R for Reproducible Research tutorial
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.DS_Store updates and images Jun 27, 2016
01_Introduction, History and Perspectives.ipynb
02_Jupyter notebooks - the inner workings.ipynb
03_Image Analysis with k-Means Clustering.ipynb
04_Reproducible research.ipynb
05_Expanded Image Analysis Lab.ipynb some renaming to make unix typing easier Jul 4, 2016
06_Jupyter notebooks on Azure.ipynb
07_Teaching with notebooks.ipynb


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