I’ve talked to many, many people who analyze data as part of their jobs who have expressed an interest in R. But people are sometimes scared to take the plunge. Here are four reasons you might want to consider it.
1. You’re tired of being locked into $$$ contracts with SPSS, SAS, or STATA
When I went out on my own, I went from having a free version of SPSS (well, free to me!) to having to pay for it. I shelled out a not significant amount of money (for one year), for a basic version of SPSS then got to work.
On my very first project, I realized I needed to do a logistic regression – a pretty basic procedure. I’d done it hundreds of times on my work computer. But the menu option was not available on my screen. What the heck?
Contacted support. My issue? Logistic regresssions were not supported in the basic version.
Seriously? The module I would have needed to download was another several hundred dollars, for ONE year of access, and for literally ONE type of analysis. Craziness. And not doable when you are on a shoestring budget.
In fact, as of press date, to get the professional version of SPSS 26, with (I assume), all packages installed, the cost is $5730 per year. Per person!
That’s insane. Guess how much R costs: NOTHING. It’s free!
2. You realize there’s so much that your stats program can’t do.
Propensity Score Matching has become a big area of interest in quantitative evaluation work today. Can you do it in SPSS?
Nope. That’s right, you’ve paid potentially thousands of dollars for an annual contract, but the program can’t do what you need it to do.
[OK, you can go to this page and download several python extensions that maybe will work. Check out this long list of instructions you need to make that happen. I’m sure you have to pay extra for the python integration!]
In R, getting the software you need on your computer is literally as easy as typing “install.packages (‘MatchIt’) .
And when a new trendy statistical procedure comes around? You can bet it will be available on R before any of the other packages.
What’s available now? Pretty much anything you can think of.
There’s a package that helps recode data easily. There’s a package that will help you automatically write your results to EXCEL. There’s a package that automatically scores survey data, even reverse coding items on the fly. There are tons of customizable graphics packages, if you want to go down that rabbit hole.
3. You are tired of always starting at square one with your data.
When I worked for a local school district, we had a climate survey that was given to every student, staff member, and parent. Tens of thousands of surveys needed to be processed, the results tabulated, and distributed online in the form of reports.
Each summer I dutifully spent one to two months preparing all of the data so it could be placed back into our survey report software, which cost over $20,000 up front and came with a $2000ish yearly maintenance fee. I spent a lot of time writing down procedures in a handbook so I would remember what to do the next time around.
One year, we had a small subset of parent survey data come in a month or so after the official close date. Now we had to make a choice – redo all of that work again to integrate the new data (and delay the reports by another few weeks), or ignore it.
It didn’t have to be that way!
With R, you write a script one time and then you’re done. You can reuse – and modify – that same script over and over, saving jillions of hours of time, and reducing the possibility that you’re going to make a mistake and not notice it.
So that late data that came in would have been loaded in with all of the other data, and the results would have been ready immediately. All in all, it might have added an hour or two to the process at most instead of WEEKS.
4. You know there are things you’re doing in EXCEL that you shouldn’t be doing.
Do you go through your data set and use “Find and Replace” to change “Strongly Agree” to 5, or “Yes” to “1” before importing into your statistics software? That is SO easy to do in R.
A list of EXCEL things you can and should be doing in R instead:
- Find and replace
- Merging data sets (VLOOKUP, and manual ways)
- Sorting data
- Filtering data
- Creating a clean ‘final data set’
Are you ready to give R a go? Go here to download RStudio Desktop (yes, there is a paid version, but the free version will do all you need and more).