minmax() reports the minimum and maximum values across all non-NA cells of a GRaster. When the levels argument is TRUE and the raster is categorical, the function reports the "lowest" and "highest" category values in a categorical (factor) GRaster.
Value
minmax() returns a numeric matrix, and minmax(..., levels = TRUE) returns a data.frame with category names. In the latter case, non-categorical rasters will have NA 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)
coordRef(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
}