As introduced in the course syllabus, there are two text books required in this course. Other readings will be linked below.
Other course readings will be linked below or (when necessary) made available on Canvas.
It is expected that students will be prepared when they come to each class session. Being prepared includes reading the required materials listed below.
No readings. (Course introduction)
R4DS(2e): Introduction, Whole game – Introduction, and Chapter 2: Workflow Basics
Additionally please be sure to have both R and R Studio installed on your machine before our class meeting.
Optional reading
R4DS(2e): Chapter 5: Data Tidying, Chapter 6: Workflow: Scripts and Projects, Chapter 7: Data Import
Optional reading
R4DS(2e) Visualize (Chapters 9-10)
Healy, Data Visualization: A Practical Introduction Chapters 1 and 3. (on Canvas)
Optional reading
Optional Exercises; (Solutions)
G 9.8, 9.9 (Skim)
Optional reading
In-Class Lab; Shape File #1; Shape File #2;
Optional Exercises; (Solutions)
Healy, Data Visualization: A Practical Introduction Chapter 7. (on Canvas)
Moreno and Basille, Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 1: Basics
Optional reading
Optional Exercises; (Solutions)
R4DS(2e) Chapter 22: Model - Introduction; Chapter 23: Model Basics
Kass et al. 2016. “Ten Simple Rules for Effective Statistical Practice”
Tong 2019. “Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science”
G 2.1-2.4 (Skim)
R4DS(2e) Chapter 24: Model Building; Chapter 21: Iteration
G 3.3, 4.1–4.6
Optional reading
Kuhn, Max. 2019. caret. Chapter
5: Model Training and Tuning
G 5–6
Molnar. 2021. Chapter 3: Interpretability; Section 8.1 Partial Dependence Plot; Section 8.5 Permutation Feature Importance; Section 9.1 Individual Conditional Expectation