Introduction

The original R-GRASS interface (R. S. Bivand 2000; Neteler and Mitasova 2008) was designed to move raster and later vector data between R and GRASS GIS. To do this, use was made of intermediate files, often using the external GDAL library on both sides. On the R side, the rgdal package was used, interfacing GDAL and PROJ as GRASS GIS also did. The GRASS commands r.in.gdal, r.out.gdal, v.in.ogr and v.out.ogr were matched by rgdal functions using the same underlying external libraries:

GDAL was supplemented for raster data by simply reading and writing uncompressed binary files using r.in.bin and r.out.bin, with custom functions on the R side. As then written, the R-GRASS interface used sp classes for both raster and vector data, supplemented more recently with sf classes for vector data only.

The current version of the R-GRASS interface has been simplified to use the terra package because it, like sf and rgdal before it, links to the important external libraries. The workhorse driver is known as RRASTER, and has been widely used in raster and terra (see also (https://rspatial.org)). It uses GDAL but writes a flat uncompressed binary file. Using terra::rast() also appears to preserve category names and colour tables, but needs further testing (see (https://github.com/rsbivand/rgrass/issues/42)).

From GDAL 3.5.0, the RRASTER driver also supports WKT2_2019 CRS representations; in earlier versions of GDAL, the driver only supported the proj-string representation (https://github.com/rsbivand/rgrass/issues/51).

These changes mean that users transferring data between R and GRASS will need to coerce between terra classes SpatVector and SpatRaster and the class system of choice. In addition, SpatRaster is only read into memory from file when this is required, so requiring some care.

Loading and attaching packages

This vignette is constructed conditioning on the availability of aforementioned R packages, i.e. if some were missing at the time of package building, some code blocks will not be displayed.

The setting below, needed before loading sp, is to prevent sp from relying on rgdal or rgeos. Those packages will retire by the end of 2023 (Pebesma and Bivand 2022).

Sys.setenv("_SP_EVOLUTION_STATUS_"="2")

On loading and attaching, terra displays its version:

## terra 1.6.47
## Linking to GEOS 3.11.1, GDAL 3.6.0, PROJ 9.1.1; sf_use_s2() is TRUE
## Loading required package: abind

terra::gdal() tells us the versions of the external libraries being used by terra:

gdal(lib="all")
##     gdal     proj     geos 
##  "3.6.0"  "9.1.1" "3.11.1"

When using CRAN binary packages built static for Windows and macOS, the R packages will use the same versions of the external libraries, but not necessarily the same versions as those against which GRASS was installed.

"SpatVector" coercion

In the terra package (Hijmans 2022b), vector data are held in "SpatVector" objects. This means that when read_VECT() is used, a "SpatVector" object is returned, and the same class of object is needed for write_VECT() for writing to GRASS.

fv <- system.file("ex/lux.shp", package="terra")
(v <- vect(fv))
##  class       : SpatVector 
##  geometry    : polygons 
##  dimensions  : 12, 6  (geometries, attributes)
##  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
##  source      : lux.shp
##  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
##  names       :  ID_1   NAME_1  ID_2   NAME_2  AREA   POP
##  type        : <num>    <chr> <num>    <chr> <num> <int>
##  values      :     1 Diekirch     1 Clervaux   312 18081
##                    1 Diekirch     2 Diekirch   218 32543
##                    1 Diekirch     3  Redange   259 18664

These objects are always held in memory, so there is no inMemory() method:

## Error in (function (classes, fdef, mtable)  : 
##   unable to find an inherited method for function 'inMemory' for signature '"SpatVector"'

The coordinate reference system is expressed in WKT2-2019 form:

cat(crs(v), "\n")
## GEOGCRS["WGS 84",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     ID["EPSG",4326]]

"sf"

Most new work should use vector classes defined in the sf package (Pebesma 2022, 2018). In this case, coercion uses st_as_sf():

v_sf <- st_as_sf(v)
v_sf
## Simple feature collection with 12 features and 6 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: 5.74414 ymin: 49.44781 xmax: 6.528252 ymax: 50.18162
## Geodetic CRS:  WGS 84
## First 10 features:
##    ID_1       NAME_1 ID_2           NAME_2 AREA    POP
## 1     1     Diekirch    1         Clervaux  312  18081
## 2     1     Diekirch    2         Diekirch  218  32543
## 3     1     Diekirch    3          Redange  259  18664
## 4     1     Diekirch    4          Vianden   76   5163
## 5     1     Diekirch    5            Wiltz  263  16735
## 6     2 Grevenmacher    6       Echternach  188  18899
## 7     2 Grevenmacher    7           Remich  129  22366
## 8     2 Grevenmacher   12     Grevenmacher  210  29828
## 9     3   Luxembourg    8         Capellen  185  48187
## 10    3   Luxembourg    9 Esch-sur-Alzette  251 176820
##                          geometry
## 1  POLYGON ((6.026519 50.17767...
## 2  POLYGON ((6.178368 49.87682...
## 3  POLYGON ((5.881378 49.87015...
## 4  POLYGON ((6.131309 49.97256...
## 5  POLYGON ((5.977929 50.02602...
## 6  POLYGON ((6.385532 49.83703...
## 7  POLYGON ((6.316665 49.62337...
## 8  POLYGON ((6.425158 49.73164...
## 9  POLYGON ((5.998312 49.69992...
## 10 POLYGON ((6.039474 49.44826...

and the vect() method to get from sf to terra:

v_sf_rt <- vect(v_sf)
v_sf_rt
##  class       : SpatVector 
##  geometry    : polygons 
##  dimensions  : 12, 6  (geometries, attributes)
##  extent      : 5.74414, 6.528252, 49.44781, 50.18162  (xmin, xmax, ymin, ymax)
##  coord. ref. : lon/lat WGS 84 (EPSG:4326) 
##  names       :  ID_1   NAME_1  ID_2   NAME_2  AREA   POP
##  type        : <num>    <chr> <num>    <chr> <num> <int>
##  values      :     1 Diekirch     1 Clervaux   312 18081
##                    1 Diekirch     2 Diekirch   218 32543
##                    1 Diekirch     3  Redange   259 18664
all.equal(v_sf_rt, v, check.attributes=FALSE)
## [1] TRUE

"Spatial"

To coerce to and from vector classes defined in the sp package (Roger S. Bivand, Pebesma, and Gomez-Rubio 2013), use the classes defined in sf as an intermediate step:

v_sp <- as(v_sf, "Spatial")
print(summary(v_sp))
## Object of class SpatialPolygonsDataFrame
## Coordinates:
##        min       max
## x  5.74414  6.528252
## y 49.44781 50.181622
## Is projected: FALSE 
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Data attributes:
##       ID_1          NAME_1               ID_2          NAME_2         
##  Min.   :1.000   Length:12          Min.   : 1.00   Length:12         
##  1st Qu.:1.000   Class :character   1st Qu.: 3.75   Class :character  
##  Median :2.000   Mode  :character   Median : 6.50   Mode  :character  
##  Mean   :1.917                      Mean   : 6.50                     
##  3rd Qu.:3.000                      3rd Qu.: 9.25                     
##  Max.   :3.000                      Max.   :12.00                     
##       AREA            POP        
##  Min.   : 76.0   Min.   :  5163  
##  1st Qu.:187.2   1st Qu.: 18518  
##  Median :225.5   Median : 26097  
##  Mean   :213.4   Mean   : 50167  
##  3rd Qu.:253.0   3rd Qu.: 36454  
##  Max.   :312.0   Max.   :182607
v_sp_rt <- vect(st_as_sf(v_sp))
all.equal(v_sp_rt, v, check.attributes=FALSE)
## [1] TRUE

"SpatRaster" coercion

In the terra package, raster data are held in "SpatRaster" objects. This means that when read_RAST() is used, a "SpatRaster" object is returned, and the same class of object is needed for write_RAST() for writing to GRASS.

fr <- system.file("ex/elev.tif", package="terra")
(r <- rast(fr))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : elev.tif 
## name        : elevation 
## min value   :       141 
## max value   :       547

In general, "SpatRaster" objects are files, rather than data held in memory:

## [1] FALSE

"stars"

The stars package (Pebesma 2021) uses GDAL through sf. A coercion method is provided from "SpatRaster" to "stars":

r_stars <- st_as_stars(r)
print(r_stars)
## stars object with 2 dimensions and 1 attribute
## attribute(s):
##           Min. 1st Qu. Median     Mean 3rd Qu. Max. NA's
## elev.tif   141     291    333 348.3366     406  547 3942
## dimension(s):
##   from to  offset       delta refsys point x/y
## x    1 95 5.74167  0.00833333 WGS 84 FALSE [x]
## y    1 90 50.1917 -0.00833333 WGS 84 FALSE [y]

which round-trips in memory.

(r_stars_rt <- rast(r_stars))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        : lyr.1 
## min value   :   141 
## max value   :   547

When coercing to "stars_proxy" the same applies:

(r_stars_p <- st_as_stars(r, proxy=TRUE))
## stars_proxy object with 1 attribute in 1 file(s):
## $elev.tif
## [1] "[...]/elev.tif"
## 
## dimension(s):
##   from to  offset       delta refsys point x/y
## x    1 95 5.74167  0.00833333 WGS 84 FALSE [x]
## y    1 90 50.1917 -0.00833333 WGS 84 FALSE [y]

with coercion from "stars_proxy" also not reading data into memory:

(r_stars_p_rt <- rast(r_stars_p))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : elev.tif 
## name        : elevation 
## min value   :       141 
## max value   :       547

"RasterLayer"

From version 3.6-3 the raster package (Hijmans 2022a) uses terra for all GDAL operations. Because of this, coercing a "SpatRaster" object to a "RasterLayer" object is simple:

(r_RL <- raster(r))
## class      : RasterLayer 
## dimensions : 90, 95, 8550  (nrow, ncol, ncell)
## resolution : 0.008333333, 0.008333333  (x, y)
## extent     : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +datum=WGS84 +no_defs 
## source     : elev.tif 
## names      : elevation 
## values     : 141, 547  (min, max)
inMemory(r_RL)
## [1] FALSE

The WKT2-2019 CRS representation is present but not shown by default:

cat(wkt(r_RL), "\n")
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]

This object (held on file rather than in memory) can be round-tripped:

(r_RL_rt <- rast(r_RL))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
## source      : elev.tif 
## name        : elevation 
## min value   :       141 
## max value   :       547

"Spatial"

"RasterLayer" objects can be used for coercion from a "SpatRaster" object to a "SpatialGridDataFrame" object:

r_sp_RL <- as(r_RL, "SpatialGridDataFrame")
summary(r_sp_RL)
## Object of class SpatialGridDataFrame
## Coordinates:
##          min       max
## s1  5.741667  6.533333
## s2 49.441667 50.191667
## Is projected: FALSE 
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Grid attributes:
##    cellcentre.offset    cellsize cells.dim
## s1          5.745833 0.008333333        95
## s2         49.445833 0.008333333        90
## Data attributes:
##    elevation    
##  Min.   :141.0  
##  1st Qu.:291.0  
##  Median :333.0  
##  Mean   :348.3  
##  3rd Qu.:406.0  
##  Max.   :547.0  
##  NA's   :3942

The WKT2-2019 CRS representation is present but not shown by default:

cat(wkt(r_sp_RL), "\n")
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]

This object can be round-tripped, but use of raster forefronts the Proj.4 string CRS representation:

(r_sp_RL_rt <- raster(r_sp_RL))
## class      : RasterLayer 
## dimensions : 90, 95, 8550  (nrow, ncol, ncell)
## resolution : 0.008333333, 0.008333333  (x, y)
## extent     : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## crs        : +proj=longlat +datum=WGS84 +no_defs 
## source     : memory
## names      : elevation 
## values     : 141, 547  (min, max)
cat(wkt(r_sp_RL_rt), "\n")
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]
(r_sp_rt <- rast(r_sp_RL_rt))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +no_defs 
## source(s)   : memory
## name        : elevation 
## min value   :       141 
## max value   :       547
crs(r_sp_RL_rt)
## Coordinate Reference System:
## Deprecated Proj.4 representation: +proj=longlat +datum=WGS84 +no_defs 
## WKT2 2019 representation:
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]

Coercion to the sp "SpatialGridDataFrame" representation is also provided by stars:

r_sp_stars <- as(r_stars, "Spatial")
summary(r_sp_stars)
## Object of class SpatialGridDataFrame
## Coordinates:
##         min       max
## x  5.741667  6.533333
## y 49.441667 50.191667
## Is projected: FALSE 
## proj4string : [+proj=longlat +datum=WGS84 +no_defs]
## Grid attributes:
##   cellcentre.offset    cellsize cells.dim
## x          5.745833 0.008333333        95
## y         49.445833 0.008333333        90
## Data attributes:
##     elev.tif    
##  Min.   :141.0  
##  1st Qu.:291.0  
##  Median :333.0  
##  Mean   :348.3  
##  3rd Qu.:406.0  
##  Max.   :547.0  
##  NA's   :3942
cat(wkt(r_sp_stars), "\n")
## GEOGCRS["WGS 84",
##     ENSEMBLE["World Geodetic System 1984 ensemble",
##         MEMBER["World Geodetic System 1984 (Transit)"],
##         MEMBER["World Geodetic System 1984 (G730)"],
##         MEMBER["World Geodetic System 1984 (G873)"],
##         MEMBER["World Geodetic System 1984 (G1150)"],
##         MEMBER["World Geodetic System 1984 (G1674)"],
##         MEMBER["World Geodetic System 1984 (G1762)"],
##         MEMBER["World Geodetic System 1984 (G2139)"],
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ENSEMBLEACCURACY[2.0]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433]],
##     CS[ellipsoidal,2],
##         AXIS["geodetic latitude (Lat)",north,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433]],
##         AXIS["geodetic longitude (Lon)",east,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433]],
##     USAGE[
##         SCOPE["Horizontal component of 3D system."],
##         AREA["World."],
##         BBOX[-90,-180,90,180]],
##     ID["EPSG",4326]]

and can be round-tripped:

(r_sp_stars_rt <- rast(st_as_stars(r_sp_stars)))
## class       : SpatRaster 
## dimensions  : 90, 95, 1  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : 5.741667, 6.533333, 49.44167, 50.19167  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## name        : lyr.1 
## min value   :   141 
## max value   :   547

``

cat(crs(r_sp_rt), "\n")
## GEOGCRS["unknown",
##     DATUM["World Geodetic System 1984",
##         ELLIPSOID["WGS 84",6378137,298.257223563,
##             LENGTHUNIT["metre",1]],
##         ID["EPSG",6326]],
##     PRIMEM["Greenwich",0,
##         ANGLEUNIT["degree",0.0174532925199433],
##         ID["EPSG",8901]],
##     CS[ellipsoidal,2],
##         AXIS["longitude",east,
##             ORDER[1],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]],
##         AXIS["latitude",north,
##             ORDER[2],
##             ANGLEUNIT["degree",0.0174532925199433,
##                 ID["EPSG",9122]]]]

References

Bivand, R. S. 2000. “Using the R Statistical Data Analysis Language on GRASS 5.0 GIS Data Base Files.” Computers and Geosciences 26: 1043–52.
Bivand, Roger S., Edzer Pebesma, and Virgilio Gomez-Rubio. 2013. Applied Spatial Data Analysis with R, Second Edition. Springer, NY. https://asdar-book.org/.
Hijmans, Robert J. 2022a. raster: Geographic Data Analysis and Modeling. https://cran.r-project.org/package=raster.
———. 2022b. terra: Spatial Data Analysis. https://cran.r-project.org/package=terra.
Neteler, M., and H. Mitasova. 2008. Open Source GIS: A GRASS GIS Approach, Third Edition. New York: Springer.
Pebesma, Edzer. 2018. Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439–46. https://doi.org/10.32614/RJ-2018-009.
———. 2021. stars: Spatiotemporal Arrays, Raster and Vector Data Cubes. https://CRAN.R-project.org/package=stars.
———. 2022. sf: Simple Features for R. https://cran.r-project.org/package=sf.
Pebesma, Edzer, and Roger Bivand. 2022. R-spatial Evolution: Retirement of rgdal, rgeos and maptools.” 2022. https://r-spatial.org/r/2022/04/12/evolution.html.