What is the fastest way to perform multiple logical comparisons in R?

Question

What is the fastest way to perform multiple logical comparisons in R?

Consider for example the vector x

set.seed(14)
x = sample(LETTERS[1:4], size=10, replace=TRUE)

I want to test if each entry of x is either a "A" or a "B" (and not anything else). The following works

x == "A" | x == "B"
[1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE

The above code loops three times through the length of the whole vector. Is there a way in R to loop only once and test for each item whether it satisfies one or another condition?

Answer 1

If your objective is just to make a single pass, that is pretty straightforward to write in Rcpp, even if you don't have much experience with C++:

#include <Rcpp.h>

// [[Rcpp::export]]
Rcpp::LogicalVector single_pass(Rcpp::CharacterVector x, Rcpp::String a, Rcpp::String b) {
  R_xlen_t i = 0, n = x.size();
  Rcpp::LogicalVector result(n);

  for ( ; i < n; i++) {
    result[i] = (x[i] == a || x[i] == b);
  }

  return result;
}

---

For such a small object as the one used in your example, the slight overhead of .Call (presumably) masks the speed of the Rcpp version,

r_fun <- function(X) X == "A" | X == "B"
##
cpp_fun <- function(X) single_pass(X, "A", "B")
##
all.equal(r_fun(x), cpp_fun(x))
#[1] TRUE
microbenchmark::microbenchmark(
  r_fun(x), cpp_fun(x), times = 1000L)
#Unit: microseconds
#expr         min    lq     mean median     uq    max neval
#r_fun(x)   1.499 1.584 1.974156 1.6795 1.8535 37.903  1000
#cpp_fun(x) 1.860 2.334 3.042671 2.7450 3.1140 51.870  1000

But for larger vectors (I'm assuming this is your real intention), it is considerably faster:

x2 <- sample(LETTERS, 10E5, replace = TRUE)
##
all.equal(r_fun(x2), cpp_fun(x2))
# [1] TRUE
microbenchmark::microbenchmark(
  r_fun(x2), cpp_fun(x2), times = 200L)
#Unit: milliseconds
#expr              min        lq      mean    median        uq      max neval
#r_fun(x2)   78.044518 79.344465 83.741901 80.999538 86.368627 149.5106   200
#cpp_fun(x2)  7.104929  7.201296  7.797983  7.605039  8.184628  10.7250   200

---

Here's a quick attempt at generalizing the above, if you have any use for it.