Functions to extract information about the measurement levels of a variable (if already present), or to specify such measurement levels.
Arguments
- x
A declared vector.
- value
A single character string of measurement levels, separated by commas.
Details
This function creates an attribute called "measurement" to a declared
This object, as an optional feature, at this point for purely aesthetic
reasons. attribute might become useful in the future to (automatically)
determine if a declared object is suitable for a certain statistical
analysis, for instance regression requires quantitative variables, while some
declared objects are certainly categorical despite using numbers to denote
categories.
It distinguishes between "categorical" and "quantitative" types of
variables, and additionally recognizes "nominal" and "ordinal" as
categorical, and similarly recognizes "interval", "ratio",
"discrete" and "continuous" as quantitative.
The words "qualitative" is treated as a synonym for "categorical",
and the words "metric" and "numeric" are treated as synonyms for
"quantitative", respectively.
See also
Other labelling functions:
drop_undeclare,
labels()
Examples
x <- declared(
c(-2, 1:5, -1),
labels = c(Good = 1, Bad = 5, DK = -1),
na_values = c(-1, -2),
label = "Test variable"
)
x
#> <declared<numeric>[7]> Test variable
#> [1] NA(-2) 1 2 3 4 5 NA(-1)
#> Missing values: -1, -2
#>
#> Labels:
#> value label
#> 1 Good
#> 5 Bad
#> -1 DK
measurement(x)
#> [1] "Unspecified, but likely categorical"
# automatically recognized as categorical
measurement(x) <- "ordinal"
measurement(x)
#> [1] "categorical, ordinal"
# the same with
measurement(x) <- "categorical, ordinal"
set.seed(1890)
x <- declared(
sample(c(18:90, -91), 20, replace = TRUE),
labels = c("No answer" = -91),
na_values = -91,
label = "Respondent's age"
)
# automatically recognized as quantitative
measurement(x) <- "discrete"
measurement(x)
#> [1] "quantitative, discrete"
# the same with
measurement(x) <- "metric, discrete"