mariposa (version 0.7.3)

to_label: Convert Labelled Variables to Factors

Description

Converts haven_labelled variables to factors, using value labels as factor levels. This is the primary function for making labelled survey data ready for plotting, cross-tabulation, and regression analysis.

Usage

to_label(
  data,
  ...,
  ordered = FALSE,
  drop_na = TRUE,
  drop_unused = FALSE,
  add_non_labelled = FALSE,
  drop.na = NULL,
  drop.unused = NULL,
  add.non.labelled = NULL
)

Value

The input with labelled variables converted to factors. For single vector input, returns a factor.

Arguments

data

A data frame, tibble, or a single vector.

...

Optional: unquoted variable names (tidyselect supported). If empty, converts all haven_labelled columns.

ordered

If TRUE, creates an ordered factor. Default: FALSE.

drop_na

If TRUE (default), tagged NAs are converted to regular NA (excluded from factor levels). If FALSE, tagged NAs are kept as factor levels with their label text.

drop_unused

If TRUE, removes factor levels with zero observations. Default: FALSE.

add_non_labelled

If TRUE, values without labels are included as factor levels using their numeric value as the level name. Default: FALSE (unlabelled values become NA).

drop.na, drop.unused, add.non.labelled

Defunct dot-case argument names, removed in mariposa 0.6.9. Calling the function with any of them is an error; use the snake_case equivalents instead. (The formals are retained only so that the old names error clearly instead of being swallowed by ....)

Details

For each labelled variable, the numeric codes are replaced by their associated value labels. The resulting factor levels are ordered by the original numeric values (not alphabetically).

When to Use This

Use to_label() when you:

• Want human-readable labels in plots (ggplot2)

• Need factor variables for regression models

• Want to create frequency tables with label text

See Also

to_character() for character output, to_labelled() for the reverse operation, val_labels() for viewing labels

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

Examples

# Convert a single variable
to_label(survey_data$gender)

# Convert specific columns in a data frame
data <- to_label(survey_data, gender, region)

# Convert all labelled columns
data <- to_label(survey_data)

# Keep values without labels as factor levels
to_label(survey_data$life_satisfaction, add_non_labelled = TRUE)