Checking whether a row has only NA values

We create random data.

A <- array(0, dim = c(5000, 50))

nar <- sample(nrow(A), 100)
A[nar, ] <- NA

Three functions that return a logical vector whose length matches the number of rows in A. The functions go from straightforward to more obscure.

f1 <- function(a)
    apply(a, 1, function(x) all(is.na(x)))

f2 <- function(a)
    rowSums(is.na(a)) == dim(a)[[2L]]

f3 <- function(a) {
    mn <- dim(a)
    .rowSums(is.na(a), m = mn[[1L]], n = mn[[2L]]) == dim(a)[[2L]]
}

We check whether results are equal.

all.equal(sort(nar),
         which(f1(A)))
all.equal(sort(nar),
         which(f2(A)))
all.equal(sort(nar),
         which(f3(A)))

[1] TRUE

[1] TRUE

[1] TRUE

The functions differ in computing time.

library("rbenchmark")
benchmark(f1(A),
          f2(A),
          f3(A), order = "relative",
          replications = 1000)[, 1:4]

   test replications elapsed relative
3 f3(A)         1000   0.765    1.000
2 f2(A)         1000   0.824    1.077
1 f1(A)         1000   7.244    9.469

What if we had fewer columns?

A <- array(0, dim = c(5000, 3))

nar <- sample(nrow(A), 100)
A[nar, ] <- NA

benchmark(f1(A),
          f2(A),
          f3(A), order = "relative",
          replications = 1000)[, 1:4]

   test replications elapsed relative
3 f3(A)         1000   0.058    1.000
2 f2(A)         1000   0.062    1.069
1 f1(A)         1000   4.634   79.897

Altogether there seems little difference between using .rowSums and rowSums; but both variations are much faster than the (straightforward) use of apply.