See Lecture 1 for course details
Office hours: Tuesday 1230-1330
Piazza webpage
Office hours: Tuesday 1230-1330
Announcements
| Jan 8 | Make sure you're registered for the class blackboard and
piazza webpages (see Lecture 1). Be sure to answer the class survey and mail it to the class email address |
|
| Jan 16 | Homework 1 is up (due before midnight on Friday, Jan 28). | HW1 |
| Jan 29 | Homework 2 is up (due before class on Feb 8). | HW2 |
| Feb 11 | Homework 3 is up (due before class on Tuesday, Feb 20). | HW3 |
| Feb 11 | Midterms from previous years. | 2015, 2016 2017 |
| Feb 27 | Homework 4 is up (due before class on Friday, March 9). | HW4 |
| March 8 | Homework 5 is up (due before class on Tuesday, Mar 21). | HW5 |
| April 3 | Homework 6 is up (due before midnight on Friday, Apr 13). | HW6 |
| April 13 | Homework 7 is up (due before midnight on Monday, Apr 30). | HW7 |
| April 14 | Midterms from previous years. | 2015, 2016 2017 |
Reading
| Jan 8 | R Markdown and knitr. In particular, look at Markdown basics, R code chunks and the Reference guide. |
| Jan 12 | Useful RStudio shortcuts. And here, more shortcuts than you'll probably need. |
| Jan 24 | Pages 25 to 35, 59 to 63, 73 to 75 (Sections 2.1 to 2.4, 3.1 to 3.2.2 and 3.4 to 3.4.1 of Matloff) |
| Jan 23 | Looping and vectorization:
Link1
Link2.
Also see the third and fourth circles in the
R inferno . Pages 35 to 57, 78 to 83, 85 to 112 (Sections 2.5 to 2.12, 3.5 to 3.8, Chapter 4, and Sections 5 to 5.4 of Matloff). |
| Jan 30 | Pages 112 to 114 (Sections 5.4.1 and 5.4.2), and all of Chapter 7 except the "Extended examples" of Matloff |
| Feb 8 | Looping and vectorization. link |
| Mar 8 | Data transformation: Chapter 5 of R for Data Science. Also, read Chapter 9 on functions from the same link. |
| Mar 11 | More on tidyverse: ( dplyr ). |
| Mar 19 | Tidy data: ( tidy data ). |
| Mar 25 | Object oriented programming. Sections 9.1 and 9.2 of Matloff (except the extended example). Read about S3 classes (you can ignore S4 classes) here. |
| Mar 27 | Regular expressions: Tutorial 1. Tutorial 2. |
| Apr 9 | ggvis: basics. examples. |
Notes (only the .html files are slides)
- Lecture 1 (pdf)
- Lecture 2 (pdf)
- Lecture 3 (pdf)
- Lecture 4 (pdf)
- Lecture 5 (pdf)
- Lectures 6 (pdf)
- Lectures 8 (pdf) (Datasets: City data, Troops data)
- Lectures 9 (pdf) (Datasets: Income)
- Lecture 10 (pdf)
- Lecture 11
- Lecture 12 (pdf)
- Lecture 13 (Datasets: Movie data)
- Lectures 14 (pdf)
- Lecture 15 (pdf)
- Lecture 16 (pdf)
- Lecture 17 (pdf)
- Lecture 18 (pdf) (Datasets: Home prices data
- Lecture 19
- Lecture 20
- Lecture 21
References
Useful textbooks/online references:- "The Art of R Programming: A Tour of Statistical Software Design", Norman Matloff
- "R for Data Science" , Garrett Grolemund and Hadley Wickham
- "Advanced R" , Hadley Wickham
- "The R Inferno" , Patrick Burns
- "Efficient R Programming" , Colin Gillespie and Robin Lovelace
- "An introduction to R"
- Programming tips for statisticians
- "Software for Data Analysis", John M. Chambers