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Comment by wanderfowl | Hacker News Reader

I'm a Post-Doc in a small social sciences department in a major university, and am probably the department's ranking R-geek. I did my dissertation, and much of my current work, doing modeling, analysis, and even machine learning in R.

In many ways, I owe much of my success to the power that R has allowed me to wield. Multicore lapplys and ggplot2 are my life these days. But even with this, R drives me absolutely batty, and the documentation, even battier.

I may be competent relative to most, but R feels so taped-together and idiosyncratic that even on my best days, I just feel like a newbie who's built up an army of ugly hacks.

Someday, I'll learn more about the python stats tools and do my stats there. But for now, R it is. Troll on, you crazy bastard.

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My data science group is currently transitioning to Python-based development from R. It's actually amazing how much faster Python development is, because of two things:

1. R libraries (and even rarely the R interpreter itself!) tend to have really weird corner case bugs that crop up every couple months, and

2. It's REALLY easy to write unmaintainable code in R, and so strange cruft creeps into the code over time.

The Python interpreter and Python statistical libs are rock solid in comparison, and with it we don't spend weeks debugging things caused by unnecessary idiosyncrasies. I just wish we'd started switching sooner and saving our time.

I just wonder if there's rationality behind not changing this (by either switching to a different language, fixing it or, hardcore mode, create a new one).

I know from a very, very different field that you often have to deal with decade(s) old technology because your employee/professor/etc is just used to it and, 15 years ago, it simply was the best option. I guess it's an equation that puts time spent learning its quirks vs time saved using a more sensible tool. While tiresome, "learning" might actually be the faster way to get things done. But it also carries so much ballast that, if there's a better alternative, must waste millions of frust-hours (and actual errors), especially for newcomers who could just as well learn a new tool - faster.

I started in SPSS. This is so much better. And although it drives me nuts, I still spend the other half of the time working in R feeling like a goddamned wizard. So even if it takes an hour and a half some days to figure out how to turn a list of unique factors into a list for lapplying (or something else stupid), it's not bad enough.

Also, remember that I'm the R geek around. Whether it's the best tool for the job or not, in my field, R is the lingua franca for stats. I could swear off R and move to Python Stats, but I'd still be supporting R among colleagues and friends. It's hard enough to convince folks who grew up in SAS to move to R, let alone to learn Python.

Finally, I'll have a hard time convincing editors that some weird-ass python implementation of GAMs or LMER is kosher when they're barely OK with the idea of GAMs. Reviewer two is, shall we say, technologically conservative.

I am in an almost identical situation, just still in the PhD stage.

I moved from Matlab (originally a mechanical engineer) and the biggest shock was documentation and just the internal help stuff in general. The help files on a regular basis require you to understand how something works to understand the thing explaining how it works.

I am often just shocked at little quirks I find trying to do things in R, not that it is worse than SATA of SAS but the goals was to be better. I am all for FOSS, and R provides many extensive capabilities not available in Matlab, but in terms of being user friendly Matlab is so superior it is honestly sad. Oh and '<-' just drives me nuts...I will never understand the choice of two characters where one is entirely sufficient.

> Oh and '<-' just drives me nuts...I will never understand the choice of two characters where one is entirely sufficient.

It's true that '<-' is a strange choice, but you can use '=' for variable assignments as well.

true

Iirc it comes from the old APL keyboard

That may be not a bad idea to get an APL keyboard actually, considering there are R interop interfaces for DyalogAPL, APLX and J.

well thank you, I always appreciate someone teaching me something :)

np. As was mentioned elsewhere in the thread, S is a pretty ancient language so R brings along some baggage with it :)

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