On reproducibility of computational methods and analyses

Reproducibility is one key aspect of the scientific method, and approaches to guarantee it are continuously developed and improved upon. While reproducing results is one (final) and highly desirable goal, it all starts with raw data, and some steps to get to a final outcome (e.g. model output and diagnostics, visualization, etc.). These steps typically require dedicated software, together with a “recipe”.

The series of posts listed below will deal with approaches (of varying complexity) to allow an entire analyses (from raw data to final report/publication) to be reproduced by anyone (with a little bit of computing knowledge) in R.