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Comment by carlmcqueen | Hacker News Reader

The updates here are massive.

I'd been waiting for the official release, instead of preview because of some issues, the official release seems to have ironed out the issues.

Excited to code directly into notebooks as reproducible code for fellow workers.

Sparklyr is really also changing the game for me in how I can integrate new users into R. It used to take a little work to get people off SAS EG or whatever statistics package they looked for. Not so much anymore.

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R Notebooks are such a killer feature!

Funny, many people say this, but iPython notebooks have been around for a while (now called Jupyter Notebooks since they are not Python specific anymore) and it didn't spur a massive migration to Python...at least as far as I can tell.

Not a bad thing, it's just R Notebooks kind of seem like old news to me.

Addendum: I looked it up and it appears that Jupyter Notebooks actually have an R kernel (https://irkernel.github.io/).

I think it's the fact that RStudio's notebooks are well-integrated into the IDE: so debugging, profiling, the panel listing the variables in the current workspace, and all the other niceties of the IDE still work with the notebook interface. I don't know of any equivalent to that in Jupyter.

Exactly right! The environment pane of RStudio and its related dev & debugging tools are available for use whether you're authoring a traditional script file or an R Notebook. It would be awesome if such facilities were available when working with Jupyter notebooks, but that is not presently the case.

Yeah but jupyter leaves bad taste in the mouth. Very frustrating to debug, binary file (no git/diff), much harder to layout than RMarkdown, no variable explorer. Additionally, the kernel idea is kind of lame/not implemented well. And you are getting nowhere in the data world without proper R support.

There wasn't migration to python because there's a total lack of anything to migrate to in python. It has only textbook examples as far as statistical models go. That's around 10% coverage and pretty much 0 coverage for all the abstractions and utility packages in R. Pretty much the same with machine learning (minus neural networks where it is strong). Python took some share from matlab and data processing tools but I don't see it retaining that. IMO it will lose it to Julia for the engineering & science people and to Scala for the data processing people.

The "second best language for everything" doesn't really work too well in the "data science" world.

What machine learning tools are missing from python? I think it's the best supported language but I would like to know what I'm missing

GAMs, BARTs, extensions to the Cox model, etc.

Jupyter (IPython) notebooks aren't binary, they are JSON. Clearing output before committing in git does show reasonable diffs (including output causes way too much churn).

Most of the nice stuff from RStudio is making its way into https://github.com/jupyterlab/jupyterlab which is starting to look nice.

I'm curious why you view the jupyter kernel idea to be not implemented well?

Because it breaks all the time for some esoteric (but always different than the previous) reason.

iPython notebooks don't have the functionality of R Notebooks.

R Notebooks allow you to prepare something for presentation, not just for running/testing. I.e.: You can run code and then choose to present the code and the output, just the code, just the output or none of them (for instance just putting a sentence in the presentation where you say you applied some mathematical function to the data, but show the mathematica function (latex) instead of the code.

This is really important to publish and to present data and it's not possible to do in Jupyter/iPhython.

Also, it allows you to export directly to HTML/PDF. Something Jupyter doesn't.

Jupyter notebooks are cool, but they are kind of mysterious in terms of how they work. For example, recently the R kernel started crashing whenever I opened a new R notebook. I finally found a note in a local document that said, "If your R kernel keeps dying, you may have to update pcre by executing 'conda update pcre'." I had never heard of pcre, but I updated it and the R kerel started working again. Go figure...

I am really excited about using R Notebooks.

Perl compatible regex is essential to R text and string parsing.

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