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.
Usage
labelled(x = double(), labels = NULL, 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 asx. 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.
Examples
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)
#> [1] Male Male Female
#> Levels: Female Male
as_factor(s1, levels = "values")
#> [1] M M F
#> Levels: M F
as_factor(s2)
#> [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>[5]>
#> [1] M M F X N/A
#>
#> Labels:
#> value label
#> M Male
#> F Female
#> X Refused
#> N/A Not applicable
as_factor(s3)
#> [1] Male Male Female Refused
#> [5] 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