Colleagues often ask me: â€œHow can I learn R?â€ Recently, I helped teach an â€œIntroduction to Râ€ class for the Advanced Research Techniques Forum (http://www.marketingpower.com/Calendar/Pages/2011ARTForum.aspx). So thatâ€™s one answer. Hereâ€™s another:

* Find an R-suitable project and force yourself to use it!* R is really a programming language, not a â€œstatistics packageâ€ â€¦ and like any programming language, you can only learn it by using it to accomplish something.

What makes a project R-suitable? I divide that into three groups:

1. Projects that need cutting-edge or custom statistical methods. R quite simply is the tool where new methods are developed first. If you need to try the latest in Bayesian, machine learning, classification, genomics, or similar areas: do it in R.

2. Processes that benefit from Râ€™s language and object structure. This is why I started with the S language back in 1997: I needed to run hundreds of models and extract key information from them. If you need to bootstrap a process, or compare or iterate models, R is the place.

3. Something that you know quite well. This is where R offers little attraction, but where you can leverage your knowledge. A frequency analysis you do every day; a regression model you run every month; a chart that you can make in 5 seconds in Excel â€“ those are great places to replicate the work in R just to force yourself up the learning curve.

Note that groups #1 and #2 are the easiest and luckiest places to be: if nothing else does what you want (except complete custom code), then R is an obvious answer. Group #3, choosing a problem you could solve elsewhere, is the most frustrating and requires enormous discipline. Youâ€™ll be questioning R every step of the way (â€œwhy canâ€™t I just point and click?!â€) â€¦ until something clicks and you discover the answer for yourself. OTOH, #3 is the easiest place to start from the perspective of finding specific help for your task; if it can be done easily somewhere else, then a recipe has likely been developed for R.

There are scores if not hundreds of R books that can help you. If you use R for long, eventually youâ€™ll own a shelf of them. Meanwhile, a great first book to learn and get stuff done is Paul Teetorâ€™s R Cookbook (http://www.amazon.com/Cookbook-OReilly-Cookbooks-Paul-Teetor/dp/0596809158).

But again, and most importantly: *Pick a problem, use R to solve it, and stay with it until youâ€™re done. *Then repeat. R undoubtedly will frustrate you. It may take hours or even days for something that seems like it should be simple. Remember that youâ€™re learning a new language, so progress should be slow. Yet every time you go through the process (choose, use, stick with it) youâ€™ll know more and will work faster and better. Good luck!