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.
You can download the manual in PDF.
The package will be available for install via after review on CRAN:
The development version from GitHub can be installed with:
# install.packages("devtools") devtools::install_github("antaldaniel/retroharmonize")
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.
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.
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.
We also provide two extensive case studies illustrating how the
retroharmonize package can be used for ex-post harmonization of data from cross-national surveys on the example of the Afrobarometer and the Eurobarometer. The creators of
retroharmonize are not affiliated with either Afrobarometer, Eurobarometer, or the organizations that designs, produces or archives their surveys.
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.