It goes without saying that data science is an expansive subject area. A key tool supporting data science work is the statistical programming language R, which has grown signficantly over the past few years and has, in the words of Roger Peng, “become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world.”
This compendium is simply my attempt to gather together pieces of R methods, based on the problems I’ve confronted and the personal itch I’ve scratched, and what I’ve found at that particular time.
It is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Canada License. <a rel=“license” href=“https://creativecommons.org/licenses/by-nc-sa/2.5/ca/"target="_blank">