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> **Possible Duplicate:**  
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> [Workflow for statistical analysis and report writing](https://stackoverflow.com/questions/1429907/workflow-for-statistical-analysis-and-report-writing) 
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I have been programming with R for not too long but am running into a project organization question that I was hoping somebody could give me some tips on. I am finding that a lot of the analysis I do is ad hoc: that is, I run something, think about the results, tweek it and run some more. This is conceptually different than in a language like C++ where you think about the entire thing you want to run before coding. It is a huge benefit of interpreted languages. However, the issue that comes up is I end up having a lot of .RData files that I save so I don't have to `source` my script every time. Does anyone have any good ideas about how to organize my project so I can return to it a month later and have a good idea of what each file is associated with?

This is sort of a documentation question I guess. Should I document my entire project at each leg and be vigorous about cleaning up files that will no longer be necessary but were a byproduct of the research? This is my current system but it is a bit cumbersome. Does anyone else have any other suggestions?

Per the comment below: One of the key things that I am trying to avoid is the proliferation of .R analysis files and .RData sets that go along with them.