Complete reproduction of a study through the use of Github, Docker and R package

What you will learn

A multi-pronged approach to make code and data easy to access, make the entire analysis available, ensure the computational environment is archived, and make the code useful to a wide audience.  The tools include making all code available on Github; creating a fully documented R package on CRAN to allow the primary algorithms from the project to be easily used by others; creating a supplementary R package that contains the data and helper functions to produce plots; and creating a docker image that contains archived versions of the R packages used in the analysis, as well as all other software and code necessary to exactly reproduce all of the analyses.

Authors

Johann Gagnon-Bartsch, Greg Hunt, and collaborators

Fully Reproducible Projects, Generalizable Tools