[R] Progress Monitor in R / looping

[Gabor Grothendieck]

This is easy to do with the proto package.  Here is a basic version:

library(proto)
ml <- proto(start = 1, end = 5, val = NULL, it = function(.) {
    if (is.null(.$val)) return(.$val <- .$start) else .$val <- .$val + 1
    if (.$val > .$end) return(FALSE) else .$val
})

# test 1
while(ml$it()) cat("iteration:", ml$val, "of", ml$end, "\n")

# test 2 - delegate, i.e. inherit, a second iterator
new.ml <- ml$proto(start = 3, end = 6, val = NULL)
while(new.ml$it()) cat("iteration:", new.ml$val, "of", new.ml$end, "\n")

and it does not take much more code than this to create an even
fancier version.


On 10/25/06, Barry Rowlingson <B.Rowlingson at lancaster.ac.uk> wrote:
> Xiaofan Cao wrote:
> > Hi there,
> >
> > I'm writing a program in R that has a few nested loops. I'd like to
> > monitor the progress when the program is running and be able to estimate
> > the remaining time.
>
>  A long time ago I started writing some code to give R something like
> an 'iterator' object. You could do this:
>
>  > ml=loop(5)
>
>  > while(iterate(ml))
>  + {cat("doing ",iteration(ml)," of ",N(ml),"\n","Ending at
> ",predictEnd(ml),"\n");sleep(5)}
>
> doing  1  of  5
>  Ending at  Wed 25 Oct 2006 11:00:05 BST
> doing  2  of  5
>  Ending at  Wed 25 Oct 2006 11:00:20 BST
> doing  3  of  5
>  Ending at  Wed 25 Oct 2006 11:00:20 BST
> doing  4  of  5
>  Ending at  Wed 25 Oct 2006 11:00:20 BST
> doing  5  of  5
>  Ending at  Wed 25 Oct 2006 11:00:20 BST
>
>  you use loop(N) to construct a 1:N loop object, while(iterate(ml)) to
> loop round it, iteration(ml) to get the current iteration number, N(ml)
> to get the iteration limit, and predictEnd(ml) to guess when the whole
> thing will finish.
>
>  All the information about the loop is encapsulated in the ml object.
>
>  It needs a chunk of polishing up and nobody seemed that interested in
> it last time I mentioned it. My particular application was to MCMC,
> where you could have an MCMC iterator object that was a subclass of my
> simple loop class, and then you could have methods like if(isBurnIn(ml))
> to decide when to start taking samples, or if(!isThinned(ml)) to decide
> whether to store a sample from a thinned chain. Again, all the info
> encapsulated in the loop object.
>
>  Another advantage is that unlike for(i in 1:10000000) it doesn't
> create a vector of 10000000 objects...
>
>  If anyone thinks this is worth me working on then I'll try and find
> some spare time (hah!) to fix it up. Or if anyone wants to take over, I
> can throw my code at you at see if it sticks.
>
> Barry
>
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