Paolo: at a first glance I was persuaded you would perform exploratory analysis between two sets.
The same concepts expressed by Roger Bivand about mapping lattice matrices apply to scatterplots.
In case you would perform exploratory analysis on the data I suggest hexagon binning to make quick plots of one var versus another one.
Then you could add regression and/or LOWESS lines to the plot: look at:
http://cran.r-project.org/web/packages/hexbin/index.html
Enjoy!
Duccio
Message: 3
Date: Wed, 22 Feb 2012 06:54:32 -0800 (PST)
From: Roger Bivand <Roger.Bivand@nhh.no>
Subject: [GRASS-user] Re: spgrass6
To: grass-user@lists.osgeo.org
Message-ID: <1329922472770-4495201.post@n6.nabble.com>
Content-Type: text/plain; charset=us-asciiJust 2k may mean 4 million rectangles. R display is vector, hard-copy, with
some recent support for raster grids when the rectangles are in fact square.
As has been said, the graphics engine is not designed for fast screen
output, but for scientific statistical graphics.spplot uses lattice graphics, which are slower anyway, but analytically more
powerful. For me running levelplot() - the internals of spplot - on a 2k by
2k matrix takes 2 seconds, but output to a png file using cairo takes 70
seconds.Using the improved raster graphics handling for square cells with image()
rather than spplot() and useRaster=TRUE - equivalent to
image.SpatialGridDataFrame() and useRasterImage=TRUE with the same matrix
takes 1.2 seconds on x11/cairo. You didn’t say which version of R you are
using - the raster graphics facilities have been improved recently.Did you try using image() instead of spplot() if your cells are square, and
if rasterImage() is available in your version of R?Roger