On Mon, Aug 2, 2010 at 2:08 PM, Michael Lachmann <lachm...@eva.mpg.de> wrote:
>
> Reduce() is much nicer, but I usually use
>
> rowSums(A) > 0 for 'or', and
> rowSums(A) == ncols for 'and'.
>
> Which works slightly faster.
For the sake of my own curiosity, I compared several of these options,
but in case others are interested.....
> boolean <- c(TRUE, FALSE, FALSE)
>
> set.seed(1)
> mydata <- data.frame(X = sample(boolean, 10^7, replace = TRUE),
+ Y = sample(boolean, 10^7, replace = TRUE),
+ Z = sample(boolean, 10^7, replace = TRUE))
>
> system.time(opt1 <- apply(mydata, 1, any))
user system elapsed
147.26 0.42 148.56
> system.time(opt2 <- Reduce('|', mydata))
user system elapsed
0.33 0.00 0.35
> system.time(opt3 <- as.logical(rowSums(mydata, na.rm = TRUE)))
user system elapsed
0.25 0.00 0.27
> system.time(opt4 <- rowSums(mydata, na.rm = TRUE) > 0)
user system elapsed
0.25 0.00 0.25
>
> identical(opt1, opt2)
[1] TRUE
> identical(opt1, opt3)
[1] TRUE
> identical(opt1, opt4)
[1] TRUE
>
> rm(boolean, mydata, opt1, opt2, opt3, opt4)
>
> I noticed, though, that Reduce() doesn't work on matrices. Is there an
> alternative for matrices, or do you have to convert the matrix first to a
> data.frame, and then use Reduce?
>
>
> --
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>
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>
--
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://www.joshuawiley.com/
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