If I ever strike it rich, I swear to god I'm donating $5,000,000 to the cause of reaching total feature parity between the best of R's packages and NumPy/SciPy.
you are underestimating the price of that by at least an order of magnitude. That is one of the biggest reasons people like R. R also has the notion of NA, which is different than NaN, built into the language.
The other huge reason for R adoption is it makes running stat analyses very simple, so for all the people who aren't programmers, and don't wish to be programmers, R is an awesome choice. The ability to, in 3 simple lines of R, load data from a csv, run a glm, and get a sophisticated report on the model is awesome.
The battle between R and NumPy reminds me of the competition between American and Japanese auto manufacturers. Did American car companies successfully play QA catchup in the past 10 years? Probably, and God knows they needed to. Meanwhile, Japanese car manufacturers were building on 35 years of QA excellence the whole time.
I would be far more excited about the possibilities created by a cornucopia of new stats/dataviz functionality built into Python than I would be about some packages that make R a bit less terrible to write.
The whole R sucks as a language thing is getting pretty old. There is some things that R is really good at, there are some things that Python is really good at. Just because R comes from a somewhat unusual ancestry (firmly rooted in functional programming with immutable data structures and generic function style OO) doesn't make it bad.
As recently as a few months ago, R used a form of "reference counting" which only allowed three values in the counter: 0, 1, and 2 [1]. This might be caused by unusual ancestry or it might not, but it's hard to stop saying R sucks when it does that in 2014.
