Thanks Jeff, and William.
@William: your example is terrific, very clear. I love it, thanks!
Cheers,
Mike
On Sun, Apr 7, 2013 at 7:52 PM, William Dunlap <wdun...@tibco.com> wrote:
> > I am aware of the apply() functions,
> > but they are wrapper function of for loops, so they are slower.
>
> While this sounds right in the abstract, it isn't always so. Many times
> looping code is slow not because of the function calls of interest but
> because of how the memory used to store the result is managed and
> other details in setting up the loop. E.g., compare the following loops:
> > system.time({ x1 <- numeric(0) ; for(i in 1:50000) x1[i] <- log(i) })
> user system elapsed
> 2.34 0.03 2.37
> > system.time(x2 <- sapply(1:50000, log))
> user system elapsed
> 0.08 0.00 0.08
> > system.time({ x3 <- numeric(50000) ; for(i in 1:50000) x3[i] <- log(i)
> })
> user system elapsed
> 0.11 0.00 0.11
> identical() shows that all those give the same results. Using sapply()
> means
> that you don't have to remember the rule about allocating the output vector
> ahead of time, thus simplifying your code.
>
> The built-in apply functions are also compiled, which saves some time.
> You can
> compile your own functions:
> > library(compiler)
> > f5 <- function(n,FUN) { x <- numeric(n) ; for(i in seq_len(n)) x[i] <-
> FUN(i) ; x }
> > f5_compiled <- cmpfun(f5)
> > system.time( x5 <- f5(50000, log) )
> user system elapsed
> 0.09 0.00 0.09
> > system.time( x5_compiled <- f5_compiled(50000, log) )
> user system elapsed
> 0.02 0.00 0.01
>
> The built-in vapply() allows you to specify the type (and size) of the
> return value
> of FUN, which can save time also (that was also part of the speedup of
> f5() above).
> > system.time(x6 <- vapply(1:50000, log, FUN.VALUE=0.0))
> user system elapsed
> 0.06 0.00 0.06
> vapply() also checks that FUN(X[i]) returns a value of the expected type
> and size,
> something the others did not do.
>
> So you can write looping code that faster than the built-in *apply
> functions, but
> it may be a fair bit more work than using them.
>
> Bill Dunlap
> Spotfire, TIBCO Software
> wdunlap tibco.com
>
>
> > -----Original Message-----
> > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
> On Behalf
> > Of C W
> > Sent: Sunday, April 07, 2013 3:11 PM
> > To: John Kane
> > Cc: r-help@r-project.org; Baylee Smith
> > Subject: Re: [R] While loop history
> >
> > May I say also ask one thing? @OP: sorry to use your post.
> >
> > What would you use instead of loops? I am aware of the apply()
> functions,
> > but they are wrapper function of for loops, so they are slower. At one
> > point, I was told to go back to C for faster implementation, but I like R
> > much more.
> >
> > In the case of repeated simulation such as Monte Carlo, what would you
> use
> > instead of for loop?
> >
> > Mike
> > On Sun, Apr 7, 2013 at 7:43 AM, John Kane <jrkrid...@inbox.com> wrote:
> >
> > >
> > >
> http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-
> > example
> > >
> > > Loops are seldom a good solution in R so some more information and data
> > > would be useful
> > >
> > > At the simplist, for your specific question I think you could set up
> two
> > > vectors (e.g. v1 <- rep(NA, 10 ) and just write the values into the
> > > vectors as you proceed through the loop.
> > >
> > > John Kane
> > > Kingston ON Canada
> > >
> > >
> > > > -----Original Message-----
> > > > From: bayywa...@gmail.com
> > > > Sent: Sun, 7 Apr 2013 14:36:33 +1200
> > > > To: r-help@r-project.org
> > > > Subject: [R] While loop history
> > > >
> > > > Hi,
> > > > I am new at R and still trying to get the hang of things.
> > > > I am running a while loop and wish to save the results of each
> iteration.
> > > > The results are a vector x of length two and I wish to save the
> results
> > > > of
> > > > each iteration as two vectors, one for x[1] and the other for x[2].
> > > >
> > > > Thanks!
> > > >
> > > > [[alternative HTML version deleted]]
> > > >
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