# Coercion between object formats

#### Roger Bivand

## 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 now archived 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/osgeo/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/osgeo/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.

On loading and attaching, terra displays its version:

    library("terra")

    ## terra 1.8.10

    library("sf")

    ## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

    library("sp")

    library("stars")

    ## Loading required package: abind

    library("raster")

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

    gdal(lib = "all")

    ##     gdal     proj     geos
    ##  "3.5.3"  "9.1.0" "3.11.0"

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>     <num>
    ##  values      :     1 Diekirch     1 Clervaux   312 1.808e+04
    ##                    1 Diekirch     2 Diekirch   218 3.254e+04
    ##                    1 Diekirch     3  Redange   259 1.866e+04

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

    try(inMemory(v))

    ## Error : unable to find an inherited method for function 'inMemory' for signature 'x = "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>     <num>
    ##  values      :     1 Diekirch     1 Clervaux   312 1.808e+04
    ##                    1 Diekirch     2 Diekirch   218 3.254e+04
    ##                    1 Diekirch     3  Redange   259 1.866e+04

    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), methods in raster are used as an intermediate step:

    v_sp <- as(v, "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:

    try(inMemory(r))

    ## [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  5.742  0.008333 WGS 84 FALSE [x]
    ## y    1 90  50.19 -0.008333 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        : elev.tif
    ## 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  5.742  0.008333 WGS 84 FALSE [x]
    ## y    1 90  50.19 -0.008333 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        : elev.tif
    ## 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.