Learning R and RStudio


These notes have evolved from a presentation at Li & Fung in the Fall of 2016. Revision inspired by Allan Miller & I leading a discussion on “How to Learn R” at the Berkeley R Beginners meetup.

A quick start to R and data science.

In the olden days, 10 years ago, I spent a lot of time build links and making tricks & tips list. Now I just point to RStudio resources and a couple of my favorite books.

The classical way to learn R is to work through the all basic ideas in “base R” and then learn the modern enhancements and packages. If you like this approach see the just released 832 page tome The Book of R: A First Course in Programming and Statistics will appeal to you. It is actually a pretty good book. But…

My prefered way to teach R today is to leverage the new way of working in R – much of which has been developed by folks now on the RStudio team. In particular Hadley Wickham and his students who have created the “tidyverse” (formerly known as the “Hadleyverse”, but Hadley is getting modest in his old age).

Jim’s Principles for Getting Started in R

  1. Use RStudio!!!
  2. Live in the tidyverse
  3. Use RStudio’s project framework
  4. Invest in learning ggplot2 concepts (don’t start with qplot()!)
  5. Deliver your results via RMarkdown
    1. And use R Notebooks for development and EDA.
  6. Use git & GitHub.  See:
    1. Josh’s Version Control with Git (& SVN)
    2. Git/GitHub chapter in Hadley’s R Packages
    3. Jenny Bryan’s complete notes http://happygitwithr.com/ from here useR! 2016 tutorial
  7. Package up your tools

Step-by-step guide

  1. Load R
  2. Load RStudio Desktop (IDE)
  3. See RStudio’s guide to on-line learning, in particular
    1. Their archive of excellent webinars, and
    2. Garrett & friends have great cheat sheets
  4. Read Garrett & Hadley’s R for Data Sciencea work in progress now complete & in print (AKA The Tidyverse Guide)
    1. Hadley just finished the Graphics for Communication chapter in which he mentions the ggplot2 extensions site.
  5. Practice on real data! Perhaps while finishing R for Data Science. You now know enough to do real work.
  6. Catch up with ggplot2 developments
    1. Winston’s Cookbook for R (with link to his R Graphics Cookbook)
  7. Read & follow advice in Hadley’s R Packages book
  8. For the brave programmers: Hadley’s Advanced R 

Other Resources

Favorite Books

(In addition to above, these predate Tideverse)

Tricks & Tips

dplyr & tidyr Quick Start

I like Brad Boehmke’s Data Processing with dplyr & tidyr

Google Drive Access

Google Sheets in R

Use the package googlesheets (duh!) see Jenny’s GitHub https://github.com/jennybc/googlesheets

Interesting Projects

Posted in R, Tricks & Tips

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