[stdbrouw]

R is in some ways more forgiving to newcomers. Sure, there's all sorts of weirdness around how vectors and matrices work, and don't get me started on the cryptic function naming, but (1) almost all batteries are included -- hardly ever a need to hunt around for packages, (2) RStudio is really nice, with graphics, a shell, a text editor, documentation etc. all in one place, (3) it's mature and well-tested.

I prefer Python myself, but after spending a couple of months with R I do understand why people like it.

(OTOH I'll be a happy person if I never ever have to work with SAS ever again.)

[srean]

> R is in some ways more forgiving to newcomers.

Oops! sorry sorry,... really sorry, apologies for snorting coffee over you, but given multiple years of experience TA'ing for machine learning / datmining courses I couldnt disagree more. R had them in absolute knots, and yeah they were asked to use RStudio if that helped. They struggled with simple things such as writing a naive Bayes classifier. Most of their mistakes were because of R's weird and silent inconsistencies: scalar or vector, copy or reference.

It is possible that all these 30 odd students every year were stupid but chances are fairly low.

EDIT:

The course has since switched to Java (Knime) and Python and that has gone a whole lot smoother.

Neither Java nor Python are my most favorite languages, but have to concede that Python is massively more consistent than R, so a student has to remember less of special cases, and the whipping boy of dearth of packages seemed less real at least in the context of the course. At least in the academic setting enthought / canopy / anaconda does a marvelous job of it.

[stdbrouw]

I said more forgiving. It's certainly not a forgiving language or ecosystem in absolute terms, you're right on the mark there. But ultimately you have to pick your poison. Do you want to struggle with all of the various quirks of R or do you want to struggle with all of the various quirks of (data analysis in) Python?