Comment by thousandautumns | Hacker News Reader

Comment by thousandautumns | Hacker News Reader

thousandautumns

This is a stupendous example of someone going overboard on their criticisms in order to grandstand.

R may not be the most "beautiful" language in a general perspective, but it certainly is more beautiful than Python when it comes to actual data analysis. There is nothing in R that is as ugly as even the best implemented pandas, numpy, and matplotlib code. All of the options in Python, which is generally pointed to as the "superior" language to R, feel tacked on and hackish.

The real story behind most of the complaints is that they come from software developers who only rarely need to do data analysis that would require R, and therefore use it infrequently and mistake their unfamiliarity with the language with the language being bad.

I also groaned at the part where the author struggled to google questions about R because of its "stupid name". I have literally never, ever had issues Googling anything about R they same way I haven't ever had issues finding answers to my questions about "Python". The author is grasping at straws, and this is a programming blog's equivalent of clickbait.

R is a poor name, whether you can google it or not. The name can get lost in the minefield of text on the internet. Just because you never had any issues with google R does not make it any better. I have had many issues googling R and it always makes me second guess if this thread is about R language at all. On SO, I have to check if R is tagged.

R is a terrible name and it is not up for a debate. Whenever you name a product, company or in this case a language as a letter "R", you're literally asking for trouble.

Just to be fair, C is also a horrible name. On the other end of the spectrum - Julia and Rust are excellent names for a programming language because they're unique in the context of programming.

>Just to be fair, C is also a horrible name. On the other end of the spectrum - Julia and Rust are excellent names for a programming language because they're unique in the context of programming.

Funny enough I most often get wrong results when googling something Rust related, because there's a town called Rust (Germany), so Google pushes the location based results up, and rust is also, well, oxidized metal, so sometimes I get DIY pages as a result.

I love pretty much every other design decision about Rust, but it's the one "hard" to Google language in my experience.

Which is why we use "rustlang" and "golang" for keywords, tags, and search terms everywhere.

Maybe they should start using Rlang. Although it's going to be annoyingly close to erlang

Funny story, I once worked extensively with early Julia in a scientific setting where the hr directors name was Julia Lang, and we joked that if someone checked packets they would think I was stalking her.

To be fair, when both of these came out, SEO wasn't a thing.

Some of it is undoubtedly hyperbole, but:

> Index vectors like a[1] … a[4]. All indexing in R is base-one. Note that no error is thrown if you try to access a[0]; it always returns an atomic vector of the same type but of length zero, written like numeric(0)

That's serious WTF right there.

In general a lot of the complaints revolve around the language making error handling unnecessarily difficult which is something that will drive me up the wall with a language. I'm a fairly defensive programmer and if your language is fighting me when I'm trying to do error checking I'm not going to be happy. I can kind of understand the thinking of "just write a perl script to verify/reformat your data before passing it to R", but that doesn't help me find my own errors.

It's not WTF at all once you actually understand why it works that way and the benefits it provides. Subsetting in R allows for any index number to be retrieved, and if there is no value, it returns an empty value (that's what "numeric(0)" is: an empty numeric value). It's the same if you tried to access a[0] or a[90000] (in an array that doesn't have 90000 elements). This makes it easier to select multiple elements at the same time or select a range of elements; if you try to access one that doesn't exist, it just returns an empty value rather than erroring.

You can also access values by negative indexes, which have another special purpose in R that make it easier to quickly manipulate/analyze the data in the array, rather than causing an error.

It's just like how SQL won't throw an error if you try to do a SELECT...WHERE ID=non_existant_value, instead it will just not return any rows (or return NULL, depending on the exact query). But you wouldn't call SQL bad, you would just acknowledge that it serves different purposes and acts differently than something like Java or C.

Your comment is a great example of what the parent commenter was talking about: experienced "programmers" tend to dislike R simply because they are unfamiliar with it, and it therefor does things that they do not expect. But again, that doesn't mean the language is bad, it just means the users should probably become more familiar with their tools rather than trying to use a wrench to bang on a nail.

> It's the same if you tried to access a[0] or a[90000] (in an array that doesn't have 90000 elements).

It's not the same: `a[90000]` would be `NA` (which is a 1-element vector, not an empty vector).

It has a different behavior when you access past the end of the array. That makes error checking more difficult since you have to test for both error conditions. 1 based indexing is a mistake, but compounding that by changing the error conditions is the WTF.

Having 1 based indexing is a design flaw that R shares with SQL. SQL's error handling also leaves much to be desired.

One time I was struggling with some odd R behavior of the sort described by the author. I asked my local R expert. He told me how to fix my program, but I protested that none of it made any sense, even when explained. He didn't disagree, he just laughed and said "don't worry about it."

That works great for him, he can "not worry about it" and things work because he knows all the quirks.

If I just "don't worry about it" my programs don't work for mysterious reasons.

It seems likely that R could have been designed to have the same strengths without having so many weird and arbitrary quirks.

> It seems likely that R could have been designed to have the same strengths without having so many weird and arbitrary quirks.

Not really... at least not practically. R is a successor to S which was written in 1976 (fun talk on the history: http://blog.revolutionanalytics.com/2016/07/rick-becker-s-ta...).

Many of the arbitrary quirks started out for the sake of backward compatibility with S. All that being said, yeah, you could probably design a 'modern R' without the weird and arbitrary quirks.

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