I also think it gets something right - it does feel different to use Python and R, and the reason may be rooted in how these languages arrived at data science. Python, as the article points out repeatedly, is a general programming language that scientists liked using for numerical computing, so slowly it acquired a billion libraries for data analysis, stats, machine learning, and so forth. R was created by scientists and statisticians specifically to do stats and analysis, but in order to be useful, it needed to acquire the full capabilities of a programming language.
What feels most natural to you often depends on what direction you come from. If you're a programmer taking on data science, you may gravitate toward python. If you're a statistician getting deeper into code R may feel more natural. It would behoove you to learn both.
What on earth is wrong with that? I suppose that for a few people on HN, this might be a bit repetitive. But otherwise, I'd recommend this article as a relatively non-combative bit on explaining the different languages, especially for someone getting started.