data from ''R'' back into a GRASS raster map:
<source lang="rsplus">
<source lang="rsplus">
writeRAST(ncdata, "sqdemNC", zcol="sqdem", ignore.stderr=TRUE)
write_RAST(ncdata$sqdem, "sqdemNC", flags = c("o","overwrite"))
</source>
</source>


Check that it imported into GRASS ok:
Check that it imported properly:
<source lang="rsplus">
<source lang="rsplus">
execGRASS("r.info", parameters=list(map="sqdemNC"))
execGRASS("r.info", parameters=list(map="sqdemNC"))
Line 326: Line 309:
<pre>
<pre>
  +----------------------------------------------------------------------------+
  +----------------------------------------------------------------------------+
  | Map:      sqdemNC                        Date: Sun Jul 19 13:06:34 2015   |
  | Map:      sqdemNC                        Date: Fri Apr 21 17:14:05 2023   |
  | Mapset:  PERMANENT                      Login of Creator: veroandreo      |
  | Mapset:  PERMANENT                      Login of Creator: veroandreo      |
  | Location: nc_spm_08_grass7                                                |
  | Location: nc_spm_08_grass7                                                |
  | DataBase: /home/veroandreo/grassdata                                      |
  | DataBase: /home/veroandreo/grassdata                                      |
  | Title:     ( sqdemNC )                                                    |
  | Title:                                                                     |
  | Timestamp: none                                                            |
  | Timestamp: none                                                            |
  |----------------------------------------------------------------------------|
  |----------------------------------------------------------------------------|
  |                                                                            |
  |                                                                            |
  |  Type of Map:  raster              Number of Categories: 0              |
  |  Type of Map:  raster              Number of Categories: 0              |
  |  Data Type:    DCELL                                                      |
  |  Data Type:    FCELL                Semantic label: (none)                |
  |  Rows:        491                                                       |
  |  Rows:        450                                                       |
  |  Columns:      400                                                       |
  |  Columns:      500                                                       |
  |  Total Cells:  196400                                                     |
  |  Total Cells:  225000                                                     |
  |        Projection: Lambert Conformal Conic                                |
  |        Projection: Lambert Conformal Conic                                |
  |            N:    228500    S: 215000.0002   Res: 27.49490794              |
  |            N:    228500    S:     215000  Res:   30                    |
  |            E:    645000    W:    630000  Res: 37.5                     |
  |            E:    645000    W:    630000  Res:   30                     |
  |  Range of data:    min = 7.47818253045085 max = 12.5000787351036        |
  |  Range of data:    min = 7.478182 max = 12.50008                        |
  |                                                                            |
  |                                                                            |
  |  Data Description:                                                        |
  |  Data Description:                                                        |
  |    generated by r.in.bin                                                  |
  |    generated by r.in.gdal                                                  |
  |                                                                            |
  |                                                                            |
  |  Comments:                                                                |
  |  Comments:                                                                |
|    r.in.gdal --overwrite -o input="/home/veroandreo/tmp/grass8-veroandr\  |
|    eo-120898/RtmpPcRHkF/file1da12dbc5b5d.grd" output="sqdemNC" memory=3\  |
|    00 offset=0 num_digits=0                                                |
  |                                                                            |
  |                                                                            |
  +----------------------------------------------------------------------------+
  +----------------------------------------------------------------------------+
Line 367: Line 353:
</source>
</source>


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


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


[[File:Grass7_rstudio.png|thumb|500px|center|RStudio used in GRASS GIS 7 session]]
[[File:Grass7_rstudio.png|thumb|500px|center|RStudio used in GRASS GIS session]]


== GRASS within R ==
== 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).
In this case, we start '''GRASS GIS within an R session''', i.e., we use <tt>initGRASS()</tt> to start GRASS in a temporary location or in an existing GRASS GIS location.


=== Startup ===
The first argument in initGRASS() is gisBase=. This argument refers to the path where GRASS is installed. In the latest rgrass
version, if the gisBase argument is missing, initGRASS() will do a minimal effort of guessing it. Firstly, if a GRASS_INSTALLATION environment variable is availabe, then its value will automatically be used for gisBase. If not, then the system command <tt>grass --config path</tt> will be tried to get the value of GISBASE that corresponds to the GRASS installation in the system PATH (if any).


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):
If users, still want/need to pass the path to GRASS installation themselves, they can run:


<source lang="bash">
<source lang="bash">
# OSGeo4W users:
# OSGeo4W shell, Linux, Mac OSX users:
grass74 --config path
grass --config path
# Linux, Mac OSX users:
grass70 --config path
</source>
</source>


Line 395: Line 379:
<source lang="bash">
<source lang="bash">
# OSGeo4W users:
# OSGeo4W users:
C:\OSGEO4~1\apps\grass\grass-7.4.1
C:\OSGeo4W64\apps\grass\grass


# Linux, Mac OSX users:
# Linux, Mac OSX users:
/usr/local/grass-7.0.1
/usr/local/grass
</source>
 
If GRASS GIS was installed through OSGeo4W, users should first start the OSGeo4W Shell. Note that it may start in C: or a directory
where the user does not have writing permission. Before calling R or RStudio, first change to a directory where you are
sure you have writing permission.
 
<source lang="rsplus">
## MS-Windows users (example for an OSGeo4W 64bit installation)
# change to a directory with writing permission
C:\>d:
D:\>cd temp
D:\temp>cd testR
 
# start R within this directory:
D:\temp\testR>R
 
# to start RStudio from OSGeo4W Shell, run: "C:/Program Files/RStudio/bin/rstudio.exe"
# load rgrass package
library(rgrass)
 
# initialisation of GRASS in the North Carolina sample dataset
initGRASS(gisBase = "C:/OSGeo4W64/apps/grass/grass",
        gisDbase = "C:/Users/marissa/grassdata/",
        location = "nc_spm_08_grass7",
        mapset = "user1",
        home=tempdir(),
        override = TRUE)
</source>
</source>


To call GRASS GIS 7 functionality from R, start R and use the initGRASS() function to define the GRASS settings:
Linux and Mac OSX users just need to start R or RStudio the usual way, load the rgrass package and initialize GRASS as shown below:


<source lang="rsplus">
<source lang="rsplus">
## MS-Windows users (example for a OSGeo4W 64bit installation):
library(rgrass)
library(rgrass7)
# initialisation and the use of North Carolina sample dataset
initGRASS(gisBase = "C:/OSGeo4W64/apps/grass/grass-7.4.1",
        gisDbase = "C:/Users/marissa/GRASSdata/",
        location = "nc_spm_08_grass7", mapset = "user1", SG="elevation")


## Linux, Mac OSX users:
# initialisation of GRASS in the North Carolina sample dataset
library(rgrass7)
initGRASS(gisBase = "/usr/lib64/grass82",
# initialisation and the use of North Carolina sample dataset
initGRASS(gisBase = "/usr/local/grass-7.0.1", home = tempdir(),  
           gisDbase = "/home/veroandreo/grassdata/",
           gisDbase = "/home/veroandreo/grassdata/",
           location = "nc_spm_08_grass7", mapset = "user1", SG="elevation")
           location = "nc_basic_spm_grass7",
          mapset = "user1",
          home=tempdir(),
          override = TRUE)
</source>
</source>


Note: the optional SG parameter is a 'SpatialGrid' object to define the ‘DEFAULT_WIND’ of the temporary location.
=== Temporary location ===
 
If we don’t pass an existing location to initGRASS(), a temporary GRASS location will be created and set. By default this will be
an XY location, i.e., no CRS defined. To define a CRS for the temporary location, we create a terra SpatRaster object that by default will be EPSG:4326.


<source lang="rsplus">
<source lang="rsplus">
# set computational region to default (optional)
library(terra)
system("g.region -dp")
x <- rast()
# verify metadata
x
gmeta()
class      : SpatRaster
dimensions  : 180, 360, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84
</source>


# get two raster maps into R space
Then, we use that object to provide an EPSG to our temporary location via the SG= parameter and we start GRASS:
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))


# calculate object summaries
<source lang="rsplus">
summary(ncdata$geology_30m)
initGRASS(home = tempdir(),
CZfg_217 CZlg_262 CZig_270 CZbg_405 CZve_583 CZam_720  CZg_766 CZam_862
          SG = x,
    292      78      277      102        8        1        2      25
          override = TRUE)
CZbg_910  Km_921 CZam_946    NA's
      18        5        2  1009790
</source>
</source>
After that, users can write their raster and vector maps into GRASS with write_RAST() and write_VECT(), use GRASS GIS modules over those data with execGRASS() and export the results back to R with read_RAST() and read_VECT() as exemplified above.
=== Existing location ===
In the case of existing locations and mapsets, users will provide the path to them via the location= and mapset= parameters, and then read data from GRASS database into R through read_RAST() and read_VECT(), do their analyses or modeling in R and potentially write results back into GRASS with write_RAST() and write_VECT(). See examples above.


== R in GRASS in batch mode ==
== R in GRASS in batch mode ==
Line 447: Line 466:


<source lang="rsplus">
<source lang="rsplus">
library(rgrass7)
library(rgrass)
# initialisation and the use of north carolina dataset
library(terra)
initGRASS(gisBase = "/home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu",  
# initialisation of GRASS in the North Carolina sample dataset
initGRASS(gisBase = "/usr/lib64/grass82",  
           home = tempdir(),  
           home = tempdir(),  
           gisDbase = "/home/veroandreo/grassdata/",
           gisDbase = "/home/veroandreo/grassdata/",
           location = "nc_spm_08_grass7", mapset = "user1", SG="elevation",
           location = "nc_spm_08_grass7",  
          mapset = "user1",
           override = TRUE)
           override = TRUE)
# set region to default
# set region to default
Line 459: Line 480:
gmeta()
gmeta()
# read data into R
# read data into R
ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
ncdata <- read_RAST("geology_30m", cat=TRUE)
# summary of geology map
# summary of geology map
summary(ncdata$geology_30m)
summary(ncdata$geology_30m)
Line 471: Line 492:


<source lang="rsplus">
<source lang="rsplus">
    R version 3.2.1 (2015-06-18) -- "World-Famous Astronaut"
R version 4.1.3 (2022-03-10) -- "One Push-Up"
    Copyright (C) 2015 The R Foundation for Statistical Computing
Copyright (C) 2022 The R Foundation for Statistical Computing
    Platform: x86_64-redhat-linux-gnu (64-bit)
Platform: x86_64-redhat-linux-gnu (64-bit)
    ...
 
    > library(rgrass7)
> library(rgrass)
    Loading required package: sp
Loading required package: XML
    Loading required package: XML
GRASS GIS interface loaded with GRASS version: (GRASS not running)
    GRASS GIS interface loaded with GRASS version: (GRASS not running)
> library(terra)
    > # initialisation and the use of north carolina dataset
terra 1.7.23
    > initGRASS(gisBase = "/home/veroandreo/software/grass-7.0.svn/dist.x86_64-unknown-linux-gnu", home = tempdir(),  
 
    +          gisDbase = "/home/veroandreo/grassdata/",
> # initialisation of GRASS in the North Carolina sample dataset
    +          location = "nc_spm_08_grass7", mapset = "user1", SG="elevation",
> initGRASS(gisBase = "/usr/lib64/grass82",  
    +          override = TRUE)
+          home = tempdir(),  
    gisdbase    /home/veroandreo/grassdata/  
+          gisDbase = "/home/veroandreo/grassdata/",
    location    nc_spm_08_grass7  
+          location = "nc_spm_08_grass7",  
    mapset      user1  
+          mapset = "user1",
    rows        620  
+          override = TRUE)
    columns    1630  
gisdbase    /home/veroandreo/grassdata/  
    north      320000  
location    nc_spm_08_grass7  
    south      10000  
mapset      user1  
    west        120000  
rows        620  
    east        935000  
columns    1630  
    nsres      500  
north      320000  
    ewres      500  
south      10000  
    projection  +proj=lcc +lat_1=36.16666666666666 +lat_2=34.33333333333334
west        120000  
     +lat_0=33.75 +lon_0=-79 +x_0=609601.22 +y_0=0 +no_defs +a=6378137
east        935000  
    +rf=298.257222101 +towgs84=0.000,0.000,0.000 +to_meter=1
nsres      500  
   
ewres      500  
    > system("g.region -dp")
projection:
    projection: 99 (Lambert Conformal Conic)
  PROJCRS["NAD83(HARN) / North Carolina",
    zone:      0
     BASEGEOGCRS["NAD83(HARN)",
    datum:      nad83
        DATUM["NAD83 (High Accuracy Reference Network)",
    ellipsoid:  a=6378137 es=0.006694380022900787
            ELLIPSOID["GRS 1980",6378137,298.257222101,
    north:      320000
 
    south:      10000
> # set region to default
    west:      120000
> system("g.region -dp")
    east:      935000
projection: 99 (Lambert Conformal Conic)
    nsres:      500
zone:      0
    ewres:      500
datum:      nad83
    rows:      620
ellipsoid:  a=6378137 es=0.006694380022900787
    cols:      1630
north:      320000
    cells:      1010600
south:      10000
    > gmeta()
west:      120000
    gisdbase    /home/veroandreo/grassdata/  
east:      935000
    location    nc_spm_08_grass7  
nsres:      500
    mapset      user1  
ewres:      500
    rows        620  
rows:      620
    columns    1630  
cols:      1630
    north      320000  
cells:      1010600
    south      10000  
 
     ...
> gmeta()
     > ncdata <- readRAST(c("geology_30m", "elevation"), cat=c(TRUE, FALSE))
gisdbase    /home/veroandreo/grassdata/  
    ...
location    nc_spm_08_grass7  
    > summary(ncdata$geology_30m)
mapset      user1  
    CZfg_217 CZlg_262 CZig_270 CZbg_405 CZve_583 CZam_720 CZg_766 CZam_862
rows        620  
        292      78      277      102        8        1        2      25
columns    1630  
    CZbg_910   Km_921 CZam_946    NA's  
north      320000  
          18        5        2  1009790
south      10000  
    > proc.time()
west        120000
      user  system elapsed  
east        935000
      8.061   2.016 10.048
nsres      500
ewres      500
projection:
PROJCRS["NAD83(HARN) / North Carolina",
     BASEGEOGCRS["NAD83(HARN)",
        DATUM["NAD83 (High Accuracy Reference Network)",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
                ...       
                ...
        BBOX[33.83,-84.33,36.59,-75.38]],
     ID["EPSG",3358]]
...
> ncdata <- read_RAST("elev_state_500m")
...
r.out.gdal complete. File </home/veroandreo/tmp/RtmpoA1IKV/file2844e28321574.grd> created.
 
> summary(ncdata)
  file2844e28321574
Min.  :  -6.47 
1st Qu.:  14.36 
Median :  86.55 
Mean  : 214.55 
3rd Qu.: 252.66 
Max.   :1878.39 
NA's   :46188   
 
> proc.time()
  user  system elapsed  
15.574   1.275 16.998
</source>
</source>


Line 538: Line 588:
=== Running out of disk space ===
=== 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 <tt>$HOME/.bashrc</tt> the entry:
Linux: A common issue is that /tmp/ is used for temporary files albeit being often a small partition. To change that to a larger directory, you may '''add''' to your <tt>$HOME/.bashrc</tt> the entry:
   
   
<source lang="bash">
<source lang="bash">
Line 546: Line 596:
</source>
</source>


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.
The drawback of this approach 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.


[[Category:FAQ]]
[[Category:FAQ]]

Latest revision as of 19:48, 21 April 2023

Installation

See R_statistics/Installation

Terminology

See "Overview" in R_statistics

R within GRASS

In this example, we will use R within a GRASS GIS session, i.e. start R (or RStudio) from the GRASS GIS terminal or command line interface.

Startup

  • First start a GRASS GIS session. Then, at the GRASS command line start R (for an 'rstudio' session, see below)
In this example we will use the North Carolina sample dataset.

Reset the region settings to the defaults 

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

Launch R from the GRASS prompt 

GRASS (nc_spm_08_grass7):~ > R

Load the rgrass library: 

library(rgrass)

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

gmeta()
gisdbase    /home/mneteler/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:
 PROJCRS["NAD83(HARN) / North Carolina",
    BASEGEOGCRS["NAD83(HARN)",
        DATUM["NAD83 (High Accuracy Reference Network)",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4152]],
    CONVERSION["SPCS83 North Carolina zone (meters)",
        METHOD["Lambert Conic Conformal (2SP)",
            ID["EPSG",9802]],
        PARAMETER["Latitude of false origin",33.75,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8821]],
        PARAMETER["Longitude of false origin",-79,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8822]],
        PARAMETER["Latitude of 1st standard parallel",36.1666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8823]],
        PARAMETER["Latitude of 2nd standard parallel",34.3333333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8824]],
        PARAMETER["Easting at false origin",609601.22,
            LENGTHUNIT["metre",1],
            ID["EPSG",8826]],
        PARAMETER["Northing at false origin",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8827]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["United States (USA) - North Carolina - counties of Alamance; Alexander; Alleghany; Anson; Ashe; Avery; Beaufort; Bertie; Bladen; Brunswick; Buncombe; Burke; Cabarrus; Caldwell; Camden; Carteret; Caswell; Catawba; Chatham; Cherokee; Chowan; Clay; Cleveland; Columbus; Craven; Cumberland; Currituck; Dare; Davidson; Davie; Duplin; Durham; Edgecombe; Forsyth; Franklin; Gaston; Gates; Graham; Granville; Greene; Guilford; Halifax; Harnett; Haywood; Henderson; Hertford; Hoke; Hyde; Iredell; Jackson; Johnston; Jones; Lee; Lenoir; Lincoln; Macon; Madison; Martin; McDowell; Mecklenburg; Mitchell; Montgomery; Moore; Nash; New Hanover; Northampton; Onslow; Orange; Pamlico; Pasquotank; Pender; Perquimans; Person; Pitt; Polk; Randolph; Richmond; Robeson; Rockingham; Rowan; Rutherford; Sampson; Scotland; Stanly; Stokes; Surry; Swain; Transylvania; Tyrrell; Union; Vance; Wake; Warren; Washington; Watauga; Wayne; Wilkes; Wilson; Yadkin; Yancey."],
        BBOX[33.83,-84.33,36.59,-75.38]],
    ID["EPSG",3358]]

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*"))
 precip
 precip_30ynormals
 precip_30ynormals_3d

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
 [1] "precip"               "precip_30ynormals"    "precip_30ynormals_3d"

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*"))
 lsat7_2002_10
 lsat7_2002_20
 lsat7_2002_30
 lsat7_2002_40
 lsat7_2002_50
 lsat7_2002_61
 lsat7_2002_62
 lsat7_2002_70
 lsat7_2002_80

Reading data from GRASS

Example 1: raster maps

Read two GRASS raster maps, "geology_30m" and "elevation", from the North Carolina sample data location into the R current session as a new object "ncdata". This object is a SpatRaster layer from terra package: 

ncdata <- read_RAST(c("geology_30m", "elevation"))

We can verify the new R object: 

ncdata
class       : SpatRaster 
dimensions  : 450, 500, 2  (nrow, ncol, nlyr)
resolution  : 30, 30  (x, y)
extent      : 630000, 645000, 215000, 228500  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=33.75 +lon_0=-79 +lat_1=36.1666666666667 +lat_2=34.3333333333333 +x_0=609601.22 +y_0=0 +ellps=GRS80 +units=m +no_defs 
sources     : file1da12a752d65.grd  
              file1da12112d9ea4.grd  
names       : geology_30m, elevation 
min values  :         217,  55.92321 
max values  :         948, 156.25197

Now, let's plot one of the raster layers within the SpatRaster object. We'll load terra library to have all functionality available: 

library(terra)
plot(ncdata$elevation, main="North Carolina elevation")

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

summary(ncdata)
  geology_30m      elevation     
 Min.   :217.0   Min.   : 56.01  
 1st Qu.:217.0   1st Qu.: 94.77  
 Median :270.0   Median :108.86  
 Mean   :311.2   Mean   :110.36  
 3rd Qu.:270.0   3rd Qu.:126.80  
 Max.   :948.0   Max.   :156.07

Example 2: single raster map

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

library(rgrass)
library(terra)
execGRASS("g.region", raster = "elevation", flags = "p")
ncdata <- read_RAST("elevation")
summary(ncdata)
plot(ncdata)

Example 3: vector maps

Here an example to transfer a single vector map from GRASS GIS to R. In this case, vector maps from GRASS will be converted into SpatVector objects from the terra package in R: 

# needed prerequisite
library(rgrass)
library(terra)
execGRASS("v.info", map="schools_wake", layer="1")
vInfo("schools_wake")
myschools <- read_VECT("schools_wake")
print(summary(myschools))
plot(myschools)

Example 4: Summarizing data

We can create a table of cell counts: 

freq(ncdata$geology_30m)
   layer value count
1      1   217 80618
2      1   262 22076
3      1   270 76597
4      1   405 28190
5      1   583  2401
6      1   720   536
7      1   766   786
8      1   862  6858
9      1   910  4996
10     1   921  1392
11     1   945     1
12     1   946   452
13     1   948    97

And compare with the equivalent GRASS module: 

execGRASS("r.stats", flags=c("c", "l"), parameters=list(input="geology_30m"), ignore.stderr=TRUE)
217 CZfg 80618
262 CZlg 22076
270 CZig 76597
405 CZbg 28190
583 CZve 2401
720 CZam 536
766 CZg 786
862 CZam 6858
910 CZbg 4996
921 Km 1392
945 CZbg 1
946 CZam 452
948 CZam 97


Example 5: 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 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: 

write_RAST(ncdata$sqdem, "sqdemNC", flags = c("o","overwrite"))

Check that it imported properly: 

execGRASS("r.info", parameters=list(map="sqdemNC"))
 +----------------------------------------------------------------------------+
 | Map:      sqdemNC                        Date: Fri Apr 21 17:14:05 2023    |
 | Mapset:   PERMANENT                      Login of Creator: veroandreo      |
 | Location: nc_spm_08_grass7                                                 |
 | DataBase: /home/veroandreo/grassdata                                       |
 | Title:                                                                     |
 | Timestamp: none                                                            |
 |----------------------------------------------------------------------------|
 |                                                                            |
 |   Type of Map:  raster               Number of Categories: 0               |
 |   Data Type:    FCELL                Semantic label: (none)                |
 |   Rows:         450                                                        |
 |   Columns:      500                                                        |
 |   Total Cells:  225000                                                     |
 |        Projection: Lambert Conformal Conic                                 |
 |            N:     228500    S:     215000   Res:    30                     |
 |            E:     645000    W:     630000   Res:    30                     |
 |   Range of data:    min = 7.478182  max = 12.50008                         |
 |                                                                            |
 |   Data Description:                                                        |
 |    generated by r.in.gdal                                                  |
 |                                                                            |
 |   Comments:                                                                |
 |    r.in.gdal --overwrite -o input="/home/veroandreo/tmp/grass8-veroandr\   |
 |    eo-120898/RtmpPcRHkF/file1da12dbc5b5d.grd" output="sqdemNC" memory=3\   |
 |    00 offset=0 num_digits=0                                                |
 |                                                                            |
 +----------------------------------------------------------------------------+

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 rgrass library in your RStudio project 

library(rgrass)

and you are ready to go.

RStudio used in GRASS GIS session

GRASS within R

In this case, we start GRASS GIS within an R session, i.e., we use initGRASS() to start GRASS in a temporary location or in an existing GRASS GIS location.

The first argument in initGRASS() is gisBase=. This argument refers to the path where GRASS is installed. In the latest rgrass version, if the gisBase argument is missing, initGRASS() will do a minimal effort of guessing it. Firstly, if a GRASS_INSTALLATION environment variable is availabe, then its value will automatically be used for gisBase. If not, then the system command grass --config path will be tried to get the value of GISBASE that corresponds to the GRASS installation in the system PATH (if any).

If users, still want/need to pass the path to GRASS installation themselves, they can run: 

# OSGeo4W shell, Linux, Mac OSX users:
grass --config path

It may report something like: 

# OSGeo4W users:
C:\OSGeo4W64\apps\grass\grass

# Linux, Mac OSX users:
/usr/local/grass

If GRASS GIS was installed through OSGeo4W, users should first start the OSGeo4W Shell. Note that it may start in C: or a directory where the user does not have writing permission. Before calling R or RStudio, first change to a directory where you are sure you have writing permission. 

## MS-Windows users (example for an OSGeo4W 64bit installation)
# change to a directory with writing permission
C:\>d:
D:\>cd temp
D:\temp>cd testR

# start R within this directory:
D:\temp\testR>R

# to start RStudio from OSGeo4W Shell, run: "C:/Program Files/RStudio/bin/rstudio.exe"
 
# load rgrass package
library(rgrass)

# initialisation of GRASS in the North Carolina sample dataset
initGRASS(gisBase = "C:/OSGeo4W64/apps/grass/grass",
         gisDbase = "C:/Users/marissa/grassdata/",
         location = "nc_spm_08_grass7", 
         mapset = "user1",
         home=tempdir(),
         override = TRUE)

Linux and Mac OSX users just need to start R or RStudio the usual way, load the rgrass package and initialize GRASS as shown below: 

library(rgrass)

# initialisation of GRASS in the North Carolina sample dataset
initGRASS(gisBase = "/usr/lib64/grass82",
          gisDbase = "/home/veroandreo/grassdata/",
          location = "nc_basic_spm_grass7",
          mapset = "user1",
          home=tempdir(),
          override = TRUE)

Temporary location

If we don’t pass an existing location to initGRASS(), a temporary GRASS location will be created and set. By default this will be an XY location, i.e., no CRS defined. To define a CRS for the temporary location, we create a terra SpatRaster object that by default will be EPSG:4326. 

library(terra)
x <- rast()
x
class       : SpatRaster 
dimensions  : 180, 360, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84

Then, we use that object to provide an EPSG to our temporary location via the SG= parameter and we start GRASS: 

initGRASS(home = tempdir(), 
          SG = x, 
          override = TRUE)

After that, users can write their raster and vector maps into GRASS with write_RAST() and write_VECT(), use GRASS GIS modules over those data with execGRASS() and export the results back to R with read_RAST() and read_VECT() as exemplified above.

Existing location

In the case of existing locations and mapsets, users will provide the path to them via the location= and mapset= parameters, and then read data from GRASS database into R through read_RAST() and read_VECT(), do their analyses or modeling in R and potentially write results back into GRASS with write_RAST() and write_VECT(). See examples above.

R in GRASS in batch mode

Run the following script with 

R CMD BATCH batch.R

library(rgrass)
library(terra)
# initialisation of GRASS in the North Carolina sample dataset
initGRASS(gisBase = "/usr/lib64/grass82", 
          home = tempdir(), 
          gisDbase = "/home/veroandreo/grassdata/",
          location = "nc_spm_08_grass7", 
          mapset = "user1",
          override = TRUE)
# set region to default
system("g.region -dp")
# verify
gmeta()
# read data into R
ncdata <- read_RAST("geology_30m", cat=TRUE)
# summary of geology map
summary(ncdata$geology_30m)
proc.time()

The result is (shorted here): 

cat batch.Rout

R version 4.1.3 (2022-03-10) -- "One Push-Up"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)

> library(rgrass)
Loading required package: XML
GRASS GIS interface loaded with GRASS version: (GRASS not running)
> library(terra)
terra 1.7.23

> # initialisation of GRASS in the North Carolina sample dataset
> initGRASS(gisBase = "/usr/lib64/grass82", 
+           home = tempdir(), 
+           gisDbase = "/home/veroandreo/grassdata/",
+           location = "nc_spm_08_grass7", 
+           mapset = "user1",
+           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:
 PROJCRS["NAD83(HARN) / North Carolina",
    BASEGEOGCRS["NAD83(HARN)",
        DATUM["NAD83 (High Accuracy Reference Network)",
            ELLIPSOID["GRS 1980",6378137,298.257222101,

> # set region to default
> 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 
west        120000 
east        935000 
nsres       500 
ewres       500 
projection:
 PROJCRS["NAD83(HARN) / North Carolina",
    BASEGEOGCRS["NAD83(HARN)",
        DATUM["NAD83 (High Accuracy Reference Network)",
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]]],
                ...        
                ...
        BBOX[33.83,-84.33,36.59,-75.38]],
    ID["EPSG",3358]] 
...
> ncdata <- read_RAST("elev_state_500m")
...
r.out.gdal complete. File </home/veroandreo/tmp/RtmpoA1IKV/file2844e28321574.grd> created.

> summary(ncdata)
 file2844e28321574
 Min.   :  -6.47  
 1st Qu.:  14.36  
 Median :  86.55  
 Mean   : 214.55  
 3rd Qu.: 252.66  
 Max.   :1878.39  
 NA's   :46188    

> proc.time()
   user  system elapsed 
 15.574   1.275  16.998

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 of this approach 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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