MetaR: simple, high-level languages for data analysis with the R ecosystem

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1 Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA
2 The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, United States
DOI
10.7287/peerj.preprints.1465v2
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology
Keywords
Data analysis, R language, Bioinformatics, Composable R, Jetbrains MPS, Language Workbench Technology
Copyright
© 2015 Campagne et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Campagne F, Digan WE, Simi M. 2015. MetaR: simple, high-level languages for data analysis with the R ecosystem. PeerJ PrePrints 3:e1465v2

Abstract

Data analysis tools have become essential to the study of biology. Tools available today were constructed with layers of technology developed over decades. Here, we explain how some of the principles used to develop this technology are sub-optimal for the construction of data analysis tools for biologists. In contrast, we applied language workbench technology (LWT) to create a data analysis language, called MetaR, tailored for biologists with no programming experience, as well as expert bioinformaticians and statisticians. A key novelty of this approach is its ability to blend user interface with scripting in such a way that beginners and experts alike can analyze data productively in the same analysis platform. While presenting MetaR, we explain how a judicious use of LWT eliminates problems that have historically contributed to data analysis bottlenecks. These results show that language design with LWT can be a compelling approach for developing intelligent data analysis tools.

Author Comment

This is a preprint submission to PeerJ Preprints. In this version, we added a new discussion item: Impact on Development of User Proficiency.