Hugo Gruson
Lead Software Architect at data.org
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Hugo is a professional developer and happy R community member. He has developed and maintains packages across many fields, such as evolutionary biology, epidemiology, statistics or reproducible science and contributes to the community via blog posts or pull requests to existing packages.
Over the past two years, he has taken an ecosystem view rather than working on individual packages, and works toward improvement of interoperability and sustainability in the community.
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Area of Expertise
Building interoperability in existing software ecosystems with S3 classes
It is common for R packages answering the same need to have different input and output formats. This may result in a large amount of spent time to reformat the inputs and outputs whenever a specific part of the data pipeline is swapped out to use a different R package. This time can come at a huge cost whenever results are needed quickly, such as in pandemic response.
Using S3 classes providing standard formats that all downstream packages use may be a good solution to this issue, thus improving the interoperability within the global R package ecosystem.
However, this approach comes with technical and social challenges. Here, I present the work we are doing to implement and encourage the adoption of standard S3 classes in epidemiology. I highlight key findings and challenges such as how to preserve backward compatibility in existing packages and give recommendation for future similar endeavors.
A reproducible analysis of CRAN Task Views to understand the state of an R package ecosystem
The research community is increasingly aware of the need to apply software engineering best practices to scientific software. This however doesn't mean that we should discard the huge ecosystem of existing tools with large, well-established, user bases. Instead, efforts should be dedicated to integrate best practices in existing tools where possible. But this can only be done if we have a clear idea of the current state of the ecosystem, with its gaps and needs.
In this presentation, I will describe the analysis we have conducted on the ecosystem of R packages for Epidemiology, as represented by the CRAN Task View in Epidemiology. It allows us to draw a picture of where efforts to support this ecosystem should focus. This also informs future training needs for this research community, and maps a path for external contributions to packages that wish it.
Importantly, this analysis is made reproducible and applicable to any CRAN Task View out of the box, which allows research and software communities from other fields to conduct the same assessment on their own domain.
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