raster (version 2.3-40)

aggregate: Aggregate raster cells or polygons

Description

Raster* objects: Aggregate a Raster* object to create a new RasterLayer or RasterBrick with a lower resolution (larger cells). Aggregation groups rectangular areas to create larger cells. The value for the resulting cells is computed with a user-specified function. SpatialPolygons: Aggregate ('dissolve') SpatialPolygons, optionally by combining polygons that have the same attributes for one or more variables.

Usage

## S3 method for class 'Raster':
aggregate(x, fact=2, fun=mean, expand=TRUE, na.rm=TRUE, filename='', ...)

## S3 method for class 'SpatialPolygons':
aggregate(x, vars=NULL, sums, dissolve=TRUE, ...)

Arguments

x

Raster* object or SpatialPolygons* object

fact

integer. Aggregation factor expressed as number of cells in each direction (horizontally and vertically). Or two integers (horizontal and vertical aggregation factor) or three integers (when also aggregating over layers). See Details

fun

function used to aggregate values

expand

logical. If TRUE the output Raster* object will be larger then the input Raster* object if a division of the number of columns or rows with factor is not an integer

na.rm

logical. If TRUE, NA cells are removed from calculations

filename

character. Output filename (optional)

...

if x is a Raster* object, additional arguments as for writeRaster

vars

character or integer. The variables (column names or numbers) that should be used to aggregate (dissolve) the SpatialPolygons by only maintaining unique combinations of these variables. The default setting is to use no variables and aggregate all polygons

sums

list with function(s) and variable(s) to summarize. This should be a list of lists in which each element of the main lists has two items. The first item is function (e.g. mean), the second element is a vector of column names (or indices) that need to summ

dissolve

logical. If TRUE overlapping polygons are dissolved into single features (requires package rgeos)

Value

• RasterLayer or RasterBrick, or a SpatialPolygons* object

Details

Aggregation of a x will result in a Raster* object with fewer cells. The number of cells is the number of cells of x divided by fact*fact (when fact is a single number) or prod(fact) (when fact consists of 2 or 3 numbers). If necessary this number is adjusted according to the value of expand. For example, fact=2 will result in a new Raster* object with 2*2=4 times fewer cells. If two numbers are supplied, e.g., fact=c(2,3), the first will be used for aggregating in the horizontal direction, and the second for aggregating in the vertical direction, and the returned object will have 2*3=6 times fewer cells. Likewise, fact=c(2,3,4) aggregates cells in groups of 2 (rows) by 3 (columns) and 4 (layers). Aggregation starts at the upper-left end of a raster (you can use flip if you want to start elsewhere). If a division of the number of columns or rows with factor does not return an integer, the extent of the resulting Raster object will either be somewhat smaller or somewhat larger then the original RasterLayer. For example, if an input RasterLayer has 100 columns, and fact=12, the output Raster object will have either 8 columns (expand=FALSE) (using 8 x 12 = 96 of the original columns) or 9 columns (expand=TRUE). In both cases, the maximum x coordinate of the output RasterLayer would, of course, also be adjusted. The function fun should take multiple numbers, and return a single number. For example mean, modal, min or max. It should also accept a na.rm argument (or ignore it as one of the 'dots' arguments).

See Also

disaggregate, resample. For SpatialPolygons* disaggregate

Examples

r <- raster()
# a new aggregated raster, no values
ra <- aggregate(r, fact=10)
r <- setValues(r, runif(ncell(r)))

# a new aggregated raster, max of the values
ra <- aggregate(r, fact=10, fun=max)

# multiple layers
s <- stack(r, r*2)
x <- aggregate(s,2)

#SpatialPolygons
if (require(rgdal) & require(rgeos)) {
	p <- shapefile(system.file("external/lux.shp", package="raster"))
	p
	pa0 <- aggregate(p)
	pa0
	pa1 <- aggregate(p, vars='NAME_1', sums=list(list(mean, 'ID_2')))
	pa1
}