It took me a while to get that working for the R scripting language. Jupyter consists of multiple ‘kernels’, to get support for a different language you have to install that language, and then install the Jupyter kernel for it. In principle you can install support for a wide range of scripting languages, but in practice it may be a little difficult to set up. But since they added support for other languages besides python, they had to rename. Jupyter started its life as IPython, or “interactive python”. A Jupyter notebook can contain the analysis, the results, and the documentation that explains the results together in a single file, making it at once understandable and reproducible. Jupyter notebooks are a great way to do an analysis, and report the results at the same time. Scripts are a great way to make reproducible workflows, but they are too technical for many situations where you have to report to scientists. Teach a scientist to script and they won’t have any more time to do analysis for the rest of their lifetime. Give a scientist a script and they will analyse data for a day.
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