Context: In response to an earlier question about reproducible research Jake wrote
One problem we discovered when creating our JASA archive was that versions and defaults of CRAN packages changed. So, in that archive, we also include the versions of the packages that we used. The vignette based system will probably break as folks change their packages (not sure how to include extra packages within the package that is the Compendium).
Finally, I wonder about what to do when R itself changes. Are there ways to produce, say, a virtual machine that reproduces the entire computational environment used for a paper such that the virtual machine is not enormous?
- What are good strategies for ensuring that reproducible data analysis is reproducible in the future (say, five, ten, or twenty years after publication)?
- Specifically, what are good strategies for maximising ongoing reproducibility when using Sweave and R?
This seems to be related to the issue of ensuring that a reproducible data analysis project will run on someone else's machine with slightly different defaults, packages, etc.