R Tutorial using Jupyter Notebooks for Reproducible Research

• The story revolves around image processing in R

General outline

1. Introduction, history and perspective (basically general context)
2. Jupyter notebook inner workings in the context of the R kernel (IRkernel package)

• 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