Also, it's unbelievably slow for basic operations like looping, function calls, and variable assignment. It's literally orders of magnitude slower than Python (I've tested it). Unlike its fellow C-flavored-Lisp Javascript, R retains an extreme level of homoiconicity, Which apparently makes the language very difficult to compile or optimize. I don't know how e.g. SBCL does it, but over the lifetime of the R language no one has managed to implement a non-trivial subset of R that performs better than GNU R (fastR was never finished to my knowledge). So you are basically relegated to writing code in C, Fortran, or C++ if you need even decent performance. Otherwise you are stuck using "vectorized" operations like lapply(), which are fine but make for a jarring experience if you're coming from other languages.

So of course it's a "real" language in the sense that Bash is a real language. But it's ultimately and fundamentally a domain-specific scripting language and I don't know if there is a way around that.

Then you can optimize and compile. This is particularly true of Scheme which foregoes much of the dynamic quality of Common Lisp in favor of a much more static view of the world. But its still basically true in CL.

R doesn't have macros of that kind. It has a sort of weird laziness based on arguments remaining unevaluated until needed. Metaprogramming in R is typically done by intercepting those "quoted" arguments and then evaluating them in modified contexts (this is exposed to the user more or less by letting them insert scopes into the "stack").

Thus, there is no distinction between macroexpansion and execution time. Hence, its tough to write a compiler, which is basically just a program which identifies invariants ahead of time and pre-computes them (eg, lexical scope is so good for compilers because you can pre-compute variable access). Because all sorts of shenanigans can be got up to by walking quoted expressions and evaluating them in modified contexts, R is hard to compile.

This is, by the way, why the `with` keyword was removed from Javascript. It provided exactly the ability to insert a scope into the stack used to look up variable bindings.

The idea of even the standard Common Lisp is that both is possible: a static Common Lisp and a dynamic Common Lisp, even within the same application in different sections of the program. Common Lisp allows hints to the compiler to remove various features (like fully generic code being reduced to type specific code), it allows a compiler to do various optimizations (inlining, getting rid of late-binding, ...) - while at the same time other parts of the code might be interpreted on the s-expression level.

The last thing I want to be thinking about is whether my compiler needs a "hint" that something can be stack allocated, for instance. That is their business, not mine.

It seems the Graal people managed to make R quite fast and is still being developed. Is there another faster? Any way, check out my blog post:

http://bootvis.nl/fastr-sudokus/

However, fastR is not a drop in replacement yet.

In any case, for me professionally, R slowness is never a problem. data.table does the heavy lifting.

What is a confusing mess, is the object system.

A domain-specific rather than general-purpose programming language? Most definitely. A scripting language? I would dispute that. Scripting languages are for automating the execution of sequences of tasks that you'd otherwise run independently.

Edit: looks like it's still a full major version behind? To be fair, there weren't many significant changes between R 2.x and 3.x.

I have not tried it in many years and I don't know how much faster or compatible it is. Nevertheless, I think it's a good thing that an "outsider" is looking at performance issues.

As far as I know it's essentially a one-man's effort. One could imagine that RStudio or Microsoft would be able to improve R performance substantially if they wanted to.

But for its domain, I think R is great.

I guess when all you have is a hammer...

For instance, why does R use <- instead of = for assignment? Because initial versions (of S) predate C -- developed down the hall -- the language that first introduced = for assignment.

Function arguments/environments are still pairlists: a type of cons cell.

You can manipulate the parameters to a function as symbols before you evaluate them. This is useful — many very good R libraries all make heavy use of it — and certainly very interesting. Lispers probably know it as quote/antiquote.

I suppose we could also point that, much like most Lisps, R gives you plenty of object systems to choose from...

And that's about it for R-as-a-Lisp.

It's also a really bad language for working with tree-structured data, something Lisps normally excel at.

I’m sure someone will chime in that there are different runtimes, like Microsoft’s (does this run on MacOS? If not, how can I use it to develop and test on my laptop) or some expensive RStudio solution.

The fact is you have none of those problems in e.g., Python. Although I would prefer to use Caret for many predictive modeling tasks, it is non-trivial to take the R runtime to production. That was what I walked away with.