There are also a lot of niche libraries for various statistical tests and analyses that are used for scientific studies. Especially applied scientists who would

There are also a lot of niche libraries for various statistical tests and analyses that are used for scientific studies. Especially applied scientists who wouldn't necessarily be able to reimplement the math and are not very familiar with programming rely on it as a workhorse. Things like variants of anova, regressions etc. are often just a single line in R, specify the data, the interaction terms etc and you get a nicely formatted output with everything calculated for you, like goodness of fit, p-values etc. There's a long tail of functionality in there that's hard to match even with the scipy stack or other statistical packages in python. Plotting is also much less of a hassle than with matplotlib. R is more of a free and de-bloated SPSS than an alternative to scipy or excel.