This vector contains common number values of NA (missing), which is aimed to
be used inside naniar functions miss_scan_count() and
replace_with_na(). The current list of numbers can be found by printing
out common_na_numbers. It is a useful way to explore your data for
possible missings, but I strongly warn against using this to replace NA
values without very carefully looking at the incidence for each of the
cases. Common NA strings are in the data object common_na_strings.
Note
original discussion here https://github.com/njtierney/naniar/issues/168
Examples
dat_ms <- tibble::tribble(~x, ~y, ~z,
1, "A", -100,
3, "N/A", -99,
NA, NA, -98,
-99, "E", -101,
-98, "F", -1)
miss_scan_count(dat_ms, -99)
#> # A tibble: 3 × 2
#> Variable n
#> <chr> <int>
#> 1 x 1
#> 2 y 0
#> 3 z 1
miss_scan_count(dat_ms, c("-99","-98","N/A"))
#> # A tibble: 3 × 2
#> Variable n
#> <chr> <int>
#> 1 x 2
#> 2 y 1
#> 3 z 2
common_na_numbers
#> [1] -9 -99 -999 -9999 9999 66 77 88
miss_scan_count(dat_ms, common_na_numbers)
#> # A tibble: 3 × 2
#> Variable n
#> <chr> <int>
#> 1 x 2
#> 2 y 0
#> 3 z 2