> # Gabor Grothendieck's function:
> most.recent.cut <- function(x)
+ as.numeric(as.vector(cut(seq(x),c(which(x),Inf),lab=which(x),right=F)))
>
> # Version that uses which() and vectorized rep()
> most.recent <- function(x) {
+ # return a vector of indices of the most recent TRUE value
+ if (!is.logical(x))
+ stop("x must be logical")
+ x.pos <- which(x)
+ if (length(x.pos)==0 || x.pos[1] != 1)
+ x.pos <- c(1, x.pos)
+ rep(x.pos, c(diff(x.pos), length(x) - x.pos[length(x.pos)] + 1))
+ }
>> x <- sample(c(T,F),1e7,rep=T) > system.time(most.recent.cut(x)) [1] 41.21 0.54 41.98 NA NA > system.time(most.recent(x)) [1] 2.67 0.08 2.78 NA NA >
-- Tony Plate
At Friday 10:21 PM 11/14/2003 -0500, Gabor Grothendieck wrote:
From: Tony Plate <[EMAIL PROTECTED]>:
>
> Here's a function that does the essential computation (written to work in
> both S-plus and R).
>
> This looks like one of those tricky problems that do not vectorize
> easily. It would be simple to write a C-program to compute this very
> efficiently. But are there any more efficient solutions than ones like the
> below (that are written without resort to C)?
>
> most.recent <- function(x) {
> # return a vector of indices of the most recent TRUE value
> if (!is.logical(x))
> stop("x must be logical")
> x[is.na(x)] <- FALSE
> # x is a logical vector
> r <- rle(x)
> ends <- cumsum(r$lengths)
> starts <- ends - r$lengths + 1
> spec <- as.list(as.data.frame(rbind(start=starts, len=r$lengths,
> value=as.numeric(r$values), prev.end=c(NA, ends[-length(ends)]))))
> names(spec) <- NULL
> unlist(lapply(spec, function(s) if (s[3]) seq(s[1], len=s[2]) else
> rep(s[4], len=s[2])), use.names=F)
> }
>
> > x <- c(F,T,T,F,F,F,T,F)
> > most.recent(x)
> [1] NA 2 3 3 3 3 7 7
>
> And using it to do the fill-forward:
>
> > x <- c(NA,2,3,NA,4,NA,5,NA,NA,NA,6,7,8,NA)
> > x[most.recent(!is.na(x))]
> [1] NA 2 3 3 4 4 5 5 5 5 6 7 8 8
> >
>
> Some timings:
>
> > x <- sample(c(T,F),1e4,rep=T)
> > system.time(most.recent(x))
> [1] 0.33 0.01 0.47 NA NA
> > x <- sample(c(T,F),1e5,rep=T)
> > system.time(most.recent(x))
> [1] 4.27 0.06 6.44 NA NA
> > x <- sample(c(T,F),1e6,rep=T)
> > system.time(most.recent(x))
> [1] 47.27 0.17 47.97 NA NA
> >
>
> -- Tony Plate
>
> PS. Actually, I just found a solution that I had lying around that is about
> 70 times as fast on random test data like the above.
I was waiting for you to post this but didn't see it so I thought I would post mine. This one is 13x as fast and only requires a single line of code.
> set.seed(111) > x <- sample(c(T,F),10000,rep=T)
> system.time(z1 <- most.recent(x)) [1] 0.92 0.02 1.68 NA NA
> system.time(z2 <- as.numeric(as.vector( cut(seq(x),c(which(x),Inf),lab=which(x),right=F)))) [1] 0.07 0.00 0.12 NA NA
> all.equal(z1,z2) [1] TRUE
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Tony Plate [EMAIL PROTECTED]
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