Equivalent of apply() by row in the tidyverse?

If you want to avoid coercing to a matrix, you can use purrr::pmap, which iterates across the elements of a list in parallel and passes them to a function:

library(tidyverse)

tab <- data_frame(a = 1:10, 
                  b = c(NA, letters[2:10]), 
                  c = c(LETTERS[1:9], NA))

tab %>% mutate(missing = pmap_lgl(., ~any(is.na(c(...)))))
#> # A tibble: 10 x 4
#>        a     b     c missing
#>    <int> <chr> <chr>   <lgl>
#>  1     1  <NA>     A    TRUE
#>  2     2     b     B   FALSE
#>  3     3     c     C   FALSE
#>  4     4     d     D   FALSE
#>  5     5     e     E   FALSE
#>  6     6     f     F   FALSE
#>  7     7     g     G   FALSE
#>  8     8     h     H   FALSE
#>  9     9     i     I   FALSE
#> 10    10     j  <NA>    TRUE

In the function, c is necessary to pull all the parameters passed to the function ... into a vector, which can be passed to is.na and collapsed with any. The *_lgl suffixed pmap simplifies the result to a Boolean vector.

Note that while this avoids coercing to matrix, it will not necessarily be faster than approaches that do, as matrix operations are highly optimized in R. It may make more sense to explicitly coerce to a matrix, e.g.

tab %>% mutate(missing = rowSums(is.na(as.matrix(.))) > 0)

which returns the same thing.