[R-sig-Geo] raster: stackApply problems..

[Vijay Lulla]

If you read the code/help for `stackApply` and `zApply` you'll see that the
results that you obtain make sense (at least they seem sensible/reasonable
to me).  IMO, if you want to control the ordering of your layers then just
use sapply, like how you've used for ver_mean.  IMO, this is the only
reliable (safe?), and quite a readable, way to accomplish what you're
trying to do.
Just my 2 cents.
-- Vijay.

On Tue, Nov 26, 2019 at 11:19 AM Leonidas Liakos via R-sig-Geo <
r-sig-geo using r-project.org> wrote:

> I added raster::zApply in my tests to validate the results. However, the
> indices of the names of the results are different now. Recall that the
> goal is to calculate from a raster stack time series the mean per day of
> the week. And that problem I have is that stackApply, zApply and
> calc/sapply return different indices in the result names. New code is
> available here:
> https://gist.github.com/kokkytos/93f315a5ecf59c0b183f9788754bc170
> I'm really curious about missing something.
>
>
> On 11/20/19 3:30 AM, Frederico Faleiro wrote:
> > Hi Leonidas,
> >
> > both results are in the same order, but the name is different.
> > You can rename the first as in the second:
> > names(res) <- names(res2)
> >
> > I provided an example to help you understand the logic.
> >
> > library(raster)
> > beginCluster(2)
> > r <- raster()
> > values(r) <- 1
> > # simple sequential stack from 1 to 6 in all cells
> > s <- stack(r, r*2, r*3, r*4, r*5, r*6)
> > s
> > res <- clusterR(s, stackApply, args = list(indices=c(2,2,3,3,1,1), fun
> > = mean))
> > res
> > res2 <- stackApply(s, c(2,2,3,3,1,1), mean)
> > res2
> > dif <- res - res2
> > # exatly the same order because the difference is zero for all layers
> > dif
> > # rename
> > names(res) <- names(res2)
> >
> > Best regards,
> >
> > Frederico Faleiro
> >
> > On Tue, Nov 19, 2019 at 4:15 PM Leonidas Liakos via R-sig-Geo
> > <r-sig-geo using r-project.org <mailto:r-sig-geo using r-project.org>> wrote:
> >
> >     I run the example with clusterR:
> >
> >     no_cores <- parallel::detectCores() -1
> >     raster::beginCluster(no_cores)
> >     ?????? res <- raster::clusterR(inp, raster::stackApply, args =
> >     list(indices=c(2,2,3,3,1,1),fun = mean))
> >     raster::endCluster()
> >
> >     And the result is:
> >
> >     > res
> >     class?????????? : RasterBrick
> >     dimensions : 180, 360, 64800, 3?? (nrow, ncol, ncell, nlayers)
> >     resolution : 1, 1?? (x, y)
> >     extent???????? : -180, 180, -90, 90?? (xmin, xmax, ymin, ymax)
> >     crs?????????????? : +proj=longlat +datum=WGS84 +ellps=WGS84
> >     +towgs84=0,0,0
> >     source???????? : memory
> >     names?????????? : layer.1, layer.2, layer.3
> >     min values :???????? 1.5,???????? 3.5,???????? 5.5
> >     max values :???????? 1.5,???????? 3.5,???????? 5.5??
> >
> >
> >     layer.1, layer.2, layer.3 (?)
> >
> >     So what corrensponds to what?
> >
> >
> >     If I run:
> >
> >     res2 <- stackApply(inp,c(2,2,3,3,1,1),mean)
> >
> >     The result is:
> >
> >     > res2
> >     class      : RasterBrick
> >     dimensions : 180, 360, 64800, 3  (nrow, ncol, ncell, nlayers)
> >     resolution : 1, 1  (x, y)
> >     extent     : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
> >     crs        : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
> >     source     : memory
> >     names      : index_2, index_3, index_1
> >     min values :     1.5,     3.5,     5.5
> >     max values :     1.5,     3.5,     5.5
> >
> >     There is no consistency with the names of the output and obscure
> >     correspondence with the indices in the case of clusterR
> >
> >
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> >
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