Remove variable label, value labels and user defined missing values

Use remove_var_label() to remove variable label, remove_val_labels() to remove value labels, remove_user_na() to remove user defined missing values (na_values and na_range) and remove_labels() to remove all.

Usage

remove_labels(
  x,
  user_na_to_na = FALSE,
  keep_var_label = FALSE,
  user_na_to_tagged_na = FALSE
)

remove_var_label(x)

remove_val_labels(x)

remove_user_na(x, user_na_to_na = FALSE, user_na_to_tagged_na = FALSE)

Arguments

x

A vector or a data frame.

user_na_to_na

Convert user defined missing values into NA?

keep_var_label

Keep variable label?

user_na_to_tagged_na

Convert user defined missing values into tagged NA? It could be applied only to numeric vectors. Note that integer labelled vectors will be converted to double labelled vectors.

Details

Be careful with remove_user_na() and remove_labels(), user defined missing values will not be automatically converted to NA, except if you specify user_na_to_na = TRUE. user_na_to_na(x) is an equivalent of remove_user_na(x, user_na_to_na = TRUE).

If you prefer to convert variables with value labels into factors, use to_factor() or use unlabelled().

Examples

x <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
var_label(x) <- "A variable"
x
#> <labelled_spss<integer>[10]>: A variable
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> Missing values: 9, 10
#> 
#> Labels:
#>  value label
#>      1  Good
#>      8   Bad

remove_labels(x)
#>  [1]  1  2  3  4  5  6  7  8  9 10
remove_labels(x, user_na_to_na = TRUE)
#>  [1]  1  2  3  4  5  6  7  8 NA NA
remove_user_na(x, user_na_to_na = TRUE)
#> <labelled<integer>[10]>: A variable
#>  [1]  1  2  3  4  5  6  7  8 NA NA
#> 
#> Labels:
#>  value label
#>      1  Good
#>      8   Bad
remove_user_na(x, user_na_to_tagged_na = TRUE)
#> 'x' has been converted into a double vector.
#> <labelled<double>[10]>: A variable
#>  [1]     1     2     3     4     5     6     7     8 NA(a) NA(b)
#> 
#> Labels:
#>  value label
#>      1  Good
#>      8   Bad