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)

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

# NOT RUN { 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) as_factor(s1, labels = "values") as_factor(s2) # 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 as_factor(s3) # 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) # }