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labelled

This package is built on the new classes haven_labelled and haven_labelled_spss introduced by haven package to handle labelled variables imported from SPSS, Stata and SAS. The labelled package propose several functions to manipulate such vectors and their metadata: variable labels, value labels and user-defined missing values.

Installation & Documentation

To install stable version:

install.packages("labelled")

Documentation of stable version: https://larmarange.github.io/labelled/

To install development version:

remotes::install_github("larmarange/labelled")

Documentation of development version: https://larmarange.github.io/labelled/dev/

Introduction

Read the vignette at https://larmarange.github.io/labelled/articles/intro_labelled.html

Cheatsheet

Some general guidelines

  1. Functions are intended to support labelled metadata structures only. However, to_labelled() method allows to convert metadata from foreign and memisc packages.
  2. Functions should, by default, modify metadata only (i.e. classes and attributes), except if explicitly expressed by the user.

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Version

Install

install.packages('labelled')

Monthly Downloads

64,724

Version

2.11.0

License

GPL (>= 3)

Last Published

April 11th, 2023

Functions in labelled (2.11.0)

sort_val_labels

Sort value labels
unique_tagged_na

Unique elements, duplicated, ordering and sorting with tagged NAs
to_factor

Convert input to a factor.
to_character

Convert input to a character vector
to_labelled

Convert to labelled data
remove_attributes

Remove attributes
update_labelled

Update labelled data to last version
remove_labels

Remove variable label, value labels and user defined missing values
x_haven_2.0

Datasets for testing
tagged_na_to_user_na

Convert tagged NAs into user NAs
val_labels

Get / Set value labels
val_labels_to_na

Recode value labels to NA
var_label

Get / Set a variable label
nolabel_to_na

Recode values with no label to NA
reexports

Objects exported from other packages
names_prefixed_by_values

Turn a named vector into a vector of names prefixed by values
na_values

Get / Set SPSS missing values
recode.haven_labelled

Recode values
recode_if

Recode some values based on condition
drop_unused_value_labels

Drop unused value labels
look_for

Look for keywords variable names and descriptions / Create a data dictionary
is_prefixed

Check if a factor is prefixed
copy_labels

Copy variable and value labels and SPSS-style missing value