Description Usage Arguments Details Value See Also Examples

Apply a function to the values of each cell of a SpatRaster. Similar to `apply`

– think of each layer in a SpatRaster as a column (or row) in a matrix.

This is generally used to summarize the values of multiple layers into one layer; but this is not required.

`app`

calls function `fun`

with the raster data as first argument. Depending on the function supplied, the raster data is represented as either a matrix in which each layer is a column, or a vector representing a cell. The function should return a vector or matrix that is divisible by ncell(x). Thus, both "sum" and "rowSums" can be used, but "colSums" cannot be used.

You can also apply a function `fun`

across datasets by layer of a `SpatRasterDataset`

. In that case, summarization is across SpatRasters, not across layers.

1 2 3 4 5 |

`x` |
SpatRaster or SpatRasterDataset |

`fun` |
a function that operates on a vector or matrix. This can be a function that is defined in base-R or in a package, or a function you write yourself (see examples). Functions that return complex output (e.g. a list) may need to be wrapped in your own function to simplify the output to a vector or matrix. The following functions have been re-implemented in C++ for speed: "sum", "mean", "median", "modal", "which", "which.min", "which.max", "min", "max", "prod", "any", "all", "sd", "std", "first". To use the base-R function for say, "min", you could use something like |

`...` |
additional arguments for |

`cores` |
positive integer. If |

`filename` |
character. Output filename |

`overwrite` |
logical. If |

`wopt` |
list with named options for writing files as in |

To speed things up, parallelization is supported, but this is often not helpful, and it may actually be slower. There is only a speed gain if you have many cores (> 8) and/or a very complex (slow) function `fun`

. If you write `fun`

yourself, consider supplying a `cppFunction`

made with the Rcpp package instead (or go have a cup of tea while the computer works for you).

SpatRaster

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
r <- rast(ncols=10, nrows=10)
values(r) <- 1:ncell(r)
x <- c(r, sqrt(r), r+50)
s <- app(x, fun=sum)
s
# for a few generic functions like
# "sum", "mean", and "max" you can also do
sum(x)
## SpatRasterDataset
sd <- sds(x, x*2, x/3)
a <- app(sd, max)
a
# same as
max(x, x*2, x/3)
## also works for a single layer
f <- function(i) (i+1) * 2 * i + sqrt(i)
s <- app(r, f)
# same as above, but that is not memory-safe
# and has no filename argument
s <- f(r)
## Not run:
#### multiple cores
test0 <- app(x, sqrt)
test1 <- app(x, sqrt, cores=2)
testfun <- function(i) { 2 * sqrt(i) }
test2 <- app(x, fun=testfun, cores =2)
## this fails because testfun is not exported to the nodes
# test3 <- app(x, fun=function(i) testfun(i), cores=2)
## to export it, add it as argument to fun
test3 <- app(x, fun=function(i, ff) ff(i), cores =3, ff=testfun)
## End(Not run)
``` |

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