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
major1
minor7.0
year 2003
month04
day 16
language R
Thank you for your time.
Helen
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