The use of R (http://www.r-project.org/) as a statistical tool in healthcare analytics is quite wide-spread, and growing. As I’ve mentioned in previous blog posts, a few of the reasons that I like R include:
- there is a large R user community that is willing to help with almost every conceivable question
- there are several power graphical user interfaces (including R Studio and Revolution Analytics)
- the availability of numerous packages that add capabilities ranging from machine-learning to six sigma quality improvement; if you need it done, chances are that somebody’s built a package that does it.
Because it is open-source and has an active user base, the capabilities and features of R continue to expand. But because of the types of problems I’m continually working on at work, I sometimes find myself using the “same old” features I’ve been using for quite a while, and may not be up-to-speed with (or even aware of) the most current functionality. Fortunately, I’ve come across a tutorial by Karl Broman called “hipsteR” which is intended for “re-educating people who learned R before it was cool!” Like me, he states that “at times my knowledge of R seems stuck in 2001”; this tutorial helps bring long-time users (and even novices) up-to-speed with some of the most recent updates/features of R.
If you’re using R regularly as a tool in healthcare analytics, I’d recommend that you check out hipsteR and find out what you’ve been missing in R since 2001!
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