Reproducibility is a hot topic in science nowadays (e.g. see this Nature special). Some argue that we are in the middle of a ‘reproducibility crisis’, and thus scientists are being strongly encouraged to increase the reproducibility of their research.
As a side product (or trailer) of our paper on reproducible science, we made a video promoting reproducible workflows. Particularly, showing how using Git and Rmarkdown make your research and scientific collaboration way much easier and better, compared to a typical (non-reproducible) workflow involving Excel, Word, some figure production software, and a lot of manual steps.
Most scientific papers are not reproducible: it is really hard, if not impossible, to understand how results are derived from data, and being able to regenerate them in the future (even by the same researchers). However, traceability and …
Rmarkdown is a great tool for reproducible science. You can combine text and code to produce dynamic reports that generate updated results with a single click, as in the example below.
I have supervised my first master project this year. The project is coming to an end, and I am very happy with the results as well as the fully reproducible workflow we have followed: all developed on GitHub using R package structure and Rmarkdown.
Science has a big reproducibility problem: hardly anyone can reproduce (i.e. re-run, re-obtain) the results of most published papers (including authors themselves!). That is a big problem not only for science as a collective enterprise but also for scientists' everyday life (‘how did I do this?