Abstract
The analysis of biological data is an important part of modern biological and medical research. Many statistical and computational tools are available for statisticians and data scientists, providing them with both computational power and flexibility. However, these tools are often not suitable for biomedical researchers performing data processing and simple analyses. Learning to code is indeed challenging and quite difficult when coming from a non-technical background.This chapter introduces MetaR, a tool designed to help biologists and clinicians to learn, and perform, the basis of data analysis. Originally created as an educational software, it eventually evolved into a mature and stable Domain Specific Language (DSL) used to support various aspects of a research project up to the creation of figures suitable for scientific publications. MetaR generates to R code, but users do not even see it. By providing high-level abstractions, MetaR hides most of the details of each step in the data analysis process. Because of its capability to blend scripting and graphical elements, it provides a novel approach to the practice of analyzing data. Simplified executions and syntax-completion are additional features that make MetaR easy to use for beginners. Language composition is available for advanced users that wish to contribute and extend the project.In our data-rich age, there is a great divide between biomedical researchers and data analysts. MetaR has proved to be an educational bridge between these two worlds.
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Acknowledgments
Work reported in this chapter was supported by the National Center for Advancing Translational Science of the National Institute of Health under awards number UL1RR024996 and UL1TR002384. Additional support was provided by the National Institute of Health NIAID award number 5R01AI107762-02 to Fabien Campagne.
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Simi, M. (2021). Learning Data Analysis with MetaR. In: Bucchiarone, A., Cicchetti, A., Ciccozzi, F., Pierantonio, A. (eds) Domain-Specific Languages in Practice. Springer, Cham. https://doi.org/10.1007/978-3-030-73758-0_9
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DOI: https://doi.org/10.1007/978-3-030-73758-0_9
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