retroharmonize

The goal of retroharmonize is to facilitate retrospective (ex-post) harmonization of data, particularly survey data, in a reproducible manner. The package provides tools for organizing the metadata, standardizing the coding of variables, variable names and value labels, including missing values, and for documenting all transformations, with the help of comprehensive S3 classes.

Currently being generalized from problems solved in the not yet released eurobarometer package (doi.)

Installation

The package is available on CRAN:

install.packages("retroharmonize")

The development version has new features with the create_codebook() functions. It can be installed from GitHub with:

# install.packages("devtools")
devtools::install_github("rOpenGov/retroharmonize")

You can download the manual in PDF for the 0.2.0 release.

Retrospective data harmonization

The aim of retroharmonize is to provide tools for reproducible retrospective (ex-post) harmonization of datasets that contain variables measuring the same concepts but coded in different ways. Ex-post data harmonization enables better use of existing data and creates new research opportunities. For example, harmonizing data from different countries enables cross-national comparisons, while merging data from different time points makes it possible to track changes over time.

Retrospective data harmonization is associated with challenges including conceptual issues with establishing equivalence and comparability, practical complications of having to standardize the naming and coding of variables, technical difficulties with merging data stored in different formats, and the need to document a large number of data transformations. The retroharmonize package assists with the latter three components, freeing up the capacity of researchers to focus on the first.

Specifically, the retroharmonize package proposes a reproducible workflow, including a new class for storing data together with the harmonized and original metadata, as well as functions for importing data from different formats, harmonizing data and metadata, documenting the harmonization process, and converting between data types. See here for an overview of the functionalities.

The new labelled_spss_survey() class is an extension of haven’s labelled_spss class. It not only preserves variable and value labels and the user-defined missing range, but also gives an identifier, for example, the filename or the wave number, to the vector. Additionally, it enables the preservation – as metadata attributes – of the original variable names, labels, and value codes and labels, from the source data, in addition to the harmonized variable names, labels, and value codes and labels. This way, the harmonized data also contain the pre-harmonization record. The stored original metadata can be used for validation and documentation purposes.

The vignette Working With The labelled_spss_survey Class provides more information about the labelled_spss_survey() class.

In Harmonize Value Labels we discuss the characteristics of the labelled_spss_survey() class and demonstrates the problems that using this class solves.

We also provide three extensive case studies illustrating how the retroharmonize package can be used for ex-post harmonization of data from cross-national surveys:

• Afrobarometer
• Arab Barometer
• Eurobarometer

The creators of retroharmonize are not affiliated with either Afrobarometer, Arab Barometer, Eurobarometer, or the organizations that designs, produces or archives their surveys.

We started building an experimental APIs data is running retroharmonize regularly and improving known statistical data sources. See: Digital Music Observatory, Green Deal Data Observatory, Economy Data Observatory.

Citations and related work

Citing the data sources

Our package has been tested on three harmonized survey’s microdata. Because retroharmonize is not affiliated with any of these data sources, to replicate our tutorials or work with the data, you have download the data files from these sources, and you have to cite those sources in your work.

Afrobarometer data: Cite Afrobarometer Arab Barometer data: cite Arab Barometer. Eurobarometer data: The Eurobarometer data Eurobarometer raw data and related documentation (questionnaires, codebooks, etc.) are made available by GESIS, ICPSR and through the Social Science Data Archive networks. You should cite your source, in our examples, we rely on the GESIS data files.

Citing the retroharmonize R package

For main developer and contributors, see the package homepage.

This work can be freely used, modified and distributed under the GPL-3 license:

citation("retroharmonize")
#> 
#> To cite package 'retroharmonize' in publications use:
#> 
#>   Daniel Antal (2021). retroharmonize: Ex Post Survey Data
#>   Harmonization. https://retroharmonize.dataobservatory.eu/,
#>   https://ropengov.github.io/retroharmonize/,
#>   https://github.com/rOpenGov/retroharmonize.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {retroharmonize: Ex Post Survey Data Harmonization},
#>     author = {Daniel Antal},
#>     year = {2021},
#>     note = {https://retroharmonize.dataobservatory.eu/,
#> https://ropengov.github.io/retroharmonize/,
#> https://github.com/rOpenGov/retroharmonize},
#>   }

Contact

For contact information, see the package homepage.

Code of Conduct

Please note that the retroharmonize project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.