| > There's a bunch of comments below which can be summed up with 'use R because <package name> doesn't have a direct python equivalent' but they're all missing the point that the Python data science ecosystem is evolving at a much faster pace than R and will completely supersede it in a few years. The point is R is a very good language for statistic because of the packages not data science. Data science can do their own thing it's okay. It's also okay for data science to use statistic models from statistic too. > R, like SAS, is a tool for non-programmers. I respect and love data science and machine learning but this behavior of generalization is terrible. There are many wondeful programmers contribute to R and uses R as I am sure there are many wonderful statisticians that use Python. They're just tools. > And there it shall remain. The only demographic where R makes sense long term are pure mathematicians/statisticians who are not proficient in programming. But that demographic is rapidly declining in size. What is up with these generalizations? R is not going anywhere in the statistic community. It's doing fine. Also from my experiences in academia most math people use matlab and if any R. It's okay to have both R and Python doing their thing. There is no need to conflate data science and statistic or have this weird tribalism. |
R has nothing going for it except a rapidly dwindling number of packages that don't yet have a direct python equivalent. It doesn't make sense to invest time into R if one already knows python unless one specifically focusing on academia pure stats type stuff.
Even then, the incoming generation of undergrads are increasingly proficient with programming and are shying away from R the same way that they shied away from Matlab after scipy matched it for 95% of their tasks.