This is useful if you want to convert tagged missing values from SAS or
Stata, or user-defined missings from SPSS, to regular R NA.
Examples
x1 <- labelled(
c(1, 5, tagged_na("a", "b")),
c(Unknown = tagged_na("a"), Refused = tagged_na("b"))
)
x1
#> <labelled<double>[4]>
#> [1] 1 5 NA(a) NA(b)
#>
#> Labels:
#> value label
#> NA(a) Unknown
#> NA(b) Refused
zap_missing(x1)
#> <labelled<double>[4]>
#> [1] 1 5 NA NA
x2 <- labelled_spss(
c(1, 2, 1, 99),
c(missing = 99),
na_value = 99
)
x2
#> <labelled_spss<double>[4]>
#> [1] 1 2 1 99
#> Missing values: 99
#>
#> Labels:
#> value label
#> 99 missing
zap_missing(x2)
#> [1] 1 2 1 NA
#> attr(,"labels")
#> missing
#> 99
#> attr(,"class")
#> [1] "haven_labelled"
# You can also apply to data frames
df <- tibble::tibble(x1, x2, y = 4:1)
df
#> # A tibble: 4 × 3
#> x1 x2 y
#> <dbl+lbl> <dbl+lbl> <int>
#> 1 1 1 4
#> 2 5 2 3
#> 3 NA(a) [Unknown] 1 2
#> 4 NA(b) [Refused] 99 (NA) [missing] 1
zap_missing(df)
#> # A tibble: 4 × 3
#> x1 x2 y
#> <dbl+lbl> <dbl+lbl> <int>
#> 1 1 1 4
#> 2 5 2 3
#> 3 NA 1 2
#> 4 NA NA 1