`S Programming' (see the FAQ) has a whole chapter with case studies. Beware that what is efficient under one version of S is not necessarily so under another, and that applies to R today vs R in 1999 (when those examples were done). However, the general principles are good for all time.
On Tue, 17 Feb 2004 [EMAIL PROTECTED] wrote: > I have been lurking in this list a while and searching in the archives to > find out how one learns to write fast R code. One solution seems to be to > write part of the code not in R but in C. However after finding a benchmark > article (http://www.sciviews.org/other/benchmark.htm) I have been more > interested in making the R code itself more efficient. I would like to find > more info about this. I have tried to mail the contact person for the > benchmark, but I have so recieved no reply. > > I am not an R programmer (or statistican) so I do not know R well. I am > looking for some advice about writing fast R code. What about the different > data types for example? Is there some good place to start to look for more > info about this? -- Brian D. Ripley, [EMAIL PROTECTED] 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html