Hi all, I am a beginner trying to use R to work with large amounts of oceanographic data, and I find that computations can be VERY slow. In particular, computational speed seems to depend strongly on the number and size of the objects that are loaded (when R starts up). The same computations are significantly faster when all but the essential objects are removed. I am running R on a machine with 16 GB of RAM, and our unix system manager assures me that there is memory available to my R process that has not been used.
1. Is the problem associated with how R uses memory? If so, is there some way to increase the amount of memory used by my R process to get better performance? The computations that are particularly slow involve looping with by(). The data are measurements of vertical profiles of pressure, temperature, and salinity at a number of stations, which are organized into a dataframe p.1 (1925930 rows, 8 columns: id, p, t, and s, etc.), and the objective is to get a much smaller dataframe and the unique values for ID is 1409 with the minimum and maximum pressure for each profile. The slow part is: h.maxmin <- by(p.1,p.1$id,function(x){ data.frame(id=x$id[1], maxp=max(x$p), minp=min(x$p))}) 2. Even with unneeded data objects removed, this is very slow. Is there a faster way to get the maximum and minimum values? platform sparc-sun-solaris2.9 arch sparc os solaris2.9 system sparc, solaris2.9 status major 1 minor 7.0 year 2003 month 04 day 16 language R Thank you for your time. Helen ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help