Many data analysts that I respect use version control.
For example:
- http://github.com/hadley/
- See comments on http://permut.wordpress.com/2010/04/21/revision-control-statistics-bleg/
However, I’m evaluating whether adopting a version control system such as git would be worthwhile.
A brief overview:
I’m a social scientist who uses R to analyse data for research publications.
I don’t currently produce R packages.
My R code for a project typically includes a few thousand lines of code for data input, cleaning, manipulation, analyses, and output generation.
Publications are typically written using LaTeX.
With regards to version control there are many benefits which I have read about, yet they seem to be less relevant to the solo data analyst.
- Backup: I have a backup system already in place.
- Forking and rewinding: I’ve never felt the need to do this,
but I can see how it could be useful (e.g., you are preparing multiple
journal articles based on the same dataset; you are preparing a report
that is updated monthly, etc) - Collaboration: Most of the time I am
analysing data myself, thus, I
wouldn’t get the collaboration
benefits of version control.
There are also several potential costs involved with adopting version control:
- Time to evaluate and learn a version control system
- A possible increase in complexity over my current file management system
However, I still have the feeling that I’m missing something.
General guides on version control seem to be addressed more towards computer scientists than data analysts.
Thus, specifically in relation to data analysts in circumstances similar to those listed above:
- Is version control worth the effort?
- What are the main pros and cons of adopting version control?
- What is a good strategy for getting started with version control
for data analysis with R (e.g., examples, workflow ideas, software, links to guides)?
I feel the answer to your question is a resounding yes- the benefits of managing your files with a version control system far outweigh the costs of implementing such a system.
I will try to respond in detail to some of the points you raised:
Yes, and so do I. However, there are some questions to consider regarding the appropriateness of relying on a general purpose backup system to adequately track important and active files relating to your work. On the performance side:
And most importantly:
For example, have a Mac and use Time Machine to backup to another hard drive in my computer. Time Machine is great for recovering the odd file or restoring my system if things get messed up. However it simply doesn’t have what it takes to be trusted with my important work:
When backing up, Time Machine has to image the whole hard drive which takes a considerable amount of time. If I continue working, there is no guarantee that my file will be captured in the state that it was when I initiated the backup. I also may reach another point I would like to save before the first backup finishes.
The hard drive to which my Time Machine backups are saved is located in my machine- this makes my data vulnerable to theft, fire and other disasters.
With a version control system like Git, I can initiate a backup of specific files with no more effort that requesting a save in a text editor- and the file is imaged and stored instantaneously. Furthermore, Git is distributed so each computer that I work at has a full copy of the repository.
This amounts to having my work mirrored across four different computers- nothing short of an act of god could destroy my files and data, at which point I probably wouldn’t care too much anyway.
As a soloist, I don’t fork that much either. However, the time I have saved by having the option to rewind has single-handedly paid back my investment in learning a version control system many, many times. You say you have never felt the need to do this- but has rewinding any file under your current backup system really been a painless, feasible option?
Sometimes the report just looked better 45 minutes, an hour or two days ago.
Yes, but you would learn a tool that may prove to be indispensable if you do end up collaborating with others on a project.
Don’t worry too much about this. Version control systems are like programming languages- they have a few key concepts that need to be learned and the rest is just syntactic sugar. Basically, the first version control system you learn will require investing the most time- switching to another one just requires learning how the new system expresses key concepts.
Pick a popular system and go for it!
Do you have one folder, say
Projectsthat contains all the folders and files related to your data analysis activities? If so then slapping version control on it is going to increase the complexity of your file system by exactly0. If your projects are strewn about your computer- then you should centralize them before applying version control and this will end up decreasing the complexity of managing your files- that’s why we have aDocumentsfolder after all.Yes! It gives you a huge undo button and allows you to easily transfer work from machine to machine without worrying about things like losing your USB drive.
The only con I can think of is a slight increase in file size- but modern version control systems can do absolutely amazing things with compression and selective saving so this is pretty much a moot point.
Keep files that generate data or reports under version control, be selective. If you are using something like
Sweave, store your.Rnwfiles and not the.texfiles that get produced from them. Store raw data if it would be a pain to re-acquire. If possible, write and store a script that acquires your data and another that cleans or modifies it rather than storing changes to raw data.As for learning a version control system, I highly recommend Git and this guide to it.
These websites also have some nice tips and tricks related to performing specific actions with Git:
http://www.gitready.com/
http://progit.org/blog.html