Zap value labels

Removes value labels, leaving unlabelled vectors as is. Use this if you want to simply drop all labels from a data frame.

Zapping labels from labelled_spss() also removes user-defined missing values by default, replacing with standard NAs. Use the user_na argument to override this behaviour.

Usage

zap_labels(x, ...)

# S3 method for haven_labelled_spss
zap_labels(x, ..., user_na = FALSE)

Arguments

x

A vector or data frame

...

Other arguments passed down to method.

user_na

If FALSE, the default, zap_labels() will convert labelled_spss() user-defined missing values to NA. If TRUE they will be treated like normal values.

See also

zap_label() to remove variable labels.

Other zappers: zap_empty(), zap_formats(), zap_label(), zap_widths()

Examples

x1 <- labelled(1:5, c(good = 1, bad = 5))
x1
#> <labelled<integer>[5]>
#> [1] 1 2 3 4 5
#> 
#> Labels:
#>  value label
#>      1  good
#>      5   bad
zap_labels(x1)
#> [1] 1 2 3 4 5

x2 <- labelled_spss(c(1:4, 9), c(good = 1, bad = 5), na_values = 9)
x2
#> <labelled_spss<double>[5]>
#> [1] 1 2 3 4 9
#> Missing values: 9
#> 
#> Labels:
#>  value label
#>      1  good
#>      5   bad
zap_labels(x2)
#> [1]  1  2  3  4 NA

# Keep the user defined missing values
zap_labels(x2, user_na = TRUE)
#> [1] 1 2 3 4 9

# zap_labels also works with data frames
df <- tibble::tibble(x1, x2)
df
#> # A tibble: 5 × 2
#>   x1        x2       
#>   <int+lbl> <dbl+lbl>
#> 1 1 [good]  1 [good] 
#> 2 2         2        
#> 3 3         3        
#> 4 4         4        
#> 5 5 [bad]   9 (NA)   
zap_labels(df)
#> # A tibble: 5 × 2
#>      x1    x2
#>   <int> <dbl>
#> 1     1     1
#> 2     2     2
#> 3     3     3
#> 4     4     4
#> 5     5    NA