This is a crosspost from Jonathan Dursi, R&D computing at scale.
See the original post here.
I was asked to do a half-day tutorial at the Great Lakes Bioinformatics conference Workshop session.
The focus was mainly on R, with some python as well. We covered:
- The basics of Jupyter notebooks - what they are and how they work
- How to install and run Jupyter notebooks on their laptop, in R and Python
- How to perform interactive analyses in a web browser using Jupyter
- Using markdown and latex to
- How to “Port” an R bioinformatics workflow from some scripts into a Jupyter notebook
- How to share a Jupyter notebook online, using three different approaches
- GitHub and
I think it went prety well; the materials are available On GitHub.
It was largely hands-on, so apart from some introductory slides,
it was mainly about giving a tour of the notebook and how use Jupyter to share analyses; the “scripts” that I went through
in presenting the material were aimed at having the students produce the notebooks