Johannes,

please provide your  sessionInfo()  which is particularly relevant for this
type (performance related) questions. I am guessing you are using a mac, as
there have been some similar reports with macs; apparently performance can
get a tremendous hit when RAM is exhausted and gets swapped to disk.

You have 60 rasters in memory, and then copy them into a RasterStack. To
avoid that, I think that Johannes Signer solution might indeed work, as you
could have a loop in which you use readRast to read a  raster, save it to
disk (e.g. 'grd' or 'tif') and keep the filenames in a vector or list. Then
make a stack using the filenames, and there should be no memory issue.

Robert

On Fri, Mar 2, 2012 at 4:37 AM, Johannes Radinger <JRadinger@gmx.at> wrote:

> Hi,
>
> I am not sure what you mean exactly with building a raster stack directly?
> My original raster files are GRASS GIS raster files in a GRASS GIS mapset.
> AFAIK these files can only be loaded with readRAST6 (with the result of a
> SpatialGridDataFrame).
>
> So I am not quite sure if you mean I have to use a differnet process to
> built the stack as my original files are no tifs (like the example).
>
> best regards,
> Johannes
>
> -------- Original-Nachricht --------
> > Datum: Fri, 2 Mar 2012 13:25:29 +0100
> > Von: Johannes Signer <j.m.signer@gmail.com>
> > An: Johannes Radinger <JRadinger@gmx.at>
> > CC: r-sig-geo@r-project.org
> > Betreff: Re: [R-sig-Geo] Performance SpatialGridDataFrame to rasterStack
>
> > Hello,
> >
> > not sure if it works for you, but why not directly build a raster
> > stack (example below).
> >
> > Hope this helps,
> > Johannes
> >
> > #
> >
> ----------------------------------------------------------------------------
> #
> > library(raster)
> >
> > # Create dummy data
> > r1 <- raster(nrows=1000, ncols=800, xmn=0, xmx=100)
> > r1 <- setValues(r1, rnorm(800000))
> >
> > # write dummy rasters
> > sapply(1:60, function(x) writeRaster(r1, paste("r", x, ".tif",
> > sep=""), overwrite=T))
> >
> > # Read read raster into a stack
> > system.time(r <- stack <- do.call("stack",
> > lapply(list.files(pattern="*.tif"), raster)))
> >
> > # on a 3 GB netbook this took just a bit more than 11 seconds
> >
> > On Fri, Mar 2, 2012 at 12:28 PM, Johannes Radinger <JRadinger@gmx.at>
> > wrote:
> > > Hi,
> > >
> > > I want to use raster maps for Species Distribution Modelling (SDM) with
> > > the package 'dismo'. Therefore I load several rastermaps (around 60)
> > with the readRAST6 command. For further processing I want to transform
> them
> > into a raster stack. Each of the raster has the same spatial extent and
> the
> > same resolution (per rastermap: around 765000 cells, thereof 760000 NA's,
> > 5000 non null cells).
> > >
> > > The readRAST6 command to load all rasters in one step into a
> > SpatialGridDataFrame works really good (1-2 min). The problem now is the
> tranformation
> > into a stack. This takes very long time (I waited for 45 min), the CPU is
> > mostly 100%, the pyhsical memory usage 3 GB (only a few MB left) and the
> > virtual memory usage for R 3 GB, the HDD activity 20-30 MB/Sec data
> written.
> > So my Computer seem to be at its maximum, and I don't have access to a
> > faster/better one. I am doint all my R processes within an Eclipse
> workspace.
> > >
> > > What I do:
> > > rast <- readRAST6(rast.list, cat=(FALSE,length(rast.list)),
> > ignore.stderr=TRUE, plugin=NULL)
> > >
> > > rast.stack <- stack(rast)
> > >
> > >
> > > Is there any way how I can improve the performance? As most of the
> > raster cells are NULL cells maybe there is a special way consider that?
> Or
> > splitting the SpatialGridDataFrame up and recombining the stacks in the
> end?
> > > Or saving substeps to the harddisk?
> > > Any help/suggestion is mostly welcome!
> > >
> > > Best regards,
> > > Johannes
> > >
> > > --
> > >
> > > Jetzt informieren: http://mobile.1und1.de/?ac=OM.PW.PW003K20328T7073a
> > >
> > > _______________________________________________
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> > > R-sig-Geo@r-project.org
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
> --
>
> Jetzt informieren: http://mobile.1und1.de/?ac=OM.PW.PW003K20328T7073a
>
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