levels: Set and get categories for categorical rasters

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

Set and get categories for categorical rasters

Description

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. The table must be NULL (i.e., no categories–so not a categorical raster), or have at least two columns. The first column must have integers and represent raster values. One or more subsequent columns must have category labels. The column with these labels is the "active category".

• levels(): Displays the "levels" table of a raster (just the value and active category columns).

• cats(): Displays the entire "levels" table of a raster.

• ⁠levels()<-⁠: (Re)assigns the "levels" table to each layer of a raster. Assigning a "levels" table to an integer raster makes it a categorical raster.

• categories(): (Re)assigns the "levels" table to specific layer(s) of a raster.

• For a complete list of functions relevant to categorical rasters, see 'vignette("GRasters", package = "fasterRaster")).

Usage

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

## S4 method for signature 'GRaster'
cats(x, layer = 1:nlyr(x))

## S4 method for signature 'GRaster'
categories(x, layer = 1, value, active = 1)

## S4 replacement method for signature 'GRaster,data.frame'
levels(x) <- value

## S4 replacement method for signature 'GRaster,data.table'
levels(x) <- value

## S4 replacement method for signature 'GRaster,GRaster'
levels(x) <- value

## S4 replacement method for signature 'GRaster,list'
levels(x) <- value

Arguments

x	A GRaster.
layer	Numeric integers, logical vector, or character: For cats() and categories(), this specifies the layer(s)for which to obtain level(s).
value	A data.frame, data.table, a list of data.frames or data.tables with one per raster layer, or a categorical SpatRaster. The table's first column is the "value" column and must contain numeric values (of class numeric or character). If a SpatRaster is supplied, then its categories will be transferred to the GRaster.
active	An integer or a character: The index or column name of the column used for category labels (the "active column"). Following terra::activeCat(), the first column of the "levels" table is ignored, so a value of 1 means to use the second column of the table for labels. A value of 2 means to use the third column, and so on.

Value

Values returned are:

• levels() and cats(): A list of data.frames or data.tables, one per raster layer.

• ⁠levels()<-⁠ and categories(): A GRaster.

See Also

terra::levels(), levels<-, terra::cats(), terra::categories(), see 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.