Description Usage Arguments Examples
Convert a labelled vector to a factor, even if it doesn't have the proper
class, as long as it has the "labels" attribute.
You can have this attribute without, for example, the haven_labelled class,
when importing data with rio::import()
for example.
1 |
x |
a vector |
... |
passed to |
1 2 3 4 5 |
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(c(1, 1, 2), c(Male = 1, Female = 2),
lablld+ label="Assigned sex at birth")
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(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
[1] 1 2 1 2 Refused Unknown
Levels: 1 2 Unknown Refused
[1] 1 2 1 2 Refused Unknown
Levels: 1 2 Unknown Refused
[1] 1 2 1 2 Refused Unknown
Levels: 1 2 Unknown Refused
[1] 1 2 3 4
Levels: 1 2 3 4
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