And why would you need to google the function c? I don't think there's ever been anything more I've wanted to know about it than is written on ?c.
But your second paragraph makes a good point. For any given big csv of numbers it's a whole lot faster and fewer LOC to clean, organise and plot in R than in Python, even with Python's ever-growing list of imports.
Ultimately the ARMA calc didn't do what they wanted mostly because ARMA was the wrong thing to use on the dataset in the first place, IMNSHO. This could my general lack of experience with R but I've been programming for 15+ years and it was one of the rougher languages to work with.
Anyway I ported the code to python, numpy, scipy, scikits (and most significantly the time series stuff) and it was much easier to pull in the data an apply smoothing filters and do some general data clean up work but the ARMA was nowhere to be seen and I settled for simple linear and quadratic fits and think it did a better job of forecasting. I really liked some things that R did automatically like when trending data it added confidence intervals on the forecasts. I was actually tempted to port the ARMA libraries to python over this but didn't want to dedicate the time to debug and validate it. R was really good for interactive manipulation but python was better for actual deployment.
But other people always seem to have big problems with things that never even occurred to me.
In this case, I've been using R to pull data out of SQLite, SQL Server and Oracle db's every single day, for years. And I've never had any problems at all. It wouldn't even occur to me to think that R's ability to get data out of a db was anything other than "just fine".
I think part of the issue is that the typical use cases of Python and R are a bit different, so a lot of functionality that in case of Python comes in well-debugged and well-documented standard libraries, in case of R comes in relatively little-supported user packages.
Also, the standard package documentation system in R is absolutely atrocious; I am convinced that R would have been far better off without any package documentation standards at all.
I just skimmed the first few google results for "install python module windows" and none seemed particularly helpful. The page you point to says "The files are unofficial (meaning: informal, unrecognized, personal, unsupported) and made available for testing and evaluation purposes." Anaconda looks appealing, but wants my email address (and automatically checks the bother me box), ugh.
But R has some things going for it. There are some algorithms and tools which exist in R but nowhere in Python (this set seems to both shrink and grow over time as both languages add more stuff). R's overly-terse syntax for some things is annoying for maintainers of R code, but R hackers enjoy it because they tend to be all about banging out piles of stuff quickly.
R also comes with a lot of stuff included that in the Python world would fall under many different umbrellas (see the several names I mentioned at the beginning--those are just some of the basics). Whether it's true or not, R users perceive Python as being relatively balkanized, with that long list of packages just to get started, and with the Python 2 vs. 3 divide which has plagued it for years and will continue for a while still.