nlevels: Number of categories in a categorical raster

In adamlilith/fasterRaster: Faster Raster and Spatial Vector Processing Using 'GRASS GIS'

Number of categories in a categorical raster

Description

This function reports the number of categories (levels) in a categorical GRaster.

Usage

## S4 method for signature 'GRaster'
nlevels(x)

Arguments

x	A GRaster.

Value

A named, numeric vector of integers. The values represent the number of categories (rows) that appear in the raster's levels table.

See Also

levels(), terra::levels(), droplevels(), vignette("GRasters", package = "fasterRaster")

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
#############################################

# NB We only have one categorical raster ships with fasterRaster, so we
# will create a second one from the elevation raster.

# 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)

}

adamlilith/fasterRaster documentation built on Oct. 26, 2024, 4:06 p.m.