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Comment by thomasfedb | Hacker News Reader

Jupyter is lovely (and JupyterLab looks delicious), but the set up required to achieve a reproducible local server with the R kernel and versioned R packages is 100% not.

Installing R packages through anaconda is like pulling teeth and the docker images for my Jupyter notebooks push past 6GB and take multiple cups of tea to build.

Is there a good solution I'm missing? A good hosted solution perhaps?

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I run https://nextjournal.com

We archive full-stack reproducibility by allowing you to install arbitrary software and version these environments using docker. You can reuse these in other articles or pull and use them locally. `xoxo` is a signup code you can you if you want to give it a try.

Looks cool, do you have an article that explains the stack from a technical perspective? Is this based on Jupyter?

Not yet. It isn't based on Jupyter but it's all written in Clojure. Been meaning to do a writeup on our stack for some time…

In addition to our own runtime protocol, we support Jupyter kernels and you can import Jupyter and (R)markdown documents.

I work for a company, Code Ocean, that aims to solve this issue. We have a custom-UI for installing packages through a variety of package managers, including Conda, CRAN, etc. Here's an example of some Julia notebooks being rendered to HTML with the environment fully configured and accessible https://codeocean.com/2018/08/16/counterexamples-on-the-mono...

you have google colab notebooks: https://colab.research.google.com

https://notebooks.azure.com (python, r, f#)

I run https://rnotebook.io -- it's free for the foreseeable future.

The Docker image is 6GB.

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