This function reports the values in a categorical GRaster that have no matching category label in its "levels" table.
GRasters can represent categorical data. Cell values are actually integers, each corresponding to a category, such as "desert" or "wetland." A categorical raster is associated with a table that matches each value to a category name.
Usage
# S4 method for class 'GRaster'
missingCats(x, layer = 1:nlyr(x))Value
A numeric vector (if xis just one layer), or a named list of numeric vectors, one per layer in x.
See also
missingCats(), missing.cases(), droplevels(), categorical rasters in 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 <- combineCats(cover, elevCat)
combined
levels(combined)
# Combine levels, treating value/NA combinations as new categories:
combinedNA <- combineCats(cover, elevCat, na.rm = FALSE)
combinedNA
levels(combinedNA)
}