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.
Aggregate a SpatialPolygon* object, optionally by combining polygons that have the same attributes for one or more variables. If the polygons touch or overlap, internal boundaries are optionally "dissolved".
# S4 method for Raster aggregate(x, fact, fun=mean, expand=TRUE, na.rm=TRUE, filename='', ...) # S4 method for SpatialPolygons aggregate(x, by, sums, dissolve=TRUE, vars=NULL, ...)
Raster* object or SpatialPolygons* object
postive 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
function used to aggregate values
TRUE the output Raster* object will be larger than the input Raster* object if a division of the number of columns or rows with
factor is not an integer
TRUE, NA cells are removed from calculations
character. Output filename (optional)
x is a Raster* object, additional arguments as for
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. You can also supply a vector with a length of length(x)
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 summarize with that function. Be careful with character and factor variables (you can use, e.g. 'first'
function(x)x or 'last'
modal for these variables
deprecated. Same as
TRUE borders between touching or overlapping polygons are removed
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 than the original RasterLayer. For example, if an input RasterLayer has 100 columns, and
fact=12, the output Raster object will have either 8 columns (
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.
fun should take multiple numbers, and return a single number. For example
It should also accept a
na.rm argument (or ignore it as one of the 'dots' arguments).
RasterLayer or RasterBrick, or a SpatialPolygons* object
resample. For SpatialPolygons*
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 p <- shapefile(system.file("external/lux.shp", package="raster")) p #> class : SpatialPolygonsDataFrame #> features : 12 #> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax) #> crs : +proj=longlat +datum=WGS84 +no_defs #> variables : 5 #> names : ID_1, NAME_1, ID_2, NAME_2, AREA #> min values : 1, Diekirch, 1, Capellen, 76 #> max values : 3, Luxembourg, 12, Wiltz, 312 pa0 <- aggregate(p) pa0 #> class : SpatialPolygons #> features : 1 #> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax) #> crs : +proj=longlat +datum=WGS84 +no_defs pa1 <- aggregate(p, by='NAME_1', sums=list(list(mean, 'ID_2'))) pa1 #> class : SpatialPolygonsDataFrame #> features : 3 #> extent : 5.74414, 6.528252, 49.44781, 50.18162 (xmin, xmax, ymin, ymax) #> crs : +proj=longlat +datum=WGS84 +no_defs #> variables : 2 #> names : NAME_1, ID_2 #> min values : Diekirch, 3 #> max values : Luxembourg, 9.5