I don’t tend to get too sniffy about the quality of discourse on the Internet. I have some appreciation for even the most pointless, uninformed flamewars. (And maybe my take on Web site comments is for another post.) But there’s an increasingly popular topic of articles and blog posts which is starting to annoy me a little. You’ve likely read them—they have titles like: “Python is Eating R’s Lunch,” “Why Python is Going to Take Over Data Science,” “Why Python is a Pain in the Ass and Will Never Beat R,” “Why Everyone Will Live on the Moon and Code in Julia in 5 Years,” etc.

This style of article obviously isn’t unique to data analysis languages. It’s a classic nerd flamewar, in the proud tradition of text editor wars and browser wars. Perhaps an added inflammatory agent here is the Data Science hype machine.

And that’s all okay. Go on the Internet and bitch about languages you don’t like, or tell everyone why your preferred one is awesome. That’s what the Internet’s here for. And Lord knows I’ve done it myself.

My only problem is that it distracts from more important, more interesting conversations about what’s happening with data analysis languages. Instead of pissing matches and popularity contests, the real interesting phenomena is how developers are comparing notes, sharing cool innovations, and increasing interoperability. A great example is the IPython notebook. The notebook doesn’t make Python better than other languages—it makes all languages better.

I think it’s a really fascinating time for folks who use and think about computer languages. The last 5 years or so has seen not only the introduction of really cool new languages, but also extraordinary developments in existing ones. I’m psyched about all these languages and I want them all to succeed and get better. Some days I want to code in R, some days in Python. Others in Julia, or Clojure, or F#, or even C++. I don’t want any of them to stagnate or disappear, or be “beaten” by any of the others. And I don’t think that’s happening anyway.

So what’s below is a somewhat tongue-in-cheek list of suggestions for facilitating productive and interesting discussions comparing languages. Many of them are not specific to our little R/Python/Matlab/Julia skirmishes, but apply to lots of different language wars (C++ vs. Java, Python vs. Perl, Ruby vs. Python, Clojure vs. Scala, Haskell vs., I dunno, everybody?) The last section is comprised of a couple of very general notes about civility. I’m strongly in favor everyone’s right to be a smug prick on the Internet. But, you know, you should probably try not to be a smug prick on the Internet.

And, please, feel free to add additions or suggestions in the comments, or in this Gist

Section 1: Being Aware of Context

§ 1, Article 1

Recognize that languages have goals and communities. It helps to evaluate them in that context. Features that are high priority to you may not be high priority to the majority of users in that language, and vice-versa.

§ 1, Article 2

Recognize that many smart, capable people are very productive in the language you’re slagging. The cool things science and industry are making in the language speaks far louder than your casual dismissals of it on a message board.

The same logic goes for language developers. For example, Hadley Wickham is a smart guy and a great programmer; he’s probably not one to waste his time improving a language that’s some irreparably broken dead-end. Same with these guys.

§ 1, Article 3

Recognize that language design is the art of the tradeoff. Don’t complain about a design choice until you understand the logic behind it. In many cases, your preferred design or feature was already considered, and would have led to undesirable outcomes elsewhere. It is helpful and interesting to disagree about how a tradeoff was managed, but do recognize that there was one.

§ 1, Article 4

Distinguish between a feature request and a language critique. If you come to a new language and miss some features of your old language, that’s fine. But that’s not necessarily a failing of the new language.

A living, breathing language is a combination of both its features and its idioms. A feature may not exist because its programmers tend to write code in a way that obviates its need. Sometimes such idioms are crutches to compensate for truly useful features that are missing; other times they are interesting and elegant expressions of a problem that you’re just not accustomed to. Try to spot the difference.

§ 1, Article 5

Pay your dues before dismissing a language. If you gave up on something in a language after finding it too difficult, consider that the problem may be yours. It may not be, but at least consider it.

§ 1, Article 6

Don’t over-sell immature, alpha-version features, no matter how promising they are. Promises don’t cook rice. Sending unsuspecting users to buggy, incomplete libraries just harms your cause in the long run.

Examples:

“ Julia has a fast-growing library of packages!” Sure, but less than a handful are close to production quality.

Julia has a fast-growing library of packages!” Sure, but less than a handful are close to production quality. “ And now ggplot has been ported to Python!” Not quite yet.

Honest advertising of works-in-progress is encouraged, though. There’s nothing inherently wrong with immature libraries, many of which are fantastic.

§ 1, Article 7

Microbenchmarks are useful for understanding differences between languages and their execution, but are of limited use in pissing contests. No one knows exactly what percentage of the world’s working software is comprised of Fibonacci number calculations, but our best guess is not much.

Section 2: Being Interesting

§ 2, Article 1

Whether one language is going to take over another is not that interesting, nor that meaningful. (When does a language get “taken over?” For Christ’s sakes, there’s still a non-trivial amount of COBOL running out there in the wild.)

Competition is pointless, but comparison is not. Languages are increasingly adopting ideas from each other, building interops with each other, and sharing tooling. Having conversations about this process is far more interesting than running popularity contests.

§ 2, Article 2

Avoid clichéd arguments. They are not necessarily incaccurate, but they are boring.

Examples:

R is a “ DSL ” or “not a real language” (see Article 2 below); R is “designed by statisticians, not computer scientists.”

” or “not a real language” (see Article 2 below); R is “designed by statisticians, not computer scientists.” “ Semantic whitespace in Python sucks.” (Generally, arguments over syntax are boring.)

Semantic whitespace in Python sucks.” (Generally, arguments over syntax are boring.) “ Julia doesn’t have as many libraries as ${pretty much anything}.”

In addition to arguments, also avoid clichéd phrases. (See, e.g., “not ready for prime-time.”)

§ 2, Article 3

Supplement abstract terms or subjective impressions with concrete definitions and examples.

Examples of statements that could use concrete support:

“ Code in language X is more expressive than language Y.” “ R is a DSL , while Python is a general purpose language.”

Section 3: Being Civil

§ 4, Article 1

Be sure that you can accurately summarize someone’s argument before you start composing your rebuttal.

§ 4, Article 2

You are not so smart that you are entitled to be smug. Some tips: