Convert a factor or data imported with foreign or memisc to labelled data.
to_labelled(x, ...)
# S3 method for data.frame
to_labelled(x, ...)
# S3 method for list
to_labelled(x, ...)
# S3 method for data.set
to_labelled(x, ...)
# S3 method for importer
to_labelled(x, ...)
foreign_to_labelled(x)
memisc_to_labelled(x)
# S3 method for factor
to_labelled(x, labels = NULL, .quiet = FALSE, ...)Factor or dataset to convert to labelled data frame
Not used
When converting a factor only:
an optional named vector indicating how factor levels should be coded.
If a factor level is not found in labels, it will be converted to NA.
do not display warnings for prefixed factors with duplicated codes
A tbl data frame or a labelled vector.
to_labelled() is a general wrapper calling the appropriate sub-functions.
memisc_to_labelled() converts a memisc::data.set()]` object created with
memisc package to a labelled data frame.
foreign_to_labelled() converts data imported with foreign::read.spss()
or foreign::read.dta() from foreign package to a labelled data frame,
i.e. using haven::labelled().
Factors will not be converted. Therefore, you should use
use.value.labels = FALSE when importing with foreign::read.spss() or
convert.factors = FALSE when importing with foreign::read.dta().
To convert correctly defined missing values imported with
foreign::read.spss(), you should have used to.data.frame = FALSE and
use.missings = FALSE. If you used the option to.data.frame = TRUE,
meta data describing missing values will not be attached to the import.
If you used use.missings = TRUE, missing values would have been converted
to NA.
So far, missing values defined in Stata are always imported as NA by
foreign::read.dta() and could not be retrieved by foreign_to_labelled().
If you convert a labelled vector into a factor with prefix, i.e. by using
to_factor(levels = "prefixed"), to_labelled.factor() is able
to reconvert it to a labelled vector with same values and labels.
if (FALSE) {
# from foreign
library(foreign)
sav <- system.file("files", "electric.sav", package = "foreign")
df <- to_labelled(read.spss(
sav,
to.data.frame = FALSE,
use.value.labels = FALSE,
use.missings = FALSE
))
# from memisc
library(memisc)
nes1948.por <- UnZip("anes/NES1948.ZIP", "NES1948.POR", package = "memisc")
nes1948 <- spss.portable.file(nes1948.por)
ds <- as.data.set(nes1948)
df <- to_labelled(ds)
}
# Converting factors to labelled vectors
f <- factor(
c("yes", "yes", "no", "no", "don't know", "no", "yes", "don't know")
)
to_labelled(f)
#> <labelled<double>[8]>
#> [1] 3 3 2 2 1 2 3 1
#>
#> Labels:
#> value label
#> 1 don't know
#> 2 no
#> 3 yes
to_labelled(f, c("yes" = 1, "no" = 2, "don't know" = 9))
#> <labelled<double>[8]>
#> [1] 1 1 2 2 9 2 1 9
#>
#> Labels:
#> value label
#> 1 yes
#> 2 no
#> 9 don't know
to_labelled(f, c("yes" = 1, "no" = 2))
#> <labelled<double>[8]>
#> [1] 1 1 2 2 NA 2 1 NA
#>
#> Labels:
#> value label
#> 1 yes
#> 2 no
to_labelled(f, c("yes" = "Y", "no" = "N", "don't know" = "DK"))
#> <labelled<character>[8]>
#> [1] Y Y N N DK N Y DK
#>
#> Labels:
#> value label
#> Y yes
#> N no
#> DK don't know
s1 <- labelled(c("M", "M", "F"), c(Male = "M", Female = "F"))
labels <- val_labels(s1)
f1 <- to_factor(s1)
f1
#> [1] Male Male Female
#> Levels: Male Female
to_labelled(f1)
#> <labelled<double>[3]>
#> [1] 1 1 2
#>
#> Labels:
#> value label
#> 1 Male
#> 2 Female
identical(s1, to_labelled(f1))
#> [1] FALSE
to_labelled(f1, labels)
#> <labelled<character>[3]>
#> [1] M M F
#>
#> Labels:
#> value label
#> M Male
#> F Female
identical(s1, to_labelled(f1, labels))
#> [1] TRUE
l <- labelled(
c(1, 1, 2, 2, 9, 2, 1, 9),
c("yes" = 1, "no" = 2, "don't know" = 9)
)
f <- to_factor(l, levels = "p")
f
#> [1] [1] yes [1] yes [2] no [2] no [9] don't know
#> [6] [2] no [1] yes [9] don't know
#> Levels: [1] yes [2] no [9] don't know
to_labelled(f)
#> <labelled<double>[8]>
#> [1] 1 1 2 2 9 2 1 9
#>
#> Labels:
#> value label
#> 1 yes
#> 2 no
#> 9 don't know
identical(to_labelled(f), l)
#> [1] TRUE