fasterRaster GRasters can represent
double-floating point numeric values, integers, or categorical/factor
data.
Double-floating point values are accurate to about the 15th to 17th
decimal place. These are called “double” rasters in
fasterRaster and DCELL rasters in
GRASS. These rasters typically take the most memory.
All numeric values in R are
double-floating point values.
Less common that double-floating point rasters, floating point
rasters are accurate to about the 7th decimal place. These are called
“float” rasters in fasterRaster and FCELL
rasters in GRASS. These rasters typically take less
memory than double-floating point rasters.
Rasters that represent integers are called “integer” rasters in
fasterRaster and CELL rasters in
GRASS. You can force a raster to be an integer using as.int().
Some of the functions in app()
function will also return integer-type rasters. Integer rasters
typically take the least memory.
Categorical rasters (also called “factor” rasters) are actually
integer rasters, but have an associated attribute table that maps each
integer value to a category label, such as “wetland” or “forest”. The
table has at least two columns. The first is integer values, and (by
default) the second is category names. This second column is the
“active” category column that is used for plotting and in some
functions. The active column can be changed using activeCat<-.
GRasteras.int(),
as.float(),
and as.doub()
coerce a raster to an integer, float, or double.datatype()
returns the data type of a GRaster.freq():
Frequency of each category across cells of a rasteris.factor()
indicates if the raster is a categorical raster.is.int(),
is.float(),
and is.doub()
indicate if values in a a raster are integers, floating-point, or
double-floating point precision.GRastersactiveCat(),
activeCats(),
and activeCat<-
can be used to see or assign which column in a “levels” table associated
with a categorical raster is used as category labels.addCats()
adds information to the “levels” table using
data.table::merge() (same as merge()).addCats<-
add new levels to a “levels” table.catNames()
reports the column names of the “levels” table of each layer of a
raster.cats()
returns the entire “levels” table of a categorical raster.combineLevels():
Combine the “levels” tables of two or more categorical
GRasters.complete.cases()
finds rows in the levels table that have no NAs.concats()
combines levels of two or more categorical or integer rasters by
concatenating them.droplevels()
removes “unused” levels in a “levels” table.levels()
returns the “levels” table of a categorical raster (just the value
column and the active column).levels<-
and categories()
can be used to assign categories to an integer raster and make it
categorical (i.e., a “factor” raster).match(),
%in%,
and $%notin%$:
Find which cells of a GRaster match or do not match certain
category labelsmissing.cases()
finds rows in the levels table that have at least one
NA.missingCats()
finds values in categorical rasters that do not have a category assigned
to them.nlevels()
returns the number of levels represented by a categorical raster.subst():
Re-assign category levelsYou can potentially save substantial space on disk by setting the
datatype argument in writeRaster()
to an appropriate value when saving a raster. This argument allows for
finer “divisions” than just integer/float/double-float, so depending on
the range of values in your raster, you can optimize file size by
selecting the one that best matches the values in the raster. See the
documentation for writeRaster()
for more information.
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