R, the master troll of statistical languages (2012)

People like to fetishize data, and R sure lets you do that. The data science landscape however is growing such that R is really just a one-trick pony, however, that one trick is for better or worse being the gold standard of statistics and modeling, somehow.

But everything else wants to sugar coat the software surrounding the statistics, and leaves you no room to grow.

This is a very bad over-simplified example, but you sort of can't learn much about graphic design or good communication skills by using ggplot2 ... you can make something look very very nice, hopefully, in the general case, sure. And you can definitely do all kinds of hacks and crazy code to make it do whatever you want, but by doing that you produce ever more fragile and environment dependent code. You'd be better off learning just about anything else for graphics (Straight SVG, D3, Processing, Cairo directly, etc) because it is of course a bit more of a problem starting up, but a generalized skill set that could allow you to grow.

You also learn pretty much nothing about web development from Shiny. Shiny is a wonderful idea, but ultimately prevents a statistician from implementing what it promises, which is an analytic application. At some point, you have to ditch it and learn more traditional web stacks. It is also something of a sales funnel into a server solution that's a DDOS or security nightmare just waiting to happen.

So instead of just griping, I guess I have some ideas... it would be nice to have a Ruby/JS/Java/Python service generator. It would be nice to have a D3/React/whatver based generator. It would be nice for there to be a data munging solution (or even whole models, more like more PMML type stuff) that can be generalized into something that could be compiled or generates Python/Java/Bash/JS/Whatever code.

Ultimately you start thinking along those lines, and you realize that the promises R is making about empowering the analyst are just teasing them rather than helping.

R could do with less magic and more concentration on being simply a great statistics engine that integrates better. I guess it is that to some degree, but it sure fails the rest of the technology world that tries to live with it.

But if someone prefers iterative solutions, or that's all they know, why can't R make them just as fast as the vectorised versions?

1) ggplot does exactly what it is supposed to do: create data visualizations. It made no promises for interactivity or display, and in fact, it was originally designed for creating publication quality charts, which it continues to do well.

1.5) ggvis is a D3 API wrapper on ggplot and allows for interactive graphics. Do you want to pay your data scientists for creating production ready graphics or let them focus on what they're best at?

2) R has been growing - outside of neural networks (which R needs to catch up on), R gets almost every pre-processing and modeling algorithm first, and distributes it for free. Furthermore, it has better sampling options, metric options, augmentation options, and model ensabling tools (stack or meta-model) VS any other language or framework - it is the gold standard.

3) I don't think there's any "magic" in R. It's just a language with a learning curve and lack of opinions.

4) Last point: R is really not built for the web (it's older than Python!) - its built for data science. There's no reason you need to run your modeling stack in the same language as your application server. R is perfectly capable of writing to databases or sending API responces in JSON or PMML.

/endrant

Not trying to start a flame-war - but this type of difference in opinion is important to see when thinking about hiring data scientists or deploying models.