The purpose of the labelled package is to provide
functions to manipulate metadata as variable labels, value labels and
defined missing values using the haven_labelled and
haven_labelled_spss classes introduced in
haven package.
These classes allow to add metadata (variable, value labels and SPSS-style missing values) to vectors.
It should be noted that value labels doesn’t imply that your vectors should be considered as categorical or continuous. Therefore, value labels are not intended to be use for data analysis. For example, before performing modeling, you should convert vectors with value labels into factors or into classic numeric/character vectors.
Therefore, two main approaches could be considered.
In approach A, haven_labelled vectors
are converted into factors or into numeric/character vectors just after
data import, using unlabelled(), to_factor()
or unclass(). Then, data cleaning, recoding and analysis
are performed using classic R vector types.
In approach B, haven_labelled vectors
are kept for data cleaning and coding, allowing to preserved original
recoding, in particular if data should be reexported after that step.
Functions provided by labelled will be useful for managing
value labels. However, as in approach A, haven_labelled
vectors will have to be converted into classic factors or numeric
vectors before data analysis (in particular modeling) as this is the way
categorical and continuous variables should be coded for analysis
functions.
Variable labels
A variable label could be specified for any vector using
var_label().
It’s possible to add a variable label to several columns of a data frame using a named list.
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"
##
## $Species
## NULLTo remove a variable label, use NULL.
var_label(iris$Sepal.Length) <- NULLIn RStudio, variable labels will be displayed in data viewer.
View(iris)You can display and search through variable names and labels with
look_for():
look_for(iris)## pos variable label col_type missing values
## 1 Sepal.Length — dbl 0
## 2 Sepal.Width — dbl 0
## 3 Petal.Length Length of petal dbl 0
## 4 Petal.Width Width of Petal dbl 0
## 5 Species — fct 0 setosa
## versicolor
## virginica
look_for(iris, "pet")## pos variable label col_type missing values
## 3 Petal.Length Length of petal dbl 0
## 4 Petal.Width Width of Petal dbl 0
look_for(iris, details = FALSE)## pos variable label
## 1 Sepal.Length —
## 2 Sepal.Width —
## 3 Petal.Length Length of petal
## 4 Petal.Width Width of Petal
## 5 Species —Value labels
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>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 8 don't know
## 9 refusedUse 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>[10]>
## [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>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 5 bugWith val_label(), you can also add or remove specific
value labels.
## <labelled<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 2 maybeTo remove all value labels, use val_labels() and
NULL. The haven_labelled class will also be
removed.
val_labels(v) <- NULL
v## [1] 1 2 2 2 3 9 1 3 2 NAAdding a value label to a non labelled vector will apply
haven_labelled class to it.
val_label(v, 1) <- "yes"
v## <labelled<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yesNote that applying val_labels() to a factor will
generate an error!
f <- factor(1:3)
f## [1] 1 2 3
## Levels: 1 2 3
val_labels(f) <- c(yes = 1, no = 3)## Error in `val_labels<-.factor`(`*tmp*`, value = c(yes = 1, no = 3)): Value labels cannot be applied to factors.You could also apply val_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
## NULLSorting value labels
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>[10]>
## [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 knowIt could be useful to reorder the value labels according to their
attached values, with sort_val_labels().
## <labelled<double>[10]>
## [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>[10]>
## [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 yesIf you prefer, you can also sort them according to the labels.
sort_val_labels(v, according_to = "l")## <labelled<double>[10]>
## [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 yesUser defined missing values (SPSS’s style)
haven (>= 2.0.0) introduced an additional
haven_labelled_spss class to deal with user defined missing
values. In such case, additional attributes will be used to indicate
with values should be considered as missing, but such values will not be
stored as internal NA values. You should note that most R
function will not take this information into account. Therefore, you
will have to convert missing values into NA if required
before analysis. These defined missing values could co-exist with
internal NA values.
It is possible to manipulate this missing values with
na_values() and na_range(). Note that
is.na() will return TRUE as well for
user-defined missing values.
## <labelled<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 9 don't know## [1] 9
v## <labelled_spss<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
## Missing values: 9
##
## Labels:
## value label
## 1 yes
## 3 no
## 9 don't know
is.na(v)## [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
na_values(v) <- NULL
v## <labelled<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 3 no
## 9 don't know## [1] 5 Inf
v## <labelled_spss<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
## Missing range: [5, Inf]
##
## Labels:
## value label
## 1 yes
## 3 no
## 9 don't knowSince version 2.1.0, it is not mandatory to define at least one value label before defining missing values.
## <labelled_spss<double>[4]>
## [1] 1 2 2 9
## Missing values: 9To convert user defined missing values into NA, simply
use user_na_to_na().
v <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
v## <labelled_spss<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 9 10
## Missing values: 9, 10
##
## Labels:
## value label
## 1 Good
## 8 Bad
v2 <- user_na_to_na(v)
v2## <labelled<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 NA NA
##
## Labels:
## value label
## 1 Good
## 8 BadYou can also remove user missing values definition without converting
these values to NA.
v <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
v## <labelled_spss<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 9 10
## Missing values: 9, 10
##
## Labels:
## value label
## 1 Good
## 8 Bad
v2 <- remove_user_na(v)
v2## <labelled<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 9 10
##
## Labels:
## value label
## 1 Good
## 8 Bador
v <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
v## <labelled_spss<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 9 10
## Missing values: 9, 10
##
## Labels:
## value label
## 1 Good
## 8 Bad
na_values(v) <- NULL
v## <labelled<integer>[10]>
## [1] 1 2 3 4 5 6 7 8 9 10
##
## Labels:
## value label
## 1 Good
## 8 BadOther conversion to NA
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.
## <labelled<double>[10]>
## [1] 1 2 2 2 3 9 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 maybe
## 3 no## <labelled<double>[10]>
## [1] 1 2 2 2 3 NA 1 3 2 NA
##
## Labels:
## value label
## 1 yes
## 2 maybe
## 3 noIn other cases, a value label is attached only to specific values that corresponds to a missing value. For example:
## <labelled<double>[5]>
## [1] 1.88 1.62 1.78 99.00 1.91
##
## Labels:
## value label
## 99 not measuredIn such cases, val_labels_to_na() could be
appropriate.
val_labels_to_na(size)## [1] 1.88 1.62 1.78 NA 1.91These two functions could also be applied to an overall data frame. Only labelled vectors will be impacted.
Converting to factor
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>[10]>
## [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 2
## [10] <NA>
## Levels: yes 2 no don't know refusedThe 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 [9] refused
## [7] [1] yes [3] no [2] 2 <NA>
## Levels: [1] yes [2] 2 [3] no [8] don't know [9] refusedThe ordered argument will create an ordinal factor.
to_factor(v, ordered = TRUE)## [1] yes 2 2 2 no refused yes no 2
## [10] <NA>
## Levels: yes < 2 < no < don't know < refusedThe 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 <NA>
## [10] <NA>
## Levels: yes no don't know refused## [1] yes <NA> <NA> <NA> no refused yes no <NA>
## [10] <NA>
## Levels: yes no don't know refusedsort_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 2
## [10] <NA>
## Levels: yes no don't know refused 2
to_factor(v, sort_levels = "v")## [1] yes 2 2 2 no refused yes no 2
## [10] <NA>
## Levels: yes 2 no don't know refused
to_factor(v, sort_levels = "l")## [1] yes 2 2 2 no refused yes no 2
## [10] <NA>
## Levels: 2 don't know no refused yesThe 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>[3]>
## [1] 1 2 3
##
## Labels:
## value label
## 1 a
## 2 b
## 3 cNote 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>[10]>
## [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>[10]>
## [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 refusedOther type of conversions
You can use to_character() for converting into a
character vector instead of a factor.
v## <labelled<double>[10]>
## [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_character(v)## [1] "yes" "2" "2" "2" "no" "refused" "yes"
## [8] "no" "2" NATo remove the haven_class, you can simply use
unclass().
unclass(v)## [1] 1 2 2 2 3 9 1 3 2 NA
## attr(,"labels")
## yes no don't know refused
## 1 3 8 9Note that value labels will be preserved as an attribute to the vector.
## [1] 1 2 2 2 3 9 1 3 2 NATo remove value labels, use remove_val_labels().
## [1] 1 2 2 2 3 9 1 3 2 NANote that if your vector does have user-defined missing values, you
may also want to use remove_user_na().
x <- c(1, 2, 2, 9)
na_values(x) <- 9
val_labels(x) <- c(yes = 1, no = 2)
var_label(x) <- "A test variable"
x## <labelled_spss<double>[4]>: A test variable
## [1] 1 2 2 9
## Missing values: 9
##
## Labels:
## value label
## 1 yes
## 2 no## <labelled_spss<double>[4]>: A test variable
## [1] 1 2 2 9
## Missing values: 9## <labelled<double>[4]>: A test variable
## [1] 1 2 2 9
##
## Labels:
## value label
## 1 yes
## 2 no
remove_user_na(x, user_na_to_na = TRUE)## <labelled<double>[4]>: A test variable
## [1] 1 2 2 NA
##
## Labels:
## value label
## 1 yes
## 2 no## [1] 1 2 2 9
## attr(,"label")
## [1] "A test variable"
unclass(x)## [1] 1 2 2 9
## attr(,"labels")
## yes no
## 1 2
## attr(,"na_values")
## [1] 9
## attr(,"label")
## [1] "A test variable"You can remove all labels and user-defined missing values with
remove_labels(). Use keep_var_label = TRUE to
preserve only variable label.
remove_labels(x, user_na_to_na = TRUE)## [1] 1 2 2 NA
remove_labels(x, user_na_to_na = TRUE, keep_var_label = TRUE)## [1] 1 2 2 NA
## attr(,"label")
## [1] "A test variable"Conditional conversion to factors
For any analysis, it is the responsibility of user to identify which
labelled numeric vectors should be considered as
categorical (and therefore converted into factors using
to_factor()) and which variables should be treated as
continuous (and therefore unclassed into numeric using
base::unclass()).
It should be noted that most functions expect categorical variables
to be coded as factors. It includes most modeling functions (such as
stats::lm() or stats::glm()) or plotting
functions from ggplot2.
In most of cases, if data documentation was properly done, categorical variables corresponds to vectors where all observed values have a value label while vectors where only few values have a value label should be considered as continuous.
In that situation, you could apply the unlabelled()
method to an overall data frame. By default, unlabelled()
works as follow:
- if a column doesn’t inherit the
haven_labelledclass, it will be not affected; - if all observed values have a corresponding value label, the column
will be converted into a factor (using
to_factor()); - otherwise, the column will be unclassed (and converted back to a
numeric or character vector by applying
base::unclass()).
df <- data.frame(
a = labelled(c(1, 1, 2, 3), labels = c(No = 1, Yes = 2)),
b = labelled(c(1, 1, 2, 3), labels = c(No = 1, Yes = 2, DK = 3)),
c = labelled(c(1, 1, 2, 2), labels = c(No = 1, Yes = 2, DK = 3)),
d = labelled(c("a", "a", "b", "c"), labels = c(No = "a", Yes = "b")),
e = labelled_spss(
c(1, 9, 1, 2),
labels = c(No = 1, Yes = 2),
na_values = 9
)
)
df %>% look_for()## pos variable label col_type missing values
## 1 a — dbl+lbl 0 [1] No
## [2] Yes
## 2 b — dbl+lbl 0 [1] No
## [2] Yes
## [3] DK
## 3 c — dbl+lbl 0 [1] No
## [2] Yes
## [3] DK
## 4 d — chr+lbl 0 [a] No
## [b] Yes
## 5 e — dbl+lbl 1 [1] No
## [2] Yes
unlabelled(df) %>% look_for()## pos variable label col_type missing values
## 1 a — dbl 0
## 2 b — fct 0 No
## Yes
## DK
## 3 c — fct 0 No
## Yes
## DK
## 4 d — chr 0
## 5 e — fct 1 No
## Yes
unlabelled(df, user_na_to_na = TRUE) %>% look_for()## pos variable label col_type missing values
## 1 a — dbl 0
## 2 b — fct 0 No
## Yes
## DK
## 3 c — fct 0 No
## Yes
## DK
## 4 d — chr 0
## 5 e — fct 1 No
## Yes
unlabelled(df, drop_unused_labels = TRUE) %>% look_for()## pos variable label col_type missing values
## 1 a — dbl 0
## 2 b — fct 0 No
## Yes
## DK
## 3 c — fct 0 No
## Yes
## 4 d — chr 0
## 5 e — fct 1 No
## YesImporting labelled data
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:
- For SPSS files, it will be better to set
use.value.labels = FALSE,to.data.frame = FALSEanduse.missings = FALSEwhen callingread.spss. Ifuse.value.labels = TRUE, variable with value labels will be converted into factors byread.spss(and kept as factors byforeign_to_label). Ifto.data.frame = TRUE, meta data describing the missing values will not be imported. Ifuse.missings = TRUE, missing values would have been converted toNAbyread.spss. - For Stata files, set
convert.factors = FALSEwhen callingread.dtato avoid conversion of variables with value labels into factors. So far, missing values defined in Stata are always imported asNAbyread.dtaand could not be retrieved byforeign_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)Using labelled with dplyr/magrittr
If you are using the %>% operator, you can use the
functions set_variable_labels(),
set_value_labels(), add_value_labels() and
remove_value_labels().
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
df <- data_frame(s1 = c("M", "M", "F"), s2 = c(1, 1, 2)) %>%
set_variable_labels(s1 = "Sex", s2 = "Question") %>%
set_value_labels(s1 = c(Male = "M", Female = "F"), s2 = c(Yes = 1, No = 2))## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## ℹ Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
df$s2## <labelled<double>[3]>: Question
## [1] 1 1 2
##
## Labels:
## value label
## 1 Yes
## 2 Noset_value_labels() will replace the list of value labels
while add_value_labels() will update it.
df <- df %>%
set_value_labels(s2 = c(Yes = 1, "Don't know" = 8, Unknown = 9))
df$s2## <labelled<double>[3]>: Question
## [1] 1 1 2
##
## Labels:
## value label
## 1 Yes
## 8 Don't know
## 9 Unknown
df <- df %>%
add_value_labels(s2 = c(No = 2))
df$s2## <labelled<double>[3]>: Question
## [1] 1 1 2
##
## Labels:
## value label
## 1 Yes
## 8 Don't know
## 9 Unknown
## 2 NoYou can also remove some variable and/or value labels.
df <- df %>%
set_variable_labels(s1 = NULL)
# removing one value label
df <- df %>%
remove_value_labels(s2 = 2)
df$s2## <labelled<double>[3]>: Question
## [1] 1 1 2
##
## Labels:
## value label
## 1 Yes
## 8 Don't know
## 9 Unknown
# removing several value labels
df <- df %>%
remove_value_labels(s2 = 8:9)
df$s2## <labelled<double>[3]>: Question
## [1] 1 1 2
##
## Labels:
## value label
## 1 Yes
# removing all value labels
df <- df %>%
set_value_labels(s2 = NULL)
df$s2## [1] 1 1 2
## attr(,"label")
## [1] "Question"To convert variables, the easiest is to use
unlabelled().
## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ region <dbl+lbl> 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, …
## $ instruction <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 0, …
## $ employed <dbl+lbl> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ matri <dbl+lbl> 0, 2, 2, 2, 1, 0, 1, 1, 2, 5, 2, 3, 0, 2, 1, 2, …
## $ religion <dbl+lbl> 1, 3, 2, 3, 2, 2, 3, 1, 3, 3, 2, 3, 2, 2, 2, 2, …
## $ newspaper <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ radio <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, …
## $ tv <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, …
## $ ideal_nb_children <dbl+lbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, …
## $ test <dbl+lbl> 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, …
glimpse(women %>% unlabelled())## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <fct> rural, rural, rural, rural, rural, rural, rural, rur…
## $ region <fct> West, West, West, West, West, South, South, South, S…
## $ instruction <fct> none, none, none, none, primary, none, none, none, n…
## $ employed <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes, yes, …
## $ matri <fct> single, living together, living together, living tog…
## $ religion <fct> Muslim, Protestant, Christian, Protestant, Christian…
## $ newspaper <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, …
## $ radio <fct> no, yes, yes, no, no, yes, yes, no, no, no, yes, yes…
## $ tv <fct> no, no, no, no, no, yes, no, no, no, no, yes, yes, n…
## $ ideal_nb_children <dbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6, 6, 4,…
## $ test <fct> no, missing, no, no, yes, no, no, no, no, yes, yes, …Alternatively, you can use functions as
dplyr::mutate_if() or dplyr::mutate_at(). See
the example below.
## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <fct> rural, rural, rural, rural, rural, rural, rural, rur…
## $ region <fct> West, West, West, West, West, South, South, South, S…
## $ instruction <fct> none, none, none, none, primary, none, none, none, n…
## $ employed <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes, yes, …
## $ matri <fct> single, living together, living together, living tog…
## $ religion <fct> Muslim, Protestant, Christian, Protestant, Christian…
## $ newspaper <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, …
## $ radio <fct> no, yes, yes, no, no, yes, yes, no, no, no, yes, yes…
## $ tv <fct> no, no, no, no, no, yes, no, no, no, no, yes, yes, n…
## $ ideal_nb_children <fct> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6, 6, 4,…
## $ test <fct> no, missing, no, no, yes, no, no, no, no, yes, yes, …## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <fct> rural, rural, rural, rural, rural, rural, rural, rur…
## $ region <fct> West, West, West, West, West, South, South, South, S…
## $ instruction <fct> none, none, none, none, primary, none, none, none, n…
## $ employed <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes, yes, …
## $ matri <fct> single, living together, living together, living tog…
## $ religion <fct> Muslim, Protestant, Christian, Protestant, Christian…
## $ newspaper <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, …
## $ radio <fct> no, yes, yes, no, no, yes, yes, no, no, no, yes, yes…
## $ tv <fct> no, no, no, no, no, yes, no, no, no, no, yes, yes, n…
## $ ideal_nb_children <fct> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6, 6, 4,…
## $ test <fct> no, missing, no, no, yes, no, no, no, no, yes, yes, …## Rows: 2,000
## Columns: 17
## $ id_woman <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 738, 165…
## $ id_household <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 711, 152…
## $ weight <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.803150, 0.…
## $ interview_date <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-06, 201…
## $ date_of_birth <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-29, 198…
## $ age <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45, 25, …
## $ residency <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ region <dbl+lbl> 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, …
## $ instruction <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 0, …
## $ employed <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes, yes, …
## $ matri <fct> single, living together, living together, living tog…
## $ religion <fct> Muslim, Protestant, Christian, Protestant, Christian…
## $ newspaper <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
## $ radio <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, …
## $ tv <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, …
## $ ideal_nb_children <dbl+lbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, …
## $ test <dbl+lbl> 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, …