Combine logical vectors in list using logical OR

Question

Question

How do I effectively combine logical vectors across the list using elementwise comparisons with logical OR (|). The result should be a logical vector of same length as the input vectors. If any of the input values is TRUE, the result is TRUE, else the result is FALSE.

Example

I have a list opts with a set of logical vectors of the same length.

> str(opts)
List of 5
 $ option1: logi [1:608247] FALSE FALSE FALSE FALSE FALSE FALSE ...
 $ option2: logi [1:608247] FALSE TRUE  FALSE TRUE  TRUE  TRUE  ...
 $ option3: logi [1:608247] FALSE TRUE  FALSE FALSE TRUE  FALSE ...
 $ option4: logi [1:608247] FALSE FALSE FALSE FALSE FALSE FALSE ...

and I want this as a result:

logi [1:608247] FALSE TRUE FALSE TRUE TRUE TRUE ...

Thus, the first value of the result, FALSE, is because the are no TRUE in the first position across all vectors of the list. The second value of the result, TRUE, is because the are two (at least one, any) TRUE in the second position of the vectors.

I am fine with changing my datastructure to be a matrix or data.frame or something else if it is better I just get this from a lapply.

Answer 1

How about Reduce:

Reduce("&", opts)
Reduce("|", opts)

Answer 2

If all the lists are the same length, you can cast it to a data frame, and then use any:

apply(data.frame(opts),1,any)

Edit: while I thought this might be fast because it avoids cbind, it turns out that this is the slowest by far of the three solutions, according to my benchmarking:

set.seed(123)
opts = as.list(as.data.frame(matrix(sample(c(TRUE, FALSE), 10000, replace=TRUE), nrow=1000)))

require(microbenchmark)
microbenchmark(Reduce("|",opts),rowSums(do.call(cbind, opts)) > 0,
               apply(as.data.frame(opts),1,any))


Unit: microseconds
                               expr      min        lq   median        uq
                  Reduce("|", opts)   99.200  101.0780  106.596  110.3725
  rowSums(do.call(cbind, opts)) > 0  209.326  211.9665  217.329  224.0505
 apply(as.data.frame(opts), 1, any) 4130.429 4245.7380 4308.054 4438.2485
     max neval
  120.63   100
  237.19   100
 6949.19   100