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R Is (Not) the Next Big Thing

There's been some talk in the R community about a blog post by Dr. AnnMaria De Mars, entitled, "The Next Big Thing," in which the author writes:

Contrary to what  some people seem to think, R is definitely not the next big thing, either. [...]

I know that R is free and I am actually a Unix fan and think Open Source software is a great idea. However, for me personally and for most users, both individual and organizational, the much greater cost of software is the time it takes to install it, maintain it, learn it and document it. On that, R is an epic fail. It does NOT fit with the way the vast majority of people in the world use computers. The vast majority of people are NOT programmers. They are used to looking at things and clicking on things.

As I'm a newbie to R and its community myself, I wanted to succinctly touch on these points with a personal observation. I've been a Linux user for the past decade, and I see some correlation between Linux's history and what's happening with R. It seems to me that R is in the "Linux, circa 1998" stage. It's unpolished, often confusing, a huge initial learning curve for the uninitiated, and can help you do Really Neat Stuff™ if you stick with it. I will not be surprised if in ten years R is the standard for statistical data analysis, much as Linux has supplanted commercial UNIX and gone on to explore territory that its predecessor never touched (look at Ubuntu).

R may not be the next big thing, but R is certainly a big thing that is forthcoming.

Comments (2)

Apr 26, 2010
 said...
I think it is fairly easy to get up to analysis of variance in R, if you can hack. The problem is doing the correct statistic for the data and for the problem.
Basic stats can be done in any spreadsheet, and Sofastatistics is getting close to usable...
Here visualization (in my mind) is essential together withan understanding of the underlying assumptions and models in the analysis.
Otherwise you will use the wrong technique and get misleading results.
The learning curve for this, however, is quite hard. Particularly if you are not doing this every day (which is my case). And this is the learning curve that keeps consultant statisticians employed.
Apr 20, 2011
universitari liked this post.

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