GRasters and GVectors are really pointers to objects in GRASS. The values displayed when you use show() or print() for a GRaster or GVector are stored in R. If, on the odd chance that you make a change to a GRaster or GVector (e.g., using commands in the rgrass package), the changes will not be automatically reflected in the GRaster or GVector. This function can be used to update the objects in R to reflect their proper values.
Examples
if (grassStarted()) {
# Setup
library(terra)
# Example data
madElev <- fastData("madElev")
madForest2000 <- fastData("madForest2000")
# Convert SpatRasters to GRasters
elev <- fast(madElev)
forest <- fast(madForest2000)
### GRaster properties
######################
# plotting
plot(elev)
# dimensions
dim(elev) # rows, columns, depths, layers
nrow(elev) # rows
ncol(elev) # columns
ndepth(elev) # depths
nlyr(elev) # layers
res(elev) # resolution (2D)
res3d(elev) # resolution (3D)
zres(elev) # vertical resolution
xres(elev) # vertical resolution
yres(elev) # vertical resolution
zres(elev) # vertical resolution (NA because this is a 2D GRaster)
# cell counts
ncell(elev) # cells
ncell3d(elev) # cells (3D rasters only)
# number of NA and non-NA cells
nacell(elev)
nonnacell(elev)
# topology
topology(elev) # number of dimensions
is.2d(elev) # is it 2-dimensional?
is.3d(elev) # is it 3-dimensional?
minmax(elev) # min/max values
# "names" of the object
names(elev)
# coordinate reference system
crs(elev)
st_crs(elev)
# extent (bounding box)
ext(elev)
# vertical extent (not defined for this raster)
zext(elev)
# data type
datatype(elev) # fasterRaster type
datatype(elev, "GRASS") # GRASS type
datatype(elev, "terra") # terra type
datatype(elev, "GDAL") # GDAL type
is.integer(elev)
is.float(elev)
is.double(elev)
is.factor(elev)
# convert data type
as.int(elev) # integer; note that "elev" is already of type "integer"
as.float(elev) # floating-precision
as.doub(elev) # double-precision
# assigning
pie <- elev
pie[] <- pi # assign all cells to the value of pi
pie
# concatenating multiple GRasters
rasts <- c(elev, forest)
rasts
# subsetting
rasts[[1]]
rasts[["madForest2000"]]
# replacing
rasts[[2]] <- 2 * forest
rasts
# adding layers
rasts[[3]] <- elev > 500 # add a layer
rasts <- c(rasts, sqrt(elev)) # add another
add(rasts) <- ln(elev)
rasts
# names
names(rasts)
names(rasts) <- c("elev_meters", "2_x_forest", "high_elevation", "sqrt_elev", "ln_elev")
rasts
# remove a layer
rasts[["2_x_forest"]] <- NULL
rasts
# number of layers
nlyr(rasts)
# correlation and covariance matrices
madLANDSAT <- fastData("madLANDSAT")
landsat <- fast(madLANDSAT) # projects matrix
layerCor(landsat) # correlation
layerCor(landsat, fun = 'cov') # covariance
}