Combine values/categories of multiple GRasters into a single GRaster

This function takes from 2 to 10 integer or categorical (factor) GRasters and creates a single GRaster that has one value per combination of values in the inputs. For example, say that there were two input rasters, with values 1 and 2 in the one raster, and 3 and 4 in the other. If the following combinations of values occurred between the two rasters, then the output raster would be re-coded with the new values:

`input_raster1`	`input_raster2`	`output_raster`
1	3	0
1	4	1
2	3	2
2	4	3

If the argument na.rm is set to TRUE (which it is, by default), then whenever at least one cell has an NA value, then the output will also have an NA (i.e., a new category number is not created). However, if na.rm is FALSE, then combinations that include an NA are assigned a new category number, unless all values are NA (in which case the output will be NA).

The difference between this function and combineLevels() is that this one creates a "combined" GRaster with a combined levels table, whereas combineLevels() just merges the levels tables.

If the inputs are all categorical rasters, then a levels() table will also be returned with the new levels.

Usage

# S4 method for class 'GRaster'
concats(x, ..., na.rm = TRUE)

Arguments

x: A GRaster with one or more layers, each of which must be have cells that represent integers or categories (factors).
...: Either missing or integer/categorical (factor) GRasters.
na.rm: Logical: If TRUE (default), then any combinations that include an NA cell will result in an NA cell in the output.

Value

A GRaster. If the inputs are all categorical (factor) rasters, then a levels table will also be returned with the new combined levels.

Examples

if (grassStarted()) {

# Setup
library(terra)

# Example data: Land cover raster
madCover <- fastData("madCover")

# Convert categorical SpatRaster to categorical GRaster:
cover <- fast(madCover)

### Properties of categorical rasters

cover # note categories
is.factor(cover) # Is the raster categorical?
nlevels(cover) # number of levels
levels(cover) # just the value and active column
cats(cover) # all columns
minmax(cover) # min/max values
minmax(cover, levels = TRUE) # min/max categories
catNames(cover) # column names of the levels table
missingCats(cover) # categories in table with no values in raster
freq(cover) # frequency of each category (number of cells)
zonalGeog(cover) # geometric statistics

### Active column

# Which column sets the category labels?
activeCat(cover)
activeCat(cover, names = TRUE)

activeCats(c(cover, cover))

# Choose a different column for category labels:
levels(cover)
activeCat(cover) <- 2
levels(cover)

### Managing levels tables

# Remove unused levels:
nlevels(cover)
cover <- droplevels(cover)
nlevels(cover)

# Re-assign levels:
value <- c(20, 30, 40, 50, 120, 130, 140, 170)
label <- c("Cropland", "Cropland", "Forest", "Forest",
 "Grassland", "Shrubland", "Herbaceous", "Flooded")

newCats <- data.frame(value = value, label = label)

cover <- categories(cover, layer = 1, value = newCats)
cats(cover)

# This is the same as:
levels(cover) <- newCats
cats(cover)

# Are there any values not assigned a category?
missingCats(cover)

# Let's assign a category for value 210 (water):
water <- data.frame(value = 210, label = "Water")
addCats(cover) <- water
levels(cover)

# Add more information to the levels table using merge():
landType <- data.frame(
     Value = c(20, 30, 40, 50, 120),
     Type = c("Irrigated", "Rainfed", "Broadleaf evergreen",
     "Broadleaf deciduous", "Mosaic with forest")
)
cats(cover)
cover <- addCats(cover, landType, merge = TRUE)
cats(cover)

### Logical operations on categorical rasters

cover < "Forest" # 1 for cells with a value < 40, 0 otherwise
cover <= "Forest" # 1 for cells with a value < 120, 0 otherwise
cover == "Forest" # 1 for cells with value of 40-120, 0 otherwise
cover != "Forest" # 1 for cells with value that is not 40-120, 0 otherwise
cover > "Forest" # 1 for cells with a value > 120, 0 otherwise
cover >= "Forest" # 1 for cells with a value >= 120, 0 otherwise

cover %in% c("Cropland", "Forest") # 1 for cropland/forest cells, 0 otherwise

### Combine categories from different rasters

# For the example, will create a second categorical raster fromm elevation.

# Divide elevation raster into "low/medium/high" levels:
madElev <- fastData("madElev")
elev <- fast(madElev)
elev <- project(elev, cover, method = "near") # convert to same CRS
fun <- "= if(madElev < 100, 0, if(madElev < 400, 1, 2))"
elevCat <- app(elev, fun)

levs <- data.frame(
     value = c(0, 1, 2),
     elevation = c("low", "medium", "high")
)
levels(elevCat) <- list(levs)

# Combine levels:
combined <- concats(cover, elevCat)
combined
levels(combined)

# Combine levels, treating value/NA combinations as new categories:
combinedNA <- concats(cover, elevCat, na.rm = FALSE)
combinedNA
levels(combinedNA)

}

Combine values/categories of multiple GRasters into a single GRaster

Usage

Arguments

Value

See also

Examples