as.int,GRaster-method | R Documentation |
In fasterRaster, rasters can have three data types: "factor" (categorical rasters), "integer" (integers), "float" (floating point values, accurate to 6th to 9th decimal places), and "double" (double-precision values, accurate to the 15th to 17th decimal places). The type of raster can be checked with:
as.int()
: Coerce values to integers (GRASS type CELL
).
as.float()
: Coerce values to floating-point precision.
as.doub()
: Coerce values to double-floating point precision.
Integer rasters can be converted categorical rasters by adding "levels" tables with levels<-
or categories()
.
## S4 method for signature 'GRaster'
as.int(x)
## S4 method for signature 'GRaster'
as.float(x)
## S4 method for signature 'GRaster'
as.doub(x)
x |
A |
A GRaster
.
datatype()
, terra::datatype()
, is.int()
, is.float()
, is.doub()
, levels<-
, vignette("GRasters", package = "fasterRaster")
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
}
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