As one of the people interviewed in the article I feel somewhat compelled to explicate a bit further. I'd be the first to admit that R is good for some things and bad for others. It's full of quirky parts that make coming from any other more standard type of scripting language (e.g. python) make a user want to pull their hair out. However that said, in the world I come from (EEB, ecology and evolutionary biology), it's by far the most popular language. At rOpenSci, we develop tools in R because that is the language our audience works in. I think the mistaken assumption of many commenters is that R users are actual programmers. Most EEB scientists I know don't want to get bogged down in learning multiple languages. They want to learn something that will make doing their science easier. R provides that. For all the credit that SciPy and Numpy deseveredly get, they still are way behind when it comes to certain statistical tasks. For instance there are whole books written doing mixed effects models in R, but you can't get those in python yet (I know statsmodels is coming along but it's nowhere near where lme4 is). Yes, if you're a python programmer you could just call that one R routine from python and go back on your merry way, but that's you, not the average ecology graduate student. Also, MatPlotlib is just not on par with the capabilities of ggplot2 and other R graphing libraries (although there is a ggplot2 port to python that is being developed).
The other important component that I think is missing from the discussion about R's merits is that it's facilitating open science. We're talking about fields that are moving from SAS/ Matlab / JMP, etc...and the creation of totally reproducible documents and experiments with tools like Sweave. Is it going to provide the fastest environment for running regression trees on a dataset with 10 million rows, no. But is it a powerful scripting language with well developed tools for manipulating data (plyr), visualization (ggplot2, lattice), doing GIS (rgdal, sp), getting data from API's (httr, jsonlite, anything rOpenSci does :) ), writing reproducible documents (knitr) and doing complex statistics (lme4, nml4, gam), yes. It allows scientists to learn one language to be able to accomplish 99% of the analytical tasks they want to be able to. I think that's the point of the article. Yes FOSS has been part of science for a long time, yes R is not the best language for many things, but there's a culture at play where it's been adopted and extended by many scientists to accomplish a lot of valuable science, and brought FOSS, openness and reproducibility to a vast number of scientists that probably wouldn't otherwise have adopted those practices.
The other important component that I think is missing from the discussion about R's merits is that it's facilitating open science. We're talking about fields that are moving from SAS/ Matlab / JMP, etc...and the creation of totally reproducible documents and experiments with tools like Sweave. Is it going to provide the fastest environment for running regression trees on a dataset with 10 million rows, no. But is it a powerful scripting language with well developed tools for manipulating data (plyr), visualization (ggplot2, lattice), doing GIS (rgdal, sp), getting data from API's (httr, jsonlite, anything rOpenSci does :) ), writing reproducible documents (knitr) and doing complex statistics (lme4, nml4, gam), yes. It allows scientists to learn one language to be able to accomplish 99% of the analytical tasks they want to be able to. I think that's the point of the article. Yes FOSS has been part of science for a long time, yes R is not the best language for many things, but there's a culture at play where it's been adopted and extended by many scientists to accomplish a lot of valuable science, and brought FOSS, openness and reproducibility to a vast number of scientists that probably wouldn't otherwise have adopted those practices.