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Comment by wenc | Hacker News Reader

To be fair, R isn't really that accessible to most social scientists. Heck, R isn't really that accessible to most programmers either. The tidyverse and R notebooks have definitely improved things by leaps and bounds, but R at its core is an unusual language. (I say this as a somewhat proficient R user) Its programmatic nature does aid in coming up with reproducible data analyses though.

SPSS has, shall we say, a less than savory reputation.

All this to say that there is a market for something much friendlier than R. R is used by pure statisticians, data scientists and the like, but most social scientists prefer Stata, which has pretty legit statistical routines as well as a point-and-click UI.

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As a proficient R user, though, you probably agree that it makes a certain sense when you get into a rhythm.

I think it surprisingly makes a lot of sense to social scientists, in spite of seeming backwards to computer scientists. I remember being in school and using STATA, and just plugging in numbers to get through exercises too quickly to bother understanding what the labs were about.

R seems to make you stop to realize what you are doing at the right moments, then uses a lot of magic to abstract away almost everything else.

I'm fairly fond of JMP, which in my mind manages to thread the gap between "Easy UI" and "Push buttons until you get a p-value" decently.

Yeah I quite like JMP. JMP for the easy routine stuff. R for when shit gets real. I don't do much stats these days though, so not having a JMP license, I just reach straight for R on the rare occasions I need it.

Personally I found that Pandas was more easy to use than R, especially you can use the rest of python ecosystem for say interfacing with databases and data format conversations.

Absolutely. I'm sort of hoping to see Python/Pandas eclipse R at some point in my career. But for now, my field is only reluctantly in R, and advising colleagues and students into Python is... non-trivial.

> Heck, R isn't really that accessible to most programmers either.

R is objectively a bad programming language. However, it is by no means inaccessible. I have no statistics background whatsoever, and I managed to learn enough R to be dangerous in a mere week. Other than the 1-based indexing and the utterly disgusting dynamic dispatch mechanism (you could simply not to use the latter), R is surprisingly pleasant to use. What I enjoyed the most is that vectors and matrices are first-class values, not objects that are referred to through pointers. It's probably copy-on-write under the hood, but I don't need to care. Hallelujah!

It's not really an objectively bad language, it's just heavily optimised for one thing - the types of linear algebra calculations used for statistical analysis. For everything else (i.e. as a general purpose programming languate) it's awful. Which is why many languages have R bridges, so you can do the grunt work somewhere less insane.

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