These functions allow for defining a RasterLayer as a categorical variable. Such a RasterLayer is linked to other values via a "Raster Attribute Table" (RAT). Thus the cell values are an index, whereas the actual values of interest are in the RAT. The RAT is a data.frame. The first column in the RAT ("ID") has the unique cell values of the layer; this column should normally not be changed. The other columns can be of any basic type (factor, character, integer, numeric or logical). The functions documented here are mainly available such that files with a RAT can be read and processed; currently there is not too much further support. Whether a layer is defined as a factor or not is currently ignored by almost all functions. An exception is the 'extract' function (when used with option df=TRUE).
Function 'levels' returns the RAT for inspection. It can be modified and set using levels <- value (but use caution as it is easy to mess things up).
as.factor and ratify create a layer with a RAT table. Function 'deratify' creates a single layer for a (or each) variable in the RAT table.
methods
layer: integer > 0 indicating which layer to use (in a RasterStack or RasterBrick)
att: numeric or character. Which variable(s) in the RAT table should be used. If NULL, all variables are extracted. If using a numeric, skip the first two default columns
append.names: logical. Should names of data.frame returned by a combination of the name of the layer and the RAT variables? (can be useful for multilayer objects
filename: character. Optional
count: logical. If TRUE, a columns with frequencies is added
...: additional arguments as for writeRaster
complete: logical. If TRUE, the layer returned is no longer a factor
drop: logical. If TRUE a factor is converted to a numerical value if possible
fun: character. Used to get a single value for each class for a weighted RAT table. 'mean', 'min', 'max', 'smallest', or 'largest'
Returns
Raster* object; list (levels); boolean (is.factor); matrix (factorValues)
Note
asFactor is deprecated and should not be used
Examples
set.seed(0)r <- raster(nrow=10, ncol=10)values(r)<- runif(ncell(r))*10is.factor(r)r <- round(r)f <- as.factor(r)is.factor(f)x <- levels(f)[[1]]x
x$code <- letters[10:20]levels(f)<- x
levels(f)f
r <- raster(nrow=10, ncol=10)values(r)=1r[51:100]=2r[3:6,1:5]=3r <- ratify(r)rat <- levels(r)[[1]]rat$landcover <- c("Pine","Oak","Meadow")rat$code <- c(12,25,30)levels(r)<- rat
r
# extract values for some cellsi <- extract(r, c(1,2,25,100))i
# get the attribute values for these cellsfactorValues(r, i)# write to file:# rr <- writeRaster(r, rasterTmpFile(), overwrite=TRUE)# rr# create a single-layer factor x <- deratify(r,"landcover")x
is.factor(x)levels(x)