I think your point is very fair in that the author is exaggerating. But, "highly readable" Are we talking about the same language? The only reason R ever saw

I think your point is very fair in that the author is exaggerating. But, "highly readable" Are we talking about the same language? The only reason R ever saw use is it's adoption by the statistical community, but all other things considered, R is a shitty language. -Awfully difficult syntax -Extremely high learning curve -Nightmare to debug (error and warning messages are the most cryptic I've ever worked with) -Some of the worst documentation I've ever encountered -Many transformations are not visible to the user (how special characters are handled for example)... -The largest set of statistical libraries...but have you ever tried using any? Good luck! Next to nonexistent documentation for most, and even with a background in mathematical statistics, I still have a hard time following examples -More often than not, lack of backwards compatibility I'm a statistician by training and data scientists by day job, and have used R for years. I really admire what some have tried to do with the language (Hadley). But, it's a result of the statistical community being light years behind on computational training and is by no means a good language; it just happened to come along at the right time and had some really genius initial developers. Frankly, the "language of choice for data science" simply doesn't exist yet. You just use what works best for the problem at the time, and more often than not that involves switching between many languages.