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Compute global statistics, that is summarized values of an entire SpatRaster.

If x is very large global can fail, except when fun is one of these built-in functions "mean", "min", "max", "sum", "prod", "range" (min and max), "rms" (root mean square), "sd" (sample standard deviation), "std" (population standard deviation), "isNA" (number of cells that are NA), "notNA" (number of cells that are not NA).

The reason that this can fail with large raster and a custom function is that all values need to be loaded into memory. To circumvent this problem you can run global with a sample of the cells.

You can compute a weighted mean or sum by providing a SpatRaster with weights.


# S4 method for SpatRaster
global(x, fun="mean", weights=NULL, maxcell=Inf, ...)





function to be applied to summarize the values by zone. Either as one or more of these built-in character values: "max", "min", "mean", "sum", "range", "rms" (root mean square), "sd", "std" (population sd, using n rather than n-1), "isNA", "notNA"; or a proper R function (but these may fail for very large SpatRasters unless you specify maxcell)


additional arguments passed on to fun


NULL or SpatRaster


positive integer used to take a regular sample of x. Ignored by the built-in functions.


A data.frame with a row for each layer

See also

zonal for "zonal" statistics, and app or Summary-methods for "local" statistics, and extract for summarizing values for polygons. Also see focal for "focal" or "moving window" operations.


r <- rast(ncols=10, nrows=10)
values(r) <- 1:ncell(r)
global(r, "sum")
#>        sum
#> lyr.1 5050
global(r, "mean", na.rm=TRUE)
#>       mean
#> lyr.1 50.5
x <- c(r, r/10)
global(x, c("sum", "mean", "sd"), na.rm=TRUE)
#>          sum  mean        sd
#> lyr.1   5050 50.50 29.011492
#> lyr.1.1  505  5.05  2.901149

global(x, function(i) min(i) / max(i))
#>         global
#> lyr.1     0.01
#> lyr.1.1   0.01