Computer scientists had this idea that some things should be public and some things private. Java takes this to the nth degree with it's public and private typing keywords. R just forces you to know the lib:::priv_fun versus lib::pub_fun trick. At best it's a signal for package end users to tell which functions they can rely on to have stable interfaces and which they can't. Unfortunately, with R's heavy use of generics it gets confusing for unwary users how developers work with the feature as some methods (e.g. different ways to summarize various kinds of standard data sets as you get with the summary generic or even the print generic) get exported and some don't with seemingly no rhyme or reason.
Is there a way to trace an attribute to a function? I couldn't find one, but curious if it exists. I seemed blocked by the fact that trace seemed to expect a name as a character string. Some functions in base R have functions in their attributes which modify their behavior (e.g. selfStart). I ended up just copying the whole code locally and then naming it, but for a better interactive experience I really wish there was a way to pass a function object as I can with debug.
R works exactly as you describe. You can type `+`(1, 2) and get 3 because in R everything that happens is a function call even if a few binary functions get special sugar so you can type 1 + 2 for them as well. The user can of course make their own of these if they wrap them in precents. For example: `%plus%` = function(a, b) { `+`(a, b)}. A few computer algebra systems languages provide even more expressivity like yacas and fricas. The later even has a type system.
R is much crazier than that because even things like assignment, curly braces, and even function definitions themselves are all function calls. It's literally the only primitive in the language, to which everything else ultimately desugars.
You have a very different definition of free than I do. Free to me means that people enter into agreements voluntarily. It's hard to claim a market is free when it's participants have no other choice...
Nope. R's advantage is that the language is extremely expressive and makes many things about it's implementation extremely transparent to it's users. The huge package count for a niche language is a direct result of that.
200% agreed. I switched from python to R for data science machine learning and data visualization and found that python is far behind .
I am able to generate and test and make machine learning models easily and in standardised manner using its tidymodels libraries while I am unable to generate models in python scikit-learn etc libraries
Speak for yourself. Programming in R is amazingly expressive for prototyping. Its semantics are extremely lispy, yet it provides excellent support for fast numerics. It keeps me focusing on the problems I want to solve and provides me the tools to abstract them quickly to handle related problems. Meanwhile, python keeps forcing me to care about pesky implementation details which I don't care about on a first or even second or third pass. I really can't understand people who like python over R. Did you guys not read SICP?
This is so far from my experience. For me, R codes do tend to skimp on polish so it takes longer to get to the initial figure, but that is made up for by enabling me to see the data from a much richer perspective (to some extent because I had to think harder about what the output meant) such that I can find all the bugs in the data and in the underlying experimental plan: the stuff which makes it clear all the commercial reports are mostly useless anyway because Garbage in -> Garbage out
