Generalizing Support for Functional OOP in R

Post by fn-mote » Tue, May 28, 2024, 9:35 PM UTC

epgui wrote: post #21

PheonixPharts wrote: post #14

Earlier quoted context omitted.

Functional programming is not orthogonal to object oriented programming, they are paradigms that can be used together and the popular object system developed in Java is not close to the only way to do OOP. R, like Common Lisp, uses an OOP system based on generic functions (well, one of many OOP systems in R, but that's a different topic), where the function handles dispatching to match the object. Effectively instead…

> Effectively instead of object.method() you have method(object) This reflects a very deep misunderstanding of the distinctive characteristics of each paradigm. It's so far off that it's "not even wrong". Functional programming is much more about things like purity and referential transparency, about composing functions and/or combinators, about a particular way of managing or modelling effects, about a way of thinki…

Post by hadley » Tue, May 28, 2024, 10:51 PM UTC

kgwgk wrote: post #20

Nice. This is a bit disappointing though: “Multiple dispatch is heavily used in S4; we don’t expect it to be heavily used in S7, but it is occasionally useful.”

Post by mr_toad » Tue, May 28, 2024, 11:06 PM UTC

orhmeh09 wrote: post #4

rossdavidh wrote: post #2

To be honest, OOP never really seemed like a good fit for R. Functional programming is a much more natural fit, given that both it and R come from a mathematical point of view. R is a great language for the mathematical/statistical stuff it was invented to do, but I don't think it will ever be a general-purpose language, and it probably would become worse at its core purpose if it tried to. More work on being easily…

OOP has been a critical part of real-life R for a long time, especially in complex implementations of classes of kernels, algorithms, and so on with S4, and more general-purpose with R6. Without these frameworks it would be difficult to implement them. Personally I find it more expressive for general-purpose computation than Python. The "fs" library is much better at working with files and paths than Python "os" and…

Post by orhmeh09 » Tue, May 28, 2024, 11:38 PM UTC

mr_toad wrote: post #26

orhmeh09 wrote: post #4

Earlier quoted context omitted.

OOP has been a critical part of real-life R for a long time, especially in complex implementations of classes of kernels, algorithms, and so on with S4, and more general-purpose with R6. Without these frameworks it would be difficult to implement them. Personally I find it more expressive for general-purpose computation than Python. The "fs" library is much better at working with files and paths than Python "os" and…

Maybe too expressive and too flexible? Different R programs can have wildly different dialects, making it difficult for two R programmers to even understand each other. I’ve seen comics depicting the learning curve for R as having local minima beyond which there are further peaks and troughs of knowledge. A beginner might learn enough to get by, but find the code of someone on the other side of one of those peaks to…

Post by chuckadams » Tue, May 28, 2024, 11:54 PM UTC

th0ma5 wrote: post #7

andrewla wrote: post #6

Earlier quoted context omitted.

OOP as used in R is very much a function of API design and not a function of routine R usage for data analysis. To many users of R they are not even aware that they are using OOP at all, especially for the S3 style of objects. When you have an object, like `model summary()` ends up being very natural, and when you fit in the tidyverse operators many very complex workflows end up being very easy to digest. But when yo…

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