R is not for me, right?
I always thought R was for statistics nerds only, not to be used for analyses in “normal” UX research. But then I was forced to use it in a recent project and its power and elegance impressed me.
How did I end up using R? We created a dashboard for one of our clients with various UX KPI across a range of products. The dashboard was created in Microsoft PowerBI, which provides great data visualization tools and allows the user to create custom analyses with a few clicks. But it does not do statistics well, relying on R integration instead.
Why R? Some examples
A couple of examples that make R such a pleasure to use:
- Typical procedures are super simple: calculating a KPI from multiple columns per participant is a one-liner because R treats data as vectors new_value <- rowMeans(…)
- Running different statistical tests depending on the data’s properties (e.g. sample size, distribution) is very easy: write your own function if (data property = X) return (statistical test Y) and use it throughout your code.
- Working with larger data sets is very convenient, because you can use subsets of data on the fly with the “subset” function.
- Data transformation is quick and powerful: Convert data from long to wide, differentiating separate measurements, with a function called “dcast”.
All of this is also possible in SPSS, but it would require a lot of clicking through dialogues and assistants. For somebody like me who isn’t much of a SPSS syntax pro, I was surprised how easy it was in R.
The power of the community
But of course, learning R meant a lot of questions – it’s not something you learn in an afternoon.
However, R has such a large and helpful community that there’s almost always an answer out there. There are numerous pages that tell you about the basics of using R, and whenever I had a more specific problem, I would almost always find the solution in one of the many active online communities. Learning R might be a bit difficult, but you’re definitely not alone.
Packages for every occasion
The solutions you find online will often use so-called packages through which you can expand R’s features. And R being open source means there are packages for almost any occasion, created and verified by the community.
R in a nutshell
R is a programming language that is free to use under the GNU licence. It comes with a broad set of statistical functions and can be expanded almost infinitely with the help of packages provided by the community. Development environments such as RStudio make life a lot easier.
My suggestion: Get RStudio, get real data to work with (to prevent playing around aimlessly), check out the tutorial pages and: have some patience!
Here you can find RStudio and tutorials:
Is R for me?
- R code is quite similar to other programming languages, so if you have dabbled a bit in coding before it might be easier to understand than SPSS syntax (was for me).
- Once you get the hang of it, analyses are super quickly done and the code can be optimized and reused later.
- Graphical output is said to be more powerful and customizable than SPSS (not yet tested).
- There’s a certain joy in doing complex data operations and statistical analyses with a couple of lines of elegant code – at least for me.
- It’s free!
- No graphical user interface, harder to learn because you need to code
I probably wouldn’t have started using R had I not been “forced” to by PowerBI. But in retrospect, I’m glad I did!
If you own SPSS, you won’t be using R for a quick mean or statistical test. However, if you know you’re going to be doing more extensive data transpositions and analyses, and potentially do them repeatedly, R might be worth looking into. Especially if SPSS seems to make what you are trying to do unnecessarily complex. I will definitely use R for future projects.