so profiling reveals that '[.data.frame' and '[[.data.frame' and '[' are the biggest timesuckers...install.packages("profr") library(profr) p <- profr(fcn_create_nonissuing_match_by_quarterssinceissue(...)) plot(p)That should at least help you see where the slow bits are. Hadley
i suppose i'll try using matrices and see how that stacks up (since all my cols are numeric, should be a problem-free approach).
but i'm really wondering if there isn't some neat vectorized approach i could use to avoid at least one of the nested loops...
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