Create a labelled vector.Source:
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
soon after importing.
labelled(x = double(), labels = NULL, label = NULL) is.labelled(x)
A vector to label. Must be either numeric (integer or double) or character.
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.
A short, human-readable description of the vector.
s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F")) s2 <- labelled(c(1, 1, 2), c(Male = 1, Female = 2)) s3 <- labelled( c(1, 1, 2), c(Male = 1, Female = 2), label = "Assigned sex at birth" ) # 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) #>  Male Male Female #> Levels: Female Male as_factor(s1, levels = "values") #>  M M F #> Levels: M F as_factor(s2) #>  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>> #>  M M F X N/A #> #> Labels: #> value label #> M Male #> F Female #> X Refused #> N/A Not applicable as_factor(s3) #>  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 2 1 2 10 9