A recent episode of Not So Standard Deviations (a great podcast for anyone in the sciences or applied statistics) covered reproducibility: https://soundcloud.c

A recent episode of Not So Standard Deviations (a great podcast for anyone in the sciences or applied statistics) covered reproducibility: https://soundcloud.com/nssd-podcast/episode-5-irl-roger-is-totally-with-it https://soundcloud.com/nssd-podcast/episode-5-irl-roger-is-t... I'd suggest treating your code like a software project: convert repeated logic into methods, collect methods into modules/libraries depending on their use, write lots of documentation, and use version control (Github with a nice README.md in each project is a great start). If you transition projects, take 5-10 minutes to update your docs (I keep a "captain's log") with the latest details and a list of todo items. I like to note my victories ("on 1/19 I produced this plot that sent me in a different direction; on 1/21 I demoed my project and received such-and-such feedback") and next steps in the log, as a way to retrace my progress over weeks or months. That podcast also mentions knitr (http://yihui.name/knitr/ http://yihui.name/knitr/), which looks great for docs.