[canjobear]

My experience is that this weird evaluation order stuff is only confusing for students with a lot of programming experience who already expect nice lexical scope. For those coming in from Excel, the tidyverse conventions are no problem and are in fact easier than all the pedantic quoting you have to do in something like Pandas. It only gets confusing when you want to write new tidyverse functions, and even then, base R isn’t any simpler: the confusing evaluation order is built into R itself at the deepest level.

[uryga]

EDIT: i gotta admit, you sound like you've got more experience with teaching R than me. so perhaps my opinions here are a bit strong for what they're based on, i.e. tutoring a couple of non-programmer friends and my own learning process. still...

> My experience is that this weird evaluation order stuff is only confusing for students with a lot of programming experience who already expect nice lexical scope

fair point, but for the most part, R itself does use pretty standard lexical scoping unless you opt into "non-standard evaluation" by using `substitute`[1]. so building a mental model of lexical scoping and "standard evaluation" is a pretty important thing to learn. after that, the student can see how quoting can "break" it, or at least be able to understand a sentence like "you know how evaluation usually works? this is different! but don't worry about it too much for now". and i think dropping someone new straight into tidyverse stuff gets in the way of this process.

> and even then, base R isn’t any simpler: the confusing evaluation order is built into R itself at the deepest level.

i mean, quoting can't really work without being deeply integrated into the language, can it? besides:

- AFAICT base R data manipulation functions don't use it a lot. [2]

- for the most part, R's evaluation order can be ignored (at a certain learning stage) because it's not observable if you stick to pure stuff, which you probably should anyway.

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[1] http://adv-r.had.co.nz/Computing-on-the-language.html#captur...

[2] admittedly, stuff with `formula`s is similarly wacky, and if you're doing stats you're going to run into that sooner or later...

[canjobear]

It's true that if you write code that is pure and error-free then you will never bump up against R's strangeness.

But try out this bit of base R:

> hello = function(cats, dogs) { return(cats) }

> hello(100, honk) # where honk has never been defined

> hello(100, print("hello!"))

> hello(100)

[uryga]

yeah, the intersection of lazy evaluation and side-effects (incl. errors/exceptions) gets confusing, you definitely have to be there to help the student out of a jam. but i think it's useful to start out pretending R follows strict evaluation (because it's natural[1]) and then, once the student gets their bearings, you can introduce laziness.

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[1] well, not "natural", but aligned with how math stuff is usually taught/done. in most cases, when asked to evaluate `f(x+1)`, you first do `x+1` and then take `f(_)` of that.