vec_detect_complete() detects "complete" observations. An observation is
considered complete if it is non-missing. For most vectors, this implies that
vec_detect_complete(x) == !vec_equal_na(x).
For data frames and matrices, a row is only considered complete if all
elements of that row are non-missing. To compare,
rows that are partially complete (they have at least one non-missing value).
A logical vector with the same size as
A record type vector is similar to a data frame, and is only considered complete if all fields are non-missing.
x <- c(1, 2, NA, 4, NA) # For most vectors, this is identical to `!vec_equal_na(x)` vec_detect_complete(x) #>  TRUE TRUE FALSE TRUE FALSE !vec_equal_na(x) #>  TRUE TRUE FALSE TRUE FALSE df <- data_frame( x = x, y = c("a", "b", NA, "d", "e") ) #> Warning: `data_frame()` was deprecated in tibble 1.1.0. #> Please use `tibble()` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_warnings()` to see where this warning was generated. # This returns `TRUE` where all elements of the row are non-missing. # Compare that with `!vec_equal_na()`, which detects rows that have at # least one non-missing value. df2 <- df df2$all_non_missing <- vec_detect_complete(df) df2$any_non_missing <- !vec_equal_na(df) df2 #> # A tibble: 5 x 4 #> x y all_non_missing any_non_missing #> <dbl> <chr> <lgl> <lgl> #> 1 1 a TRUE TRUE #> 2 2 b TRUE TRUE #> 3 NA NA FALSE FALSE #> 4 4 d TRUE TRUE #> 5 NA e FALSE TRUE