Eduardo: On Wed, Nov 17, 2010 at 9:53 AM, Eduardo de Oliveira Horta <eduardo.oliveiraho...@gmail.com> wrote: > Thanks. > > I wanted to avoid loops (even inside functions) because I thought they were > much slower than vector operations, and I really need an efficient code (not > an exercise!).
This is a common miconception. What is true is that (internally) vectorized functions -- which are loops in the **compiled C or Fortran code -- are much faster than interpreted (at the R level) loops. So, for example: > system.time (y <- sin(seq_len(1e6))) user system elapsed 0.14 0.00 0.14 > y <- vector(length=1e6) > system.time (for(i in seq_len(1e6))y[i] <- sin(i)) user system elapsed 4.84 0.02 4.88 But Reduce(), Vectorize, and anything in the apply family **are** (disguised) loops at the interpreted level (albeit done efficiently). They have to be, since they are designed to work with arbitrary user provided functions. So, continuing the example above, caclulating the signs with sapply is no more efficient than using the explicit loop: > system.time(sapply(seq_len(1e6),sin)) user system elapsed 4.54 0.05 4.59 R also permits direct recursive implementation of loops; but because of the heavy internal bookkeeping involved, this tends to be still more inefficient. Finally, IMO Bill Dunlap's pronouncements on R matters should always be viewed as authoritative and generally also recommended R programming practice. Therefore anything I say that conflicts with his comments is wrong. Cheers, Bert Bert Gunter Genentech Nonclinical statistics ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
