These lecture notes were written by me to accompany John Verzani’s Using R for Introductory Statistics (2nd ed.), to be delivered in lectures teaching students how to program with R in the programming lab accompanying a lecture section focusing on the statistical methods themselves. No knowledge of programming is assumed; my objective was to teach basic R programming well enough to use R for statistical analyses.

These notes are not intended to stand alone; I like Verzani’s book and I believe that these notes should supplement it, not replace it. For those taking the programming lab for the University of Utah’s Mathematics Department statistics courses, I would insist on reading Verzani’s book in addition to these lecture notes. However, these notes could serve as a light weight introduction to R and statistical programming.

These lecture notes were adapted from lecture notes written for an eight-week intensive course covering the same topics that I also wrote. Most of the notes are an exact duplication, but in order to accomodate the instructors of the lecture sections (none of which I was teaching at the time) I added and rearranged lectures to slow down the lab’s pace.

Lectures 1 through 4 cover R basics. Lectures 5 and 6 cover plotting. Lectures 7 and 8 cover multivariate analysis (lightly; this is a topic covered in greater depth in another course). Lecture 9 discusses probability models in R. Lecture 10 dives into the “tidyverse”, discussing dplyr, magrittr, and reshape2 for data manipulation (this does not correspond to material in Verzani’s book). Lecture 11 discusses computer-intensive methods for hypothesis testing (based on resampling methods). Lecture 12 discusses bootstrapping and Bayesian statistics (very light Bayesian statistics). Chapter 13 discusses confidence intervals, and Chapter 14 covers hypothesis testing.

I hope that you find these notes useful, and wish you the best of luck.

Curtis Miller