user2016-tutorial-jupyter's Introduction

R Tutorial using Jupyter Notebooks for Reproducible Research

The story revolves around image processing in R

General outline

Introduction, history and perspective (basically general context)
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.