Re: [R] nested for loops too slow

Well, sort of... aggregate() is basically a wrapper for lapply(), which ultimately must loop over the function call at the R interpreter level, as opposed to vectorized functions that loop at the C level and hence can be orders of magnitude faster. As a result, there is often little difference in

Re: [R] nested for loops too slow

You don't need loops at all. grw <- aggregate(gw ~ ts + ISEG + iter, data = dat, FUN = sum) GRW <- aggregate(gw ~ ts + ISEG, data = grw, FUN = function(x){max(x) - min(x)}) DC <- aggregate(div ~ ts + ISEG, data = subset(dat, IRCH == 1), FUN = function(x){max(x) - min(x)}) iter <- a

Re: [R] nested for loops too slow

You are certainly in Circle 2 of 'The R Inferno', which I suspect is where almost all of the computation time is coming from. Instead of doing: divChng <- rbind(divChng,c(datTS$ts[1], SEG[j], DC, GRW, max(datTS$iter))) it would be much better to create 'divChng' to be the final length and then

[R] nested for loops too slow

The small example below works lighting-fast; however, when I run the same script on my real problem, a 1Gb text file, the for loops have been running for over 24 hrs and I have no idea if the processing is 10% done or 90% done. I have not been able to figure out a betteR way to code up the materia