Course Expectations

I expect you to be here to learn R. R is not an easy thing to learn - there is a sharp learning curve, and we’re going to try and get over that curve in 2 weeks. This is a difficult task. However, it is a task I think is doable – if you’re willing to lean into the ride! I am here to make the ride a little bit easier for you.

The reading (yes, there is a textbook, it’s free, but you do need to use it) should take you about one hour. The assigned homework should take you anywhere from 35 minutes to one hour.

On the other hand, I expect you to ask questions. I expect you to tell me to slow down.

The Secret

The secret to learning how to code is trying it out yourself. R is a playground of “I wonder what would happen if…” and “I wonder why this didn’t work” or really “I wonder why this DID work!” (more than likely that one). This means to get gud, you need to put in some serious effort. If you see an error message, I want your first reaction to be “Huh! Okay, what does that mean?” not “AHHHHH DR.C I BROKE IT I GIVE UP.”

About Me

My name is Dr. Carriere. You can call me Dr. C, Dr. Carriere, Prof. C, Professor Carriere. Most students call me Dr. C. While I enjoy informality of ideally calling me “Kevin”, I must fight back against gender discrimination, and females are called Dr. less often. I am an Assistant Professor of the Psychology Department, and like most things baking, chess, Pokemon, and statistics.

How (and what) To Read

The book we are using is Danielle Navarro’s “Learning statistics with R: A tutorial for psychology students and other beginners.” Danielle is one of my personal idols - she is brilliant at R and makes actual art with R. You may see her art appear if I minimize my screen – her art rotates in my background photos. You can learn more about our author https://djnavarro.net/.

This textbook will be different. There will be text, but as Danielle writes herself:

“I want you to be able to copy code from the book directly into R if if you want to test things.”

And I ask that you do that. While reading the chapter, read (her writing style is very engaging!) but also actively follow along. Copy and paste her code into the Console (we’ll learn more about this on Monday, so don’t worry too much about Monday doing that, but it would help to loop back to this after class.)

What This Course Is Not (Methods, Basically)

This course is not going to ask you to analyze any data using any statistical methods. We not only do not have time to teach it, but without any pre-reqs for the course, it could easily consume us. Besides, it is far too easy for me to slip into a rabbit hole about analyzing data, and if you want that, take my seminar courses and advanced labs with me (and take Statistics for the Life Sciences (BIO-245)!.) We will understand what data looks like, we will learn how to manipulate it, and we will learn various ways to visualize it. We will not learn if things are “significant” or not.

How You’ll Be Graded

Course Schedule

Instructions for Homework Submission

Instructions for homework submission can be viewed here.

Monday, January 10th

To Do Before Class

To Read For Class

Swirl Homework due Tuesday, 9:00am

  • R Programming: Basic Building Blocks 1
  • R Programming: Workspace and Files
  • A (very) short introduction to R: Module 1
  • R Programming: Sequences of Numbers
  • R Programming: Vectors

Tuesday, January 11th

To Read For Class

Swirl Homework, due Wednesday, 9:00am

  • R Programming: Missing Values
  • R Programming: Subsetting Vectors
  • A (very) short introduction to R: Module 2
  • R Programming: Matrices and Data Frames
  • R Programming: Looking at Data

Wednesday, January 12th

To Read For Class

Swirl Homework, due Thursday, 9:00am

  • Exploratory Data Analysis: Principles of Analytic Graphs
  • R Programming: Base Graphics
  • Exploratory Data Analysis Base Plotting Systems

Thursday, January 13th

To Read For Class

Swirl Homework, due Friday, 9:00am

  • Exploratory Data Analysis: Exploratory Graphs
  • Exploratory Data Analysis: Working With Colors

Friday, January 14th

To Read For Class

Swirl Homework, due Monday, 9:00am

  • Getting and Cleaning Data: Manipulating Data with dplyr
  • Getting and Cleaning Data: Grouping and Chaining with dplyr
  • Getting and Cleaning Data: Tidying Data with tidyr

Monday, January 17th

To Read For Class

-https://drive.google.com/file/d/1fhb-u7pfzCME2X5Gcj-3vQGrhdZjwWdl/view?usp=sharing

Swirl Homework, due Tuesday, 9:00am

  • Exploratory Data Analysis: GGPlot2 Part1
  • Exploratory Data Analysis: GGPlot2 Part2
  • Exploratory Data Analysis: GGPlot2 Extras

Tuesday, January 18th

  • R Programming: Functions

Course Homework, due Wednesday, 9:00am

  • Dr. C will upload a lab.

Wednesday, January 19th

Course Homework, due Thursday, 9:00am

  • Dr. C will upload a lab.

Thursday, January 20th

Course Homework, due Friday, 9:00am

  • Final Project Submission

Friday, January 21st

Useful Places To Check For Advice

Final Project

Your final project is your own. I have very loose guidelines because I’m not 100% sure how far we’ll get in two weeks. The overall goal is to replicate a published figure in a psychology paper. Details will follow on exactly what this will look like, but I have been collecting a list of datasets and the corresponding figure numbers slowly. Each folder also should come with a baseline description of the file. More information will come by Friday, January 14th.