Get or Set Variable Labels

Retrieves or assigns variable labels (the "label" attribute) on vectors or data frame columns. Variable labels describe what a variable measures (e.g., "Age of respondent") and are commonly used in survey data imported from SPSS, Stata, or SAS.

The function operates in two modes:

• GET mode: Called with bare variable names or no ... arguments, returns existing labels.

• SET mode: Called with name = "label" pairs, assigns labels to variables and returns the modified data.

Usage

var_label(data, ...)

Arguments

data

A data frame, tibble, or a single vector.

...

In GET mode: unquoted variable names (tidyselect supported). If empty, returns labels for all variables. In SET mode: named pairs where the name is a variable and the value is the label string. Use NULL to remove a label.

Value

• GET mode (vector input): A single character string (the label), or NULL if no label exists.

• GET mode (data frame input): A named character vector of labels. Variables without labels return NA.

• SET mode: The modified data frame (invisibly), with labels assigned.

Details

Variable labels are stored as the "label" attribute on each column, which is the standard used by the haven package for SPSS/Stata/SAS imports. These labels are preserved by mariposa's codebook(), frequency(), and describe() functions.

When to Use This

Use var_label() when you:

• Want to inspect what variables measure in imported survey data

• Need to add labels to manually created data before using codebook() or write_spss()

• Want to update or correct labels after import

See also

val_labels() for value labels, codebook() for viewing all metadata, copy_labels() for preserving labels after dplyr operations

Other labels: copy_labels(), drop_labels(), find_var(), set_na(), to_character(), to_label(), to_labelled(), to_numeric(), unlabel(), val_labels()

Examples

# GET: retrieve all variable labels
var_label(survey_data)
#>                                                id 
#>                                                NA 
#>                                               age 
#>                                    "Age in years" 
#>                                            gender 
#>                                          "Gender" 
#>                                            region 
#>                              "Region (East/West)" 
#>                                         education 
#>                  "Highest educational attainment" 
#>                                            income 
#>                  "Monthly household income (EUR)" 
#>                                        employment 
#>                               "Employment status" 
#>                             political_orientation 
#>         "Political orientation (1=left, 5=right)" 
#>                             environmental_concern 
#>           "Environmental concern (1=low, 5=high)" 
#>                                 life_satisfaction 
#> "Life satisfaction (1=dissatisfied, 5=satisfied)" 
#>                                  trust_government 
#>        "Trust in government (1=none, 5=complete)" 
#>                                       trust_media 
#>             "Trust in media (1=none, 5=complete)" 
#>                                     trust_science 
#>           "Trust in science (1=none, 5=complete)" 
#>                                   sampling_weight 
#>                                "Weighting factor" 
#>                                           stratum 
#>                         "Stratification variable" 
#>                                    interview_mode 
#>                                  "Interview mode" 

# GET: specific variables
var_label(survey_data, age, gender)
#>            age         gender 
#> "Age in years"       "Gender" 

# GET: from a single vector
var_label(survey_data$age)
#> [1] "Age in years"

# SET: assign labels
data <- var_label(survey_data,
  age = "Age of respondent",
  gender = "Gender identity"
)

# REMOVE: set to NULL
data <- var_label(data, age = NULL)