A labelled vector is a common data structure in other statistical environments, allowing you to assign text labels to specific values. This class makes it possible to import such labelled vectors in to R without loss of fidelity. This class provides few methods, as I expect you'll coerce to a standard R class (e.g. a factor) soon after importing.

labelled(x, labels)

is.labelled(x)

Arguments

x

A vector to label. Must be either numeric (integer or double) or character.

labels

A named vector. 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.

...

Ignored

Examples

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