I'm not sure what about Jupyter makes Python more compelling in comparison to R. R is entirely usable in Jupyter Notebooks, and R Notebooks are, in my opinion, possibly superior to Jupyter notebooks in many ways.

> and as more people realize that there's a benefit to simultaneously training researchers to run code as well as stats, I suspect we'll start to see an exodus from pure R solutions

I'm not sure what you are saying here.

I would actually argue that most of the Python data science toolchain is years behind what is available in R.

> I would actually argue that most of the Python data science toolchain is years behind what is available in R.

I do not want to litigate this on HN, but the problem with R is the toolchain around your data science work.

You've fit a model in R, and that's great! Now how do you get it into a real-time system? Or how do you test the software you wrote to train the model?

> R is entirely usable in Jupyter Notebooks

Not for everybody, e.g., the Swirl R package (https://swirlstats.com/) doesn't work in Jupyter, since Jupyter has limited support for R's many ways of getting interactive input from users.

s/possibly/definitely/

It seems to depend on what you’re doing.

Python definitely has more mindshare for machine learning, and particularly deep learning. However, that’s not all of statistics. For things like mixed-effects modeling, I think R still has a clear lead. There are some python packages (e.g., statsmodels) but R’s lme4 has more features, like custom covariance structures, and virtually every textbook and tutorial currently uses R. I’m actually not sure if I’ve ever actually encountered statsmodels in the wild. PyMCMC is relatively popular, but I think bugs/jags are also more common.

And then there is the Zelig modeling framework for R that I can’t imagine not using after having used it.

Don’t get me wrong, I like Python well enough, and knew it before I coded R. But Python is really behind R in stats support. I’d also add the tidyverse in there for general data munging.

If I want libraries I’ll use R; if I want a programming language I love I'll use Racket or maybe Clojure; if I want some libraries and an okay programming language I’ll use Python, I guess.

Woa, thanks for pointing out Zelig, I needed that relogit and I didn't even know it :)

The counter factual simulation features are amazing and easy.

Wondering why it hasn't got more publicity?

The migration between languages is also industry specific. They are still teaching SAS to finance and healthcare analysts, for instance, and R and Python are still rising in healthcare specifically. Keep in mind all the legacy code and all the coders who just know SAS and don't need to change. It'll take longer for the transition than you think.

I have tried to move from SAS to R a few times at this point it's largely inertia there is so much in my org already written in SAS makes it very difficult to convince people to change.

I do like the SAS dev tools (especially Enterprise Guide). I'd really love if R had some sort of GUI front end for non technical people. Eg. I know the finance analysts in our org wouldn't have a clue how to configure their own ODBC sources -which you need to do with R Studio until it's as easy for them as SAS convincing them to switch won't get any traction.

You may want to try Julia

I'm definitely well in the R camp but keep feeling this nagging pull from Python. Especially for trading...it would be so nice to have a language for both research and production, as right now I translate all my research into scala for production.

I hate to be the stereotypical Julia recommender, but it is made for this use case, more so than Python, which isn’t all that much faster than R if speed matters. (Unless you want to try Cython but that’s a whole bag of worms.)

I'd second that. R and Python both have the same pre-LLVM performance issues.

I don't expect either R or Python to go away either time soon, nor would I want them to, but I would like to see people moving to things like Julia and Nim, which have the same level of expressivity, but are much more performant. I have difficulty imagining many people saying "I love programming in R and Python, but don't like Julia or Nim."

I like Python but at least with stats/numerics there isn't a big reason to move away from R except for specific libraries (especially DL stuff) or front-end integration with web-land (and even then things like Jupyter mitigate against that).

I would also add two good reasons to stick with R: RStudio and Hadley Wickham.

In theory, there are Python and Julia equivalents to RStudio (JupyterLab, Spyder, PyCharm, Juno, whatever) but RStudio is just so, so, so good. A truly great piece of software.

And of course if you have a data pipeline type workflow, and it fits into the Hadleyverse paradigm and isn't too performance intensive, there's nothing better.

Julia has it's own "*verse" type data pipeline framework, with an even greater variety of backends and plotting solutions than R.

It's still in development (mutate and select have PRs) but it's almost there.

https://github.com/queryverse/Query.jl

I've tried it while it was unstable and it was excellent (I remember some random forest training that went from a few days down to half an hour). Since everything was unstable at the time, one update was all it took to break everything. Now that 1.0 is out, I'd definitely like to pick it up again. Unfortunately for my current use cases, the ecosystem just doesn't exist like it does with R or Python.