Don't know about Jupyter, but been using Rmarkdown for as long as they are available. Recently, however, I am moving away from Rmarkdown and I don't miss it. I

Don't know about Jupyter, but been using Rmarkdown for as long as they are available. Recently, however, I am moving away from Rmarkdown and I don't miss it. IMO their ideal use case is in tasks like communicating homework exercises to a teacher. Or for tiny stand-alone projects. The problems they are designed to solve are, supposedly, numerous: reproducibility, literate programming, interactive exploratory analysis. But they end up getting in the way more than helping. 1) Reproducibility: nothing in the notebook is more reproducible compared with a simple source file. And you cannot really reuse anything within the notebook. Notebooks make re-running the written code more convenient for people not familiar with your stack. But, like mentioned above, it looses its appeal in any substantial project, where you will not be able to share the whole data that fuels the notebook. 2) Literate programming: it works only when you don't change anything, or make the notebook very abstract. If you do an analysis on a prepared dataset, add some comments, draw some pictures, then you have a "literate programming" document. But it lives only as long as nothing is changed. If you change the dataset it depended on you will likely have to change the comments in the notebook. You cannot just re-run it. And the comments added to the notebook - in my experience - nobody reads them, besides the original author. Almost every time somebody sent me a notebook I had additional questions about the results, which required me to interact and ask questions. It just adds additional burden on your part to describe what you do two times: once in the code, and another time in text. You can either guess what the recipients will be asking about and try to cover all corners, or simply answer those questions directly in a shorter amount of time. 3) Interactive exploratory analysis - notebooks slow it down. Writing simple functions in an editor and sending them to a REPL or simply exploring things in the REPL directly - both are faster alternatives. With notebooks you have to care about presenting the figures and pictures and comments from step one. In a typical exploratory analysis you are just trying things out without any idea of preserving them, before you stumble upon something useful. 4) On top of all that notebooks make things difficult to change. Say you have a notebook that loads 3 datasets, compares them with pictures and tables, transforms them, then compares them some more. And you decide to change the color of one picture. You either will have to re-run this notebook from the start just to change the picture. Or, alternatively, you start using cache, which, in my experience, is never a better option. Just adding an alternative opinion.