S3, S4, R6, and reference classes. To be fair they are situational and not one size fits all. The stricter ones are mainly used in biostats where significant metadata makes more sense in OO. S3 is nice and easy, primarily just a list with dispatches. Everything else is less so.
The coercion always gets on my nerves, JavaScript gets a bad rep but R is pretty damn warty too; weird ass data types ('ordered factor', anyone) that just seem so very far away from design choices in other languages without being particularly ergonomic or aesthetically appealing
I remember when we first used R in a stochastic class. The professor (a mathematician) was in love with the language and the students (computer science) considered the language to be the PHP of science.
as a computer scientist and programming languages nerd, I think R is a much better language than Python (comparing the two only because Python is leading in the data science field)
I also believe that the tools available are superior, RStudio is very good IMO.
Because it’s built around a very specialized set of needs (data manipulation, visualization, and statistical modeling), and it is essentially best in class at it, but it has quirks as a result. Anyone coming to R from a background in another language will feel those quirks intensely and assume it’s bad.
Javascript and C also have weak typing. And Python isn't statically typed, it's just that like Ruby, it doesn't allow implicit conversion, except with numbers.
If you learn tidyverse, then you're going to cringe whenever you use Pandas or most things in the Python data science ecosystem
https://www.rstudio.com/wp-content/uploads/2015/02/data-wran...