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Create one GRaster layer per unique value in a GRaster

Source: R/segregate.r
segregate.Rd

This function creates a multi-layered GRaster for every unique values in an input GRaster. By default, the output will have a value of 1 wherever the input has the given value, and 0 elsewhere. This is useful for creating dummy variable GRaster layers for use with models that have factors, especially if the input GRaster is categorical. Note that the predict() function in fasterRaster usually does not need this treatment of GRasters since it can handle categorical rasters already.

Usage

# S4 method for class 'GRaster'
segregate(x, classes = NULL, keep = FALSE, other = 0, bins = 100, digits = 3)

Arguments

x

A GRaster.

classes

Either NULL (default) or a character vector with category labels for which to create outputs. If the input is not a categorical/factor or integer GRaster, this is ignored.

keep

Logical: If FALSE (default), then the original value in the input GRaster will be retained in the each of the output GRaster layers wherever the input had the respective value. Other cells will be assigned a value of other.

other

Numeric or NA: Value to assign to cells that do not have the target value.

bins

Numeric: Number of bins in which to put values. This is only used for GRasters that are not categorical/factor rasters or integer rasters.

digits

Numeric: Number of digits to which to round input if it is a numeric or double GRaster (see vignettes("GRasters", package = "fasterRaster")).

Value

If the input x is a single-layered GRaster, the output will be a multi-layered GRaster with one layer per value in the input, or one layer per values in classes. If the input is a multi-layered GRaster, the output will be a list of multi-layered GRasters.

See also

terra::segregate()

Examples

if (grassStarted()) {

# Setup
library(terra)

# Elevation and land cover raster
madElev <- fastData("madElev") # integer raster
madCover <- fastData("madCover") # categorical raster

# Convert to GRasters
elev <- fast(madElev)
cover <- fast(madCover)

# Subset elevation raster to just a few values to make example faster:
elevSubset <- elev[elev <= 3]
segregate(elevSubset)
segregate(elevSubset, keep = TRUE, other = -1)

# Segregate the factor raster
segregate(cover)

classes <- c("Grassland with mosaic forest", "Mosaic cropland/vegetation")
seg <- segregate(cover, classes = classes)
plot(seg)

}