This function retrieves tagged NA values and their associated value labels from a labelled vector.

get_na(x, as.tag = FALSE)



Variable (vector) with value label attributes, including tagged missing values (see tagged_na()); or a data frame or list with such variables.


Logical, if TRUE, the returned values are not tagged NA's, but their string representative including the tag value. See 'Examples'.


The tagged missing values and their associated value labels from x, or NULL if x has no tagged missing values.


Other statistical software packages (like 'SPSS' or 'SAS') allow to define multiple missing values, e.g. not applicable, refused answer or "real" missing. These missing types may be assigned with different values, so it is possible to distinguish between these missing types. In R, multiple declared missings cannot be represented in a similar way with the regular missing values. However, tagged_na() values can do this. Tagged NAs work exactly like regular R missing values except that they store one additional byte of information: a tag, which is usually a letter ("a" to "z") or character number ("0" to "9"). This allows to indicate different missings.

Furthermore, see 'Details' in get_values.


x <- labelled(c(1:3, tagged_na("a", "c", "z"), 4:1),
              c("Agreement" = 1, "Disagreement" = 4, "First" = tagged_na("c"),
                "Refused" = tagged_na("a"), "Not home" = tagged_na("z")))
# get current NA values
#> <labelled<double>[10]>
#>  [1]     1     2     3 NA(a) NA(c) NA(z)     4     3     2     1
#> Labels:
#>  value        label
#>      1    Agreement
#>      4 Disagreement
#>  NA(c)        First
#>  NA(a)      Refused
#>  NA(z)     Not home
#>    First  Refused Not home 
#>       NA       NA       NA 
# which NA has which tag?
get_na(x, as.tag = TRUE)
#>    First  Refused Not home 
#>  "NA(c)"  "NA(a)"  "NA(z)" 

# replace only the NA, which is tagged as NA(c)
if (require("sjmisc")) {
  replace_na(x, value = 2, = "c")
  get_na(replace_na(x, value = 2, = "c"))

  # data frame as input
  y <- labelled(c(2:3, 3:1, tagged_na("y"), 4:1),
                c("Agreement" = 1, "Disagreement" = 4, "Why" = tagged_na("y")))
  get_na(data.frame(x, y))
#> $x
#>    First  Refused Not home 
#>       NA       NA       NA 
#> $y
#> Why 
#>  NA