Create a labelled vector.

A labelled vector is a common data structure in other statistical environments, allowing you to assign text labels to specific values.

labelled(x, labels, label = NULL)

is.labelled(x)

Arguments

x	A vector to label. Must be either numeric (integer or double) or character.
labels	A named vector or NULL. The vector should be the same type as x. Unlike factors, labels don't need to be exhaustive: only a fraction of the values might be labelled.
label	A short, human-readable description of the vector.

Value

An object of class "haven_labelled" or "haven_labelled_spss".

See also

labelled (haven)

haven::is.labelled() (haven)

Examples

s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F"))
s1
#> <Labelled character>
#> [1] M M F
#> 
#> Labels:
#>  value  label
#>      M   Male
#>      F Female
str(s1)
#>  'haven_labelled' chr [1:3] "M" "M" "F"
#>  - attr(*, "labels")= Named chr [1:2] "M" "F"
#>   ..- attr(*, "names")= chr [1:2] "Male" "Female"
s2 <- labelled(c(1, 1, 2), c(Male = 1, Female = 2),
               label="Assigned sex at birth")
s2
#> <Labelled double>: Assigned sex at birth
#> [1] 1 1 2
#> 
#> Labels:
#>  value  label
#>      1   Male
#>      2 Female
str(s2)
#>  'haven_labelled' num [1:3] 1 1 2
#>  - attr(*, "label")= chr "Assigned sex at birth"
#>  - attr(*, "labels")= Named num [1:2] 1 2
#>   ..- attr(*, "names")= chr [1:2] "Male" "Female"

# Unfortunately it's not possible to make as.factor() work for labelled objects
# so instead use to_factor(). This works for all types of labelled vectors.
to_factor(s1)
#> [1] Male   Male   Female
#> Levels: Male Female
to_factor(s1, levels = "prefixed")
#> [1] [M] Male   [M] Male   [F] Female
#> Levels: [M] Male [F] Female
to_factor(s2)
#> [1] Male   Male   Female
#> attr(,"label")
#> [1] Assigned sex at birth
#> Levels: Male Female

# Other statistical software supports multiple types of missing values
s3 <- labelled_spss(c(1, 1, 2, 2, 8, 9),
               c(Male = 1, Female = 2, Refused = 8, "Not applicable" = 9),
               na_values = c(8, 9)

)
s3
#> <Labelled SPSS double>
#> [1] 1 1 2 2 8 9
#> Missing values: 8, 9
#> 
#> Labels:
#>  value          label
#>      1           Male
#>      2         Female
#>      8        Refused
#>      9 Not applicable
str(s3)
#>  'haven_labelled_spss' num [1:6] 1 1 2 2 8 9
#>  - attr(*, "labels")= Named num [1:4] 1 2 8 9
#>   ..- attr(*, "names")= chr [1:4] "Male" "Female" "Refused" "Not applicable"
#>  - attr(*, "na_values")= num [1:2] 8 9
to_factor(s3)
#> [1] Male           Male           Female         Female         Refused       
#> [6] Not applicable
#> Levels: Male Female Refused Not applicable
to_factor(s3, user_na_to_na = TRUE)
#> [1] Male   Male   Female Female <NA>   <NA>  
#> Levels: Male Female Refused Not applicable
is.labelled(s1)
#> [1] TRUE