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2020 has been a sad and difficult year for many and certainly unusual for all of us. For us at Discindo, the adjustments required by limited mobility and closures were not easy, but our work is mostly online and remote anyway, so we are surely much better off than most.

One of the activities we were involved this year was to contribute to an ongoing project that involved several in-person collaborative sessions and workshops with a broader audience. At the beginning of the year, the Free Software Macedonia NGO (of which we are members, and which hosts the usual in-person RSkopje meetups.) was awarded a grant from Civica Mobilitas to organize introductory workshops for open data and reproducible research using tools in the R and Python spheres. As 2020 progressed, and it became clear that in person training workshops at FSM’s hacklab KIKA were out of the question, we reorganized the activities to happen online. We had a total of five teleconference workshops on the basics of reproducible data analysis using R and git tools like Rmd, ggplot, gh-pages. Although we feel like the workshop attendance could have been better, we had a great first experience with this type of educational project, and we learned a lot in the process.

One of the project’s deliverables was a short introductory guide for reproducible data analysis. We wrote this document over the past two months, and recently it had its release and promotion in a small online press conference. The booklet (as we have come to call it) can be accessed here and the bookdown source code is at this Discindo git repository. Here we’ll briefly talk about the content and future plans for this guide, and even more briefly reflect on our experience in writing it.

The content of the booklet was guided by the workshop schedule. An important aspect for us was to show an entire workflow starting with some analysis in an interactive session, that gets converted to a script, then to a rmarkdown report rendered to a static HTML, that finally gets deployed on gh-pages as a public record of the analysis. In line with this, in the first couple of chapters, we introduced the topic of open data and reproducible research and discussed the toolkit we would use throught the book, R and git. Of course, R has a wealth of resources for reproducible researh, whether on the side of literate programming (rmarkdown, knitr, flexdashboard, and many more) or on the side of project and workflow management (goodpractices, devtools, drake, workflowr, and many more). Of course, this owes a lot to the very active community of researchers, data scientists, and software engineers who use R and care about open data, transparent analyses, and reproducibility, for example the rOpenSci organization.

Thereafter, through a simple hypothetical scenario, we introduce how and why seemingly simple tasks and code can be or can become after some time irreproducible. We talk about the importance of dependency management, absolute paths, and documentation. We then discuss some ways in which a simple script whose result cannot be reproduced outside of the context where it was created can be converted to a bit more robust, documented program, that makes minimal assumptions about the environment in which it is going to be used. With the main principles of reproducibility out of the way, we introduce literate programming and parametrized Rmd reports, which are a great way to ‘scale’ an analysis while maintaining simple and manageable report code. We also discuss organization of code and data in a bundle (package) that be shared such that even someone that does not have the exact inputs required for a particular analysis, can nonetheless carry out our analysis, as is very common in the sciences with data+code repositories like DataDryad and Zenodo. We wrap up this first version of the guide with a quick intro to git and a tutorial for publishing a locally generated HTML report as a simple webpage using GitHub and gh-pages.

Our future plans include revising this first version after some reader feedback and discussing what would be the most useful additions. The booklet is rather thin on practical code at this time. One option for expansion would be to include one or two realistic worked examples of making a reproducible project with data and code. Another possibly interesting idea is to talk about Python analogs to the R tools and workflows we discuss. Adding a chapter on make– and git-based workflows with tools like drake and workflowr, as well as a chapter on docker are definitive additions for the near to mid-term future of this guide.