> For my use case, R is absolutely terrible compared to some for profit statistical package / language.

Which one? I've switched most of my work over to Julia, but I'd much rather use R or Python than Stata or SPSS.

What is your background? I'm assuming you either come from a programming background or at least enjoy programming and are pretty good at it. Most people I know who are either statisticians or scientists first and programmers only reluctantly love Stata and SPSS.

> What is your background?

Academic research, so more econometrics/data science work, but I have some experience with application programming that I've managed to leverage.

> Most people I know who are either statisticians or scientists first and programmers only reluctantly love Stata and SPSS

They are good to the extent that a lot of published - social science - research uses terms and methods that assume you're using one of the two. Of course, having an ok point and click interface also helps.

However, data access, aggregation, and cleaning are easily ninety percent of what's involved in even basic econometric(y) research. It is orders of magnitude easier to do all of this programmatically in R. Once you start working with larger datasets, or once performance becomes an issue, you pretty much have to transition to Python, Julia, or something similar by default.

I don't understand the use case for SPSS. My local university is training their neuroscience researchers on it, which seems so odd in 2022 with Julia or python sitting right there.

Have been using SPSS decades and I think it is a good statistical tool for those who do not want to do programming much. It gives me something Iu can easily explain to and teacher other statistical users (e.g. hand in many papers with some regression analysis in it and team members who have no programming experience and do NOT want to learn much other than absolutely minimum and necessary; they are social scientists and that is it). I knew R, python, SPSS, SIR/DBMS and most of SAS all can do this. Frankly only SPSS they can use. And I believe they can use it after I am not in the picture anymore still. That is the use case of SPSS. There are more people in that hole than your think.

It's 80% about having a point and click interface, 10% about path dependency effects, and 10% about whether or not they have a paid license.

Teaching someone who knows a bit of Excel and very little programming how to do statistical analysis in SPSS is easy and lets you focus on the statistics.

Teaching them to do statistical analysis in Julia will involve you spending 80% of your time teaching them Julia and maybe 20% of your time teaching them statistical analysis.

> Teaching them to do statistical analysis in Julia will involve you spending 80% of your time teaching them Julia and maybe 20% of your time teaching them statistical analysis.

This works until they run into a use case that doesn't involve running various forms of regression analysis on panel data.

In the parent comment's case, I could imagine that there's an expectation that someone doing neuroscience research will eventually have to expand beyond what's possible in SPSS. In this case, it may make sense to go through the effort of teaching them how to program in Python or Julia.

I agree with you, though I wonder what's included in your definition of "do statistical analysis" ? Is it just using the stats functions as blackboxes without understanding what is going on under the hood?

I find using Python and/or R to be very helpful for teaching, since you can implement the stats procedures using primitives (prob. calculations), so you get some experience with how things work.

Sure it requires some "coding" but nothing harder than using a calculator, so I think it's worth learning.

Julia is a bit more involved (need to learn something about data types), but still would be manageable.

SPSS was originally created for social scientists and psychologists. It allows people who usually don't have really a clue what they are doing, to create something that looks like science. Later on it was marketed as a predictive analytics suite for business minded people.

From time to time, I still have to use SPSS. Again and again I'm flabberghasted how bad this overpriced piece of software is.

Ease of use. I haven't used it personally, but I'm pretty sure you can do everything with a mouse - no need to learn actual code.

JMP and stata are really solid tools, but I feel like the advantage stems from the fact that they help me discover new statistical methods. In the GUI I can see an option for something I never seen before, read the documentation, and therefore expand my stats skillset. This type of discoverability is a bit harder with R/Python just due to the nature of it being purely script driven.

The language itself can be criticized, but when academic statisticians publish a new method, they often post an R package, too, so it has recent functionality unavailable elsewhere.

What makes R interesting is the amazing libraries for statistical ideas that are not completely run of the mill.

You almost literally can't come up empty on CRAN.

What for profit statistical package feels better? SAS literally has 8 character name limits in places. The data input command is literally called CARDS. It feels ancient. Minitab and SPSS aren't much better in syntax with regards to scripting which is important for reporoducability.

Maybe you can switch to Python? Its also free and has a lot of statistical packages.