Convert to labelled data
Convert a factor or data imported with foreign or memisc to labelled data.
to_labelled(x, ...) ## S3 method for class 'data.frame' to_labelled(x, ...) ## S3 method for class 'list' to_labelled(x, ...) ## S3 method for class 'data.set' to_labelled(x, ...) ## S3 method for class 'importer' to_labelled(x, ...) foreign_to_labelled(x) memisc_to_labelled(x) ## S3 method for class 'factor' to_labelled(x, labels = NULL, .quiet = FALSE, ...)
x |
Factor or dataset to convert to labelled data frame |
... |
Not used |
labels |
When converting a factor only:
an optional named vector indicating how factor levels should be coded.
If a factor level is not found in |
.quiet |
do not display warnings for prefixed factors with duplicated codes |
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.
A tbl data frame or a labelled vector.
## Not run:
# 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)
df <- to_labelled(nes1948)
ds <- as.data.set(nes19480)
df <- to_labelled(ds)
## End(Not run)
# Converting factors to labelled vectors
f <- factor(c("yes", "yes", "no", "no", "don't know", "no", "yes", "don't know"))
to_labelled(f)
to_labelled(f, c("yes" = 1, "no" = 2, "don't know" = 9))
to_labelled(f, c("yes" = 1, "no" = 2))
to_labelled(f, c("yes" = "Y", "no" = "N", "don't know" = "DK"))
s1 <- labelled(c('M', 'M', 'F'), c(Male = 'M', Female = 'F'))
labels <- val_labels(s1)
f1 <- to_factor(s1)
f1
to_labelled(f1)
identical(s1, to_labelled(f1))
to_labelled(f1, labels)
identical(s1, to_labelled(f1, labels))
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
to_labelled(f)
identical(to_labelled(f), l)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.