Message-ID: <Pine.LNX.4.44.0411051813200.24480-100000@gannet.stats>
Date: 2004-11-05T18:17:07Z
From: Brian Ripley
Subject: Resources for optimizing code
In-Reply-To: <Pine.LNX.4.44.0411051850350.17589-100000@reclus.nhh.no>
On Fri, 5 Nov 2004, Roger Bivand wrote:
> On Fri, 5 Nov 2004, Janet Elise Rosenbaum wrote:
>
> >
> > I want to eliminate certain observations in a large dataframe (21000x100).
> > I have written code which does this using a binary vector (0=delete obs,
> > 1=keep), but it uses for loops, and so it's slow and in the extreme it
> > causes R to hang for indefinite time periods.
> >
> > I'm looking for one of two things:
> > 1. A document which discusses how to avoid for loops and situations in
> > which it's impossible to avoid for loops.
> >
> > or
> >
> > 2. A function which can do the above better than mine.
>
> ?subset
> newdata <- subset(DATAFRAME, asst==1)
>
> which will work whether DATAFRAME is a matrix or data.frame (two different
> classes).
Sorry, not for matrices:
> A <- matrix(1:20, 5)
> asst <- c(1,0,0,1,0)
> subset(A, asst)
[1] 1 4 6 9 11 14 16 19
Maybe it should, but in biggish problems like this it is almost certainly
a bit more efficient to use the bare tools, that is indexing.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595