Introduction to labelled

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.

Variable labels

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)

You can display and search through variable names and labels with look_for():

look_for(iris)

##       variable           label
## 1 Sepal.Length            <NA>
## 2  Sepal.Width            <NA>
## 3 Petal.Length Length of petal
## 4  Petal.Width  Width of Petal
## 5      Species            <NA>

look_for(iris, "pet")

##       variable           label
## 3 Petal.Length Length of petal
## 4  Petal.Width  Width of Petal

look_for(iris, details = TRUE)

##       variable           label   class    type
## 1 Sepal.Length            <NA> numeric  double
## 2  Sepal.Width            <NA> numeric  double
## 3 Petal.Length Length of petal numeric  double
## 4  Petal.Width  Width of Petal numeric  double
## 5      Species            <NA>  factor integer
##                          levels value_labels unique_values n_na na_values
## 1                                                       35    0          
## 2                                                       23    0          
## 3                                                       43    0          
## 4                                                       22    0          
## 5 setosa; versicolor; virginica                          3    0          
##   na_range
## 1         
## 2         
## 3         
## 4         
## 5

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>
##  [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

Sorting 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>
##  [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

User 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 atributes 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.

v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, no = 3, "don't know" = 9))
v

## <Labelled double>
##  [1]  1  2  2  2  3  9  1  3  2 NA
## 
## Labels:
##  value      label
##      1        yes
##      3         no
##      9 don't know

na_values(v) <- 9
na_values(v)

## [1] 9

## <Labelled SPSS double>
##  [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>
##  [1]  1  2  2  2  3  9  1  3  2 NA
## 
## Labels:
##  value      label
##      1        yes
##      3         no
##      9 don't know

na_range(v) <- c(5, Inf)
na_range(v)

## [1]   5 Inf

## <Labelled SPSS double>
##  [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 know

Since version 2.1.0, it is not mandatory to define at least one value label before defining missing values.

x <- c(1, 2, 2, 9)
na_values(x) <- 9
x

To 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>
##  [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>
##  [1]  1  2  3  4  5  6  7  8 NA NA
## 
## Labels:
##  value label
##      1  Good
##      8   Bad

You 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>
##  [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)

## Some user defined missing values have been removed but not converted to NA.

v2

## <Labelled integer>
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## Labels:
##  value label
##      1  Good
##      8   Bad

v <- labelled_spss(1:10, c(Good = 1, Bad = 8), na_values = c(9, 10))
v

## <Labelled SPSS integer>
##  [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>
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## Labels:
##  value label
##      1  Good
##      8   Bad

Other 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.

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.

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>
##  [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.

## <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

Importing 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 = 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.
For Stata files, set 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)

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.

library(dplyr)

## 
## 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()` is deprecated, use `tibble()`.
## This warning is displayed once per session.

df$s2

## <Labelled double>: Question
## [1] 1 1 2
## 
## Labels:
##  value label
##      1   Yes
##      2    No

set_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>: 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>: Question
## [1] 1 1 2
## 
## Labels:
##  value      label
##      1        Yes
##      8 Don't know
##      9    Unknown
##      2         No

You 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>: 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>: 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, you can use functions as mutate_if or mutate_at. See the example below.

library(questionr)

## 
## Attaching package: 'questionr'

## The following object is masked from 'package:labelled':
## 
##     lookfor

data(fertility)
glimpse(women)

## Observations: 2,000
## Variables: 17
## $ id_woman          <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 73…
## $ id_household      <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 71…
## $ weight            <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.8031…
## $ interview_date    <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-0…
## $ date_of_birth     <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-2…
## $ age               <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45…
## $ residency         <dbl+lbl> 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, …
## $ instruction       <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, …
## $ employed          <dbl+lbl> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ matri             <dbl+lbl> 0, 2, 2, 2, 1, 0, 1, 1, 2, 5, 2, 3, 0, 2, …
## $ religion          <dbl+lbl> 1, 3, 2, 3, 2, 2, 3, 1, 3, 3, 2, 3, 2, 2, …
## $ newspaper         <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ radio             <dbl+lbl> 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, …
## $ tv                <dbl+lbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, …
## $ ideal_nb_children <dbl+lbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6…
## $ test              <dbl+lbl> 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, …

glimpse(to_factor(women))

## Observations: 2,000
## Variables: 17
## $ id_woman          <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 73…
## $ id_household      <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 71…
## $ weight            <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.8031…
## $ interview_date    <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-0…
## $ date_of_birth     <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-2…
## $ age               <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45…
## $ residency         <fct> rural, rural, rural, rural, rural, rural, rura…
## $ region            <fct> West, West, West, West, West, South, South, So…
## $ instruction       <fct> none, none, none, none, primary, none, none, n…
## $ employed          <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes,…
## $ matri             <fct> single, living together, living together, livi…
## $ religion          <fct> Muslim, Protestant, Christian, Protestant, Chr…
## $ newspaper         <fct> no, no, no, no, no, no, no, no, no, no, no, no…
## $ radio             <fct> no, yes, yes, no, no, yes, yes, no, no, no, ye…
## $ tv                <fct> no, no, no, no, no, yes, no, no, no, no, yes, …
## $ ideal_nb_children <fct> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6,…
## $ test              <fct> no, missing, no, no, yes, no, no, no, no, yes,…

glimpse(women %>% mutate_if(is.labelled, to_factor))

## Observations: 2,000
## Variables: 17
## $ id_woman          <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 73…
## $ id_household      <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 71…
## $ weight            <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.8031…
## $ interview_date    <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-0…
## $ date_of_birth     <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-2…
## $ age               <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45…
## $ residency         <fct> rural, rural, rural, rural, rural, rural, rura…
## $ region            <fct> West, West, West, West, West, South, South, So…
## $ instruction       <fct> none, none, none, none, primary, none, none, n…
## $ employed          <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes,…
## $ matri             <fct> single, living together, living together, livi…
## $ religion          <fct> Muslim, Protestant, Christian, Protestant, Chr…
## $ newspaper         <fct> no, no, no, no, no, no, no, no, no, no, no, no…
## $ radio             <fct> no, yes, yes, no, no, yes, yes, no, no, no, ye…
## $ tv                <fct> no, no, no, no, no, yes, no, no, no, no, yes, …
## $ ideal_nb_children <fct> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6, 6,…
## $ test              <fct> no, missing, no, no, yes, no, no, no, no, yes,…

glimpse(women %>% mutate_at(vars(employed:tv), to_factor))

## Observations: 2,000
## Variables: 17
## $ id_woman          <dbl> 391, 1643, 85, 881, 1981, 1072, 1978, 1607, 73…
## $ id_household      <dbl> 381, 1515, 85, 844, 1797, 1015, 1794, 1486, 71…
## $ weight            <dbl> 1.803150, 1.803150, 1.803150, 1.803150, 1.8031…
## $ interview_date    <date> 2012-05-05, 2012-01-23, 2012-01-21, 2012-01-0…
## $ date_of_birth     <date> 1997-03-07, 1982-01-06, 1979-01-01, 1968-03-2…
## $ age               <dbl> 15, 30, 33, 43, 25, 18, 45, 23, 49, 31, 26, 45…
## $ residency         <dbl+lbl> 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, …
## $ instruction       <dbl+lbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 2, …
## $ employed          <fct> yes, yes, no, yes, yes, no, yes, no, yes, yes,…
## $ matri             <fct> single, living together, living together, livi…
## $ religion          <fct> Muslim, Protestant, Christian, Protestant, Chr…
## $ newspaper         <fct> no, no, no, no, no, no, no, no, no, no, no, no…
## $ radio             <fct> no, yes, yes, no, no, yes, yes, no, no, no, ye…
## $ tv                <fct> no, no, no, no, no, yes, no, no, no, no, yes, …
## $ ideal_nb_children <dbl+lbl> 4, 4, 4, 4, 4, 5, 10, 5, 4, 5, 6, 10, 2, 6…
## $ test              <dbl+lbl> 0, 9, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, …