R Tutorial using Jupyter Notebooks for Reproducible Research
- The story revolves around image processing in R
- Introduction, history and perspective (basically general context)
- Jupyter notebook inner workings in the context of the R kernel (
- Tour of Notebooks and Short Image Analysis Lab
- Become familiar with the anatomy of notebooks
- Learn how to efficiently navigate and work with notebooks
- Gain some practice using shortcuts
- Perform a short image analysis lab
- Reproducible research - how Jupyter and R can address this
- Expanded image analysis lab
- Become comfortable building out a solution in notebooks with R and making it reproducible
- Gain/regain the experience of a "hackathon" style lab involving collaboration
- Become more comfortable and familiar with the notebook shortcuts and tools
- Get some experience doing image color quantization
- Microsoft Azure and Jupyter notebooks (they've got a few services available)
- Teaching, training and workshoping with Jupyter notebooks
- Wrap up
What's so special about Jupyter notebooks?
- Jupyter's power is in having code, data, and text all accessible from one file viewed in a browser, living locally or in the cloud.
- The Jupyter Project used to be part of the IPython Project and has spun off.
- It supports over 40 different languages (see this article) and that is actively growing.
Text bits (non-code cells)
The text in a notebook is written in the simple markdown language, making it a rich format capable of "rendering" latex, links, images and even html (since we're in a browser and the R kernel let's us).
The "new" wave on the block (at least R-flavored notebooks)
Amazing fact: in 2014, there were 80,000 jupyter notebooks on github. In 2015 the number almost tripled to 230,000. This shows how popular and fast-growing the usage is in the community.
- github numbers from article by Alex Perrier here