In the spirit of the Emacs-Vim Editor War and the tabs-versus-spaces debate, the summer of 2018 ended with another epic conflict, the First Notebook War. We'll discuss what all the fuss was about, the event that triggered the war, what we can learn from the opponents and proponents of Jupyter and R notebooks, and how best to benefit from the rapid technological advances of computational notebooks.
Jupyter notebooks are data science tools that combine text, code, and output to help data scientists communicate the goals, methods, and results of data analyses. Despite their popularity in academia and industry, including at companies like Amazon, Netflix, and PayPal, Jupyter notebooks have drawbacks that can confound novices and frustrate experts. The fervent discussion of the advantages and disadvantages of notebooks at the end of the summer of 2018 has been dubbed the First Great Notebook War and threatens to erupt into a conflict on the scale of the Emacs-Vim Editor War or the tabs-versus-spaces debate.
This talk will discuss the pros and cons of computational notebooks for data science, describe the differences between Jupyter notebooks and R notebooks, and suggest workflows and tools for getting the most out of computational notebooks, including how to 1) work with Jupyter notebooks, R markdown, and Julia, Python, and R scripts using JupyText 2) configure Jupyter Notebooks to run on markdown files with notedown 3) create and run Jupyter and R notebooks from scripts and markdown files with nbless, and 4) combine Python and R code in R notebooks and Radix articles using the rmarkdown, reticulate, and radix R packages in the RStudio IDE or at the command line.