A newcomer’s (angry) guide to R
HN is predisposed to hate R because everyone here is coming from a &quot;real&quot; programming context. Their concerns are generally valid, but they should keep in mind a lot of people using do not have a software development background and do not care that the language is not elegantly designed: they just want to get analytical work done. In that respect, R is far, far superior to Python. Even something as simple as installing a library is a conceptual leap for these people (why wouldn&#x27;t the software just come with everything needed to work?). Have you ever tried explaining the various python package and environment management options to someone with a background in Excel&#x2F;SQL? Just getting a basic environment set up can be days of frustrating effort (though Anaconda is getting better with this). Compared to R, where you install RStudio and are off to the races, with a helpful package installation GUI. Another great example: in R, data types are pretty fungible, everything is a vector, coercing things generally &quot;just works&quot;. In pandas, it can be very confusing that you need to explicitly turn a 1x1 dataframe into a scalar value. Same thing with Python vs R datetimes.<p>I understand some of this stuff is actually seen as a positive for Python in some contexts (production usage) and I agree. Just pointing out the woke take is the languages are both good, but good at different things. If I need to run a quick analysis on a dataset, I&#x27;m grabbing R 9&#x2F;10 times. If I&#x27;m building a production pipeline, I&#x27;m using Python 9&#x2F;10 times. This is perfectly fine.