Reduce() is really amazingly fast!
Even with a much larger number of columns, it is still in the same ballpark
(and much more readable):
> boolean <- c(TRUE, rep(FALSE,10^3))
> a<-matrix(sample(boolean, 10^7, replace = TRUE),10^4,10^3)
> b<-data.frame(a)
> system.time({opt4 <- rowSums(a, na.rm = TRUE) > 0})
user system elapsed
0.129 0.001 0.131
> system.time({opt2 <- Reduce('|',b)})
user system elapsed
0.190 0.109 0.303
and:
> boolean <- c(TRUE, rep(FALSE,10^4))
> a<-matrix(sample(boolean, 10^7, replace = TRUE),10^3,10^4)
> b<-data.frame(a)
> system.time({opt4 <- rowSums(a, na.rm = TRUE) > 0})
user system elapsed
0.082 0.001 0.083
> system.time({opt2 <- Reduce('|',b)})
user system elapsed
0.205 0.001 0.209
It seems to pretty much make rowSums obsolete, vs. Reduce('+'), except that
it works on lists, and converting a matrix to a data.frame takes ages.
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