st_apply apply a function to array dimensions: aggregate over space, time, or something else

```
# S3 method for stars
st_apply(
X,
MARGIN,
FUN,
...,
CLUSTER = NULL,
PROGRESS = FALSE,
FUTURE = FALSE,
rename = TRUE,
.fname,
single_arg = has_single_arg(FUN, list(...)) || can_single_arg(FUN),
keep = FALSE
)
```

- X
object of class

`stars`

- MARGIN
see apply; index number(s) or name(s) of the dimensions over which

`FUN`

will be applied- FUN
see apply and see Details.

- ...
arguments passed on to

`FUN`

- CLUSTER
cluster to use for parallel apply; see makeCluster

- PROGRESS
logical; if

`TRUE`

, use`pbapply::pbapply`

to show progress bar- FUTURE
logical;if

`TRUE`

, use`future.apply::future_apply`

- rename
logical; if

`TRUE`

and`X`

has only one attribute and`FUN`

is a simple function name, rename the attribute of the returned object to the function name- .fname
function name for the new attribute name (if one or more dimensions are reduced) or the new dimension (if a new dimension is created); if missing, the name of

`FUN`

is used- single_arg
logical; if

`TRUE`

, FUN takes a single argument (like`fn_ndvi1`

below), if`FALSE`

FUN takes multiple arguments (like`fn_ndvi2`

below).- keep
logical; if

`TRUE`

, preserve dimension metadata (e.g. time stamps)

object of class `stars`

with accordingly reduced number of dimensions;
in case `FUN`

returns more than one value, a new dimension is created carrying
the name of the function used; see the examples. Following the logic of
apply, This new dimension is put before the
other dimensions; use aperm to rearrange this, see last example.

FUN is a function which either operates on a single object, which will be the data of each iteration step over dimensions MARGIN, or a function that has as many arguments as there are elements in such an object. See the NDVI examples below. The second form can be VERY much faster e.g. when a trivial function is not being called for every pixel, but only once (example).

The heuristics for the default of `single_arg`

work often, but not always; try
setting this to the right value when `st_apply`

gives an error.

```
tif = system.file("tif/L7_ETMs.tif", package = "stars")
x = read_stars(tif)
st_apply(x, 1:2, mean) # mean band value for each pixel
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> mean 25.5 53.33333 68.33333 68.91242 82 255
#> dimension(s):
#> from to offset delta refsys point x/y
#> x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
#> y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
st_apply(x, c("x", "y"), mean) # equivalent to the above
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> mean 25.5 53.33333 68.33333 68.91242 82 255
#> dimension(s):
#> from to offset delta refsys point x/y
#> x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
#> y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
st_apply(x, 3, mean) # mean of all pixels for each band
#> stars object with 1 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> mean 59.23541 61.07112 65.96675 68.91242 76.25445 83.18266
#> dimension(s):
#> from to
#> band 1 6
if (FALSE) {
st_apply(x, "band", mean) # equivalent to the above
st_apply(x, 1:2, range) # min and max band value for each pixel
fn_ndvi1 = function(x) (x[4]-x[3])/(x[4]+x[3]) # ONE argument: will be called for each pixel
fn_ndvi2 = function(red,nir) (nir-red)/(nir+red) # n arguments: will be called only once
ndvi1 = st_apply(x, 1:2, fn_ndvi1)
# note that we can select bands 3 and 4 in the first argument:
ndvi2 = st_apply(x[,,,3:4], 1:2, fn_ndvi2)
all.equal(ndvi1, ndvi2)
# compute the (spatial) variance of each band; https://github.com/r-spatial/stars/issues/430
st_apply(x, 3, function(x) var(as.vector(x))) # as.vector is required!
# to get a progress bar also in non-interactive mode, specify:
if (require(pbapply)) { # install it, if FALSE
pboptions(type = "timer")
}
st_apply(x, 1:2, range) # dimension "range" is first; rearrange by:
st_apply(x, 1:2, range) %>% aperm(c(2,3,1))
}
```