Do I really need to learn R?
Someone asked me this recently.
Many R advocates would absolutely say yes to everyone who asks.
(I actually gave her a pretty long answer, summarized here).
It depends on what kind of work you do and the context in which you’re working.
I can say that R is a very handy tool to have in your pocket.
It’s powerful. It’s flexible. It’s cost-effective.
But not everyone needs it.
If you have access to another software program (or two) that handles all the statistics you need; if you’re well versed in it and have secure legal access to a site license, you probably don’t need to learn R. This is particularly true if members of your field are not using R in any large part.
It’s always good to have options. A second software package in which you can check your work. (Though that may or may not be R).
Still, there are times when R is the best tool for the job. Or the only tool.
More often, I suspect, is this situation: there are other tools would work just as well, just not the one you have already. And how many packages can you justify purchasing?
This exact situation came up for me just last month.
I was working with a client who needed a pretty specific statistic calculated–a sample size estimate for a kappa statistic (for inter rater reliability). Naturally, they had a tight deadline.
I checked all my sample size software, and none had that statistic. (Of course).
I didn’t look, but it’s quite possible I could have purchased a third sample size software package that had this specific statistic. There are others out there that I don’t have.
A year ago, I would have been a bit stuck and may have had to do just that.
But I’ve been taking some of our own R workshops over the past 6 months, taught by David Lillis. And I’ve started to see the options opening up. So I thought I’d check to see if there’s an R package for sample size estimates on kappa.
Lo and behold, there is.
It wasn’t hard to download or use, and I got the answer I needed pretty quickly.
My day was saved.
So really it comes down to what I’ve been saying for years–it’s always good to have more statistical software options when you need them.
R is becoming an obvious choice, not just due to the $0 price tag, but the fact that so many people are creating packages that perform ridiculously specific tests–like a sample size calculation for a kappa statistic.
I realize that R looks extremely intimidating from the outside. I was required to use its parent SPlus in one of my grad programs, and I didn’t like it one bit.
But once you get started, you realize it’s pretty logical. I don’t know if R makes more sense than SPlus (I don’t think so) or if David is just a better teacher than my grad school TA (very likely).
The other thing I’ve been saying for years is that you don’t have to learn every difficult or ridiculously specific analysis up front. You don’t need to master every option on a tool to be able to use it.
Build for yourself a solid foundation, from basics up through linear models, and build it well. Once you have those skills, you’ll be able to add new ones from there as you need them.