The purpose of the labelled package is to provide functions to manipulate
metadata as variable labels, value labels and defined missing values
using the labelled class and the label attribute introduced in haven package.
A variable label could be specified for any vector using var_label.
library(labelled)
var_label(iris$Sepal.Length) <- "Length of sepal"
It's possible to add a variable label to several columns of a data frame using a named list.
var_label(iris) <- list(Petal.Length = "Length of petal", Petal.Width = "Width of Petal")
To get the variable label, simply call var_label.
var_label(iris$Petal.Width)
## [1] "Width of Petal"
var_label(iris)
## $Sepal.Length
## [1] "Length of sepal"
##
## $Sepal.Width
## NULL
##
## $Petal.Length
## [1] "Length of petal"
##
## $Petal.Width
## [1] "Width of Petal"
##
## $Species
## NULL
To remove a variable label, use NULL.
var_label(iris$Sepal.Length) <- NULL
In RStudio, variable labels will be displayed in data viewer.
View(iris)
The first way to create a labelled vector is to use the labelled function.
It's not mandatory to provide a label for each value observed in your vector.
You can also provide a label for values not observed.
v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, no = 3, "don't know" = 8, refused = 9))
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 8 don't know
## 9 refused
Use val_labels to get all value labels and val_label to get the value label associated
with a specific value.
val_labels(v)
## yes no don't know refused
## 1 3 8 9
val_label(v, 8)
## [1] "don't know"
val_labels could also be used to modify all the value labels attached to a vector,
while val_label will update only one specific value label.
val_labels(v) <- c(yes = 1, nno = 3, bug = 5)
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 nno
## 5 bug
val_label(v, 3) <- "no"
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 5 bug
With val_label, you can also add or remove specific value labels.
val_label(v, 2) <- "maybe"
val_label(v, 5) <- NULL
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 2 maybe
To remove all value labels, use val_labels and NULL. The labelled class will also be removed.
val_labels(v) <- NULL
v
## [1] 1 2 2 2 3 9 1 3 2 NA
Adding a value label to a non labelled vector will apply labelled class to it.
val_label(v, 1) <- "yes"
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
Note that applying val_labels to a factor will have no effect!
f <- factor(1:3)
f
## [1] 1 2 3
## Levels: 1 2 3
val_labels(f) <- c(yes = 1, no = 3)
f
## [1] 1 2 3
## Levels: 1 2 3
You could also apply value labels to several columns of a data frame.
df <- data.frame(v1 = 1:3, v2 = c(2, 3, 1), v3 = 3:1)
val_label(df, 1) <- "yes"
val_label(df[, c("v1", "v3")], 2) <- "maybe"
val_label(df[, c("v2", "v3")], 3) <- "no"
val_labels(df)
## $v1
## yes maybe
## 1 2
##
## $v2
## yes no
## 1 3
##
## $v3
## yes maybe no
## 1 2 3
val_labels(df[, c("v1", "v3")]) <- c(YES = 1, MAYBE = 2, NO = 3)
val_labels(df)
## $v1
## YES MAYBE NO
## 1 2 3
##
## $v2
## yes no
## 1 3
##
## $v3
## YES MAYBE NO
## 1 2 3
val_labels(df) <- NULL
val_labels(df)
## $v1
## NULL
##
## $v2
## NULL
##
## $v3
## NULL
val_labels(df) <- list(v1 = c(yes = 1, no = 3), v2 = c(a = 1, b = 2, c = 3))
val_labels(df)
## $v1
## yes no
## 1 3
##
## $v2
## a b c
## 1 2 3
##
## $v3
## NULL
Value labels are sorted by default in the order they have been created.
v <- c(1,2,2,2,3,9,1,3,2,NA)
val_label(v, 1) <- "yes"
val_label(v, 3) <- "no"
val_label(v, 9) <- "refused"
val_label(v, 2) <- "maybe"
val_label(v, 8) <- "don't know"
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 9 refused
## 2 maybe
## 8 don't know
It could be useful to reorder the value labels according to their attached values.
sort_val_labels(v)
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 maybe
## 3 no
## 8 don't know
## 9 refused
sort_val_labels(v, decreasing = TRUE)
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 9 refused
## 8 don't know
## 3 no
## 2 maybe
## 1 yes
If you prefer, you can also sort them according to the labels.
sort_val_labels(v, according_to = "l")
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 8 don't know
## 2 maybe
## 3 no
## 9 refused
## 1 yes
In some cases, values who don't have an attached value label could be considered as missing.
nolabel_to_na will convert them to NA.
v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, maybe = 2, no = 3))
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 maybe
## 3 no
nolabel_to_na(v)
## <Labelled double>
## [1] 1 2 2 2 3 NA 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 maybe
## 3 no
In other cases, a value label is attached only to specific values that corresponds to a missing value. For example:
size <- labelled(c(1.88, 1.62, 1.78, 99, 1.91), c("not measured" = 99))
size
## <Labelled double>
## [1] 1.88 1.62 1.78 99.00 1.91
##
## Labels:
## value label
## 99 not measured
In such cases, val_labels_to_na could be appropriate.
val_labels_to_na(size)
## [1] 1.88 1.62 1.78 NA 1.91
These two functions could also be applied to an overall data frame. Only labelled vectors will be impacted.
A labelled vector could easily be converted to a factor with to_factor.
v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, no = 3, "don't know" = 8, refused = 9))
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 8 don't know
## 9 refused
to_factor(v)
## [1] yes 2 2 2 no refused yes no
## [9] 2 <NA>
## Levels: yes 2 no don't know refused
The levels argument allows to specify what should be used as the factor levels,
i.e. the labels (default), the values or the labels prefixed with values.
to_factor(v, levels = "v")
## [1] 1 2 2 2 3 9 1 3 2 <NA>
## Levels: 1 2 3 8 9
to_factor(v, levels = "p")
## [1] [1] yes [2] 2 [2] 2 [2] 2 [3] no
## [6] [9] refused [1] yes [3] no [2] 2 <NA>
## Levels: [1] yes [2] 2 [3] no [8] don't know [9] refused
The ordered argument will create an ordinal factor.
to_factor(v, ordered = TRUE)
## [1] yes 2 2 2 no refused yes no
## [9] 2 <NA>
## Levels: yes < 2 < no < don't know < refused
The argument nolabel_to_na specify if the corresponding function should be
applied before converting to a factor. Therefore, the two following commands are equivalent.
to_factor(v, nolabel_to_na = TRUE)
## [1] yes <NA> <NA> <NA> no refused yes no
## [9] <NA> <NA>
## Levels: yes no don't know refused
to_factor(nolabel_to_na(v))
## [1] yes <NA> <NA> <NA> no refused yes no
## [9] <NA> <NA>
## Levels: yes no don't know refused
sort_levels specifies how the levels should be sorted: "none" to keep the order in which value labels
have been defined, "values" to order the levels according to the values and "labels" according
to the labels. "auto" (default) will be equivalent to "none" except if some values with no
attached labels are found and are not dropped. In that case, "values" will be used.
to_factor(v, sort_levels = "n")
## [1] yes 2 2 2 no refused yes no
## [9] 2 <NA>
## Levels: yes no don't know refused 2
to_factor(v, sort_levels = "v")
## [1] yes 2 2 2 no refused yes no
## [9] 2 <NA>
## Levels: yes 2 no don't know refused
to_factor(v, sort_levels = "l")
## [1] yes 2 2 2 no refused yes no
## [9] 2 <NA>
## Levels: 2 don't know no refused yes
The function to_labelled could be used to turn a factor into a labelled numeric vector.
f <- factor(1:3, labels = c("a", "b", "c"))
to_labelled(f)
## <Labelled double>
## [1] 1 2 3
##
## Labels:
## value label
## 1 a
## 2 b
## 3 c
Note that to_labelled(to_factor(v)) will not be equal to v due to the way factors are
stored internally by R.
v
## <Labelled double>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 8 don't know
## 9 refused
to_labelled(to_factor(v))
## <Labelled double>
## [1] 1 2 2 2 3 5 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 2
## 3 no
## 4 don't know
## 5 refused
In haven package, read_spss, read_stata and read_sas are natively importing data
using the labelled class and the label attribute for variable labels.
Functions from foreign package could also import some metadata from SPSS and Stata
files. to_labelled can convert data imported with foreign into a labelled data frame.
However, there are some limitations compared to using haven:
use.value.labels = FALSE, to.data.frame = FALSE and
use.missings = FALSE when calling read.spss. If use.value.labels = TRUE, variable with
value labels will be converted into factors by read.spss (and kept as factors by foreign_to_label).
If to.data.frame = TRUE, meta data describing the missing values will not be imported.
If use.missings = TRUE, missing values would have been converted to NA by read.spss.convert.factors = FALSE when calling read.dta to avoid conversion
of variables with value labels into factors. So far, missing values defined in Stata are always
imported as NA by read.dta and could not be retrieved by foreign_to_labelled.The memisc package provide functions to import variable metadata and store them in specific
object of class data.set. The to_labelled method can convert a data.set into a labelled data frame.
# from foreign
library(foreign)
df <- to_labelled(read.spss(
"file.sav",
to.data.frame = FALSE,
use.value.labels = FALSE,
use.missings = FALSE
))
df <- to_labelled(read.dta(
"file.dta",
convert.factors = 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)