Has labels
Examples
example("labelled", "haven")
#>
#> lablld> s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F"))
#>
#> lablld> s2 <- labelled(c(1, 1, 2), c(Male = 1, Female = 2))
#>
#> lablld> s3 <- labelled(
#> lablld+ c(1, 1, 2),
#> lablld+ c(Male = 1, Female = 2),
#> lablld+ label = "Assigned sex at birth"
#> lablld+ )
#>
#> lablld> # Unfortunately it's not possible to make as.factor work for labelled objects
#> lablld> # so instead use as_factor. This works for all types of labelled vectors.
#> lablld> as_factor(s1)
#> [1] Male Male Female
#> Levels: Female Male
#>
#> lablld> as_factor(s1, levels = "values")
#> [1] M M F
#> Levels: M F
#>
#> lablld> as_factor(s2)
#> [1] Male Male Female
#> Levels: Male Female
#>
#> lablld> # Other statistical software supports multiple types of missing values
#> lablld> s3 <- labelled(
#> lablld+ c("M", "M", "F", "X", "N/A"),
#> lablld+ c(Male = "M", Female = "F", Refused = "X", "Not applicable" = "N/A")
#> lablld+ )
#>
#> lablld> s3
#> <labelled<character>[5]>
#> [1] M M F X N/A
#>
#> Labels:
#> value label
#> M Male
#> F Female
#> X Refused
#> N/A Not applicable
#>
#> lablld> as_factor(s3)
#> [1] Male Male Female Refused Not applicable
#> Levels: Female Male Not applicable Refused
#>
#> lablld> # Often when you have a partially labelled numeric vector, labelled values
#> lablld> # are special types of missing. Use zap_labels to replace labels with missing
#> lablld> # values
#> lablld> x <- labelled(c(1, 2, 1, 2, 10, 9), c(Unknown = 9, Refused = 10))
#>
#> lablld> zap_labels(x)
#> [1] 1 2 1 2 10 9
has_labels(x)
#> [1] TRUE