We can now easily solve the problem of bioinformatics data integration. The R kernel for Jupyter is available here, and the installation instructions are on the same page. What follows are some notes I took during that process, in the hope that they are useful for anybody else trying to do the same thing.įirst you need to have R and Jupyter installed, but I’m assuming you already got that far. 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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