I trust the authors immensely but i don't see what yet another class system in R solves. That's on me, but I'd like to understand more of what motivates this effort.
From the article: “S7 is a new OOP system being developed as a collaboration between representatives from R-Core, Bioconductor, tidyverse/Posit, ROpenSci, and the wider R community, with the goal of unifying S3 and S4 and promoting interoperability.”
It then goes on to describe what that means in depth.
It describes 3 new generics in base R that help their new S7 system.
It all seems motivated by better interop with python which is 'neat' but really doesn't seem like a critical necessity of the language. I guess it's more of a tactical thing where they're trying to make it easier for python users to eventually try R. Or for R users that work alongside python users to not abandon R.
The Python interop is in the blog post because it makes for convenient and compact examples, not because it motivated any of the features.
If you're interested in what motivated S7, you may enjoy this talk Hadley gave: https://www.youtube.com/watch?v=P3FxCvSueag (R7 was the working name for the package at the time)
In other words OOP can be great for tooling, but doesn't make much sense for what R is meant to be used for -interactive analysis- in every day work.
R's mess of OOP systems works great, S3 is "fine" for just dispatching 'methods' based on attributes, one doesn't even know it's happening in base R ALL the time.
R flexibility also makes it possible to build your own class system. i.e. modern ggplot2 has its own ggproto object system.
I'm an old R user forced to mostly use python because that's what the team uses.
R is so much better than python in many areas concerning data pipelines: connecting with external database systems through an unified API, superior data munging utilities, as well as plotting, a more comprehensive (obviously) statistical analysis toolset.
I even find rmarkdown vastly superior to jupyter.
But IMO the best reason to use R rather tha python is that its tools will make you approach the problem as a statistician rather than a programmer.
The sooner SQL is phased out in favor of something more akin to Hadley Wickham's dplyr the better. Don't particularly like the syntax of this but it's the right direction.
Everyone assumes things that aren't true, not just "junior" data scientists. There is not standard methodology for non gaussian non independent random variables.
