Turning R into SPSS?

I have written about several free alternatives to SPSS, including PSPP, Jamovi, and JASP. Bob Munchen has reviewed a few more options: Deducer, RKWard, Rattle, and the good old R Commander (in the screenshot on the left). We also find a review of Blue Sky Statistics. Blue Sky Statistics is another option for those seeking SPSS “simplicity” with R power underneath.

Blue Sky Statistics is available for Windows, and is open source. They make money from paid support. I note that it comes with a polished interface and this data editor that reminds us of Excel. I was very happy to see that Blue Sky Statistics offers many options for data handling, like recoding, merging, computing variables, or subsetting — that’s much better than what say jamovi offers at the moment.

The dialogs are quite intuitive if you are familiar with SPSS, and they can also produce R code. This is a feature we know from the R Commander, and ostensibly the aim is to allow users to wean from the graphical interface and move to the console. Nice as the idea is, it is defeated by custom commands like BSkyOpenNewDataset() that we don’t normally use.

The models offered by Blue Sky Statistics are fine for many uses — for those not living on the cutting edge. A nice touch are the interactive tables in the output, where you can customize to some degree.

Exciting as Blue Sky Statistics and other GUI are at first sight, I’m gradually becoming less excited about GUI for R. Probably the biggest challenge is the “hey, this is all text!” shock when you first open R (or typically Rstudio these days). Once you realize that the biggest challenge is to make the right choices and then interpret your results, you become less hung up about the “right” software. Once you realize that you’ll have to remember either way — where to click, or what to type — copying and pasting code fragments becomes less daunting. If you restrict yourself to a few basic commands like lm(), plot(), and summary(), R isn’t that difficult. Sure, when you come across idiosyncrasies because different developers use different naming conventions, R can be hard. But then, there are also the moments where you realize that there are so many ready-made solutions (i.e. packages) available and that with R you really are in control of your analysis. And the day you learn about replication and knitr, there’s hardly a way back.

One reason I kept looking for GUI was my MA students. I’m excited to see more and more of them choosing Rstudio over SPSS (they are given the choice, we’re currently use both in parallel)… so I there might be simply no need for turning R into SPSS.


Another one to watch: jamovi for stats

Here’s another open statistical program to watch: jamovi. Like JASP, jamovi is built on top of R. Unlike JAPS, jamovi is not focused on Bayesian analysis, but wants to be community driven. This means it has plugins (‘modules’) where others can contribute missing code. With its easy to use interface — as we know it from JASP –, jamovi is bound to appeal to many researchers and those familiar with SPSS will find their way around without problems. This is definitely one to watch.

Why We Should Watch JASP

jaspJASP — “a fresh way to do statistics — has been around for a while now, but this is really a project I am watching. Even though it explicitly does not refer to just another statistic programme, that’s certainly a useful mnemonic until Google ranks the page higher. JASP comes with a clean interface that will feel familiar to SPSS users, but actually improves on SPSS on many fronts to make it easier to use. That’s a nice touch.

JASP uses a journal system like we know it from IPyhton and Jupyter with live preview. The live preview is great, as users can immediately see what consequences their choices have. Unfortunately, this only really works for relatively small datasets, experiments or a simple population survey with a thousand respondents or so. Better than other similar solutions, the code is not visible to the user, which leads to nice outputs. At the same time, we can go back to the analysis and modify the output at any time. That’s slightly easier than finding the corresponding code in say Rmarkdown and recompiling.

As a nice touch, there is integration with OSF and SocArxiv. This means if I wrote a paper based on analysis carried out in JASP, I could upload this alongside the paper, and anyone can see the output file and modify it — online.

JASP uses R to do the calculations, which gives it a bright future in the kind of things it can offer. Unfortunately, at the moment, what’s on offer remains limited. This means for my purposes, JASP is not (yet) a replacement for SPSS, just like PSPP. The way JASP implements what it offers, however, is the reason why we should watch this project: it is easy to use, and it lets users choose Bayesian analysis for everything.

While JASP uses R underneath, there does not appear to be a plugin system (other than getting involved in developing for the project). I think that user-provided extensions might be just what is needed to make this project take off, because generally speaking, this is the kind of program I can see myself use in teaching (alongside Rstudio) and for simple analyses. For more advanced analyses, R remains the go-to application.