Below you’ll find the presentation files for the course and the exercises we’ll do during the hands on sessions.
Note: You also have access to the source material at the github repo. Feel free to use these materials for any educational purpose!
We’ve also included links to materials to go further learning R at the bottom of the page!
It may help to have just the R code from the slides so you can easily run the code without typing it out. Below you’ll find .R scripts with just the R code from the notes (notice that there is some extra stuff as we often use R code to bring in images and things like that).
Justin teaches the following three courses for Data Matters, a week-long series of one and two-day courses aimed at students and professionals in business, research, and government. The material is open to anyone. Feel free to use these materials for any educational purpose!
This course covers the material in the first set of notes (R Basics, RMarkdown, & the tidyverse) in quite a bit more detail. All of the source materials are available on the corresponding github site as well.
This course introduces common programming techniques that can improve the efficiency of your R programs. These techniques include the use of loops and vectorized functions to avoid repeated sections of code. To really take R programs to the next level, we see how to write custom functions that will help to streamline code.
The course provides participants an introduction to utilizing R for writing reproducible reports and presentations that easily embed R output, using online repositories and version control software for collaboration, creation of basic websites using R, and the development of interactive dashboards and web applets.
I am in the process of updating this material. It will be ready by the first week of August (since that is when I have to teach it :).
This eBook (link)[https://dtkaplan.github.io/DataComputingEbook/] by
Danny Kaplan and Matt Beckman is a free resource for an introduction to
R course. The book has been used as a primary textbook for undergraduate
courses that teach an introduction to R & tidyverse with no assumed
prerequisites in statistics or programming. The book includes chapters
devoted to basic RStudio IDE, data wrangling, visualization, and other
topics useful for data processing & exploration.