STATS 32: Introduction to R for Undergraduates
Spring 2020-2021
Announcements
No lectures week 1 (March 30, April 1) – apologies, the dates of week 1 were previously wrong.
Make sure to install R before coming to class.
I will be trying to make this course more interactive (as much as is feasible be distance). My approach is outlined in course changes, so please read it and come to the first class with an opinion about this!
Course description
This short course runs for weeks two through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R.
Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Instructor
Damian Pavlyshyn (damianp@stanford.edu)
Schedule
Class meets during weeks two to five (not week 1), from April 6 to April 29.
Office hours will be held during weeks two to six, from April 7 to May 5.
Lecture: Tuesdays and Thursdays 12:30 - 13:50 PDT by Zoom (link available through Canvas).
Office hours: Wednesdays 9:00 - 11:00 PDT by Zoom (link available through Canvas).
Assignments
Final project & proposal (graded)
The only graded assignments for this class are the final project (80%) and the project proposal (20%).
Programming questions (not graded)
Programming is one of those things that you can't learn just by listening to lectures: you have to practice (and practice and practice)! However, since this is a 1 credit class, I don't want to have graded assignments on top of the project. To that end, after each session I will release a few questions to test your understanding of that session's material, and will release the answers a few days later. Responses to these questions will NOT be graded.
Course materials
There is no textbook for this class. Having said that, much of the material for this class was heavily inspired by "R for Data Science" by Garrett Grolemund and Hadley Wickham which is available online for free here. It is very comprehensive and well-written, and I recommend it highly to anyone who wants to do data science in R!