Statistical Computing: Fall 2016

Statistics 36-350

Instructor: Ryan Tibshirani (ryantibs at cmu dot edu)

TAs:
Shannon Gallagher (sgallagh at andrew dot cmu dot edu)
Kwhangho Kim (kwhanghk at andrew dot cmu dot edu)
Kevin Lin (kevinl1 at andrew dot cmu dot edu)
Alan Mishler (amishler at andrew dot cmu dot edu)
Zixu Wang (zixuw at andrew dot cmu dot edu)

Lecture times: Mondays, Wednesdays, and Fridays 11:30am-12:20pm, Baker Hall A51

Office hours:
RT: Mondays 3-4pm, Tues 4-5p, Fridays 12:20-1pm, Baker Hall 229B
SG: Mondays 4-5pm, Baker Hall 132M
KL: Tuesdays 6-7pm, Porter 117
KK/AM/ZW: Friday 3:30-4:30pm, Baker 132M

Syllabus: here. Discussions: Piazza group.

Class schedule

Week 1: Mon Aug 29 -- Fri Sept 2. Introduction to R and strings.

Monday:
- Introduction: slides, html, Rmd
- Intruder problem: intruder.R, intruder_demo.R
Wednesday:
- String basics: slides, html, Rmd
- Lab 1w: html, Rmd
Friday:
- Substrings: slides, html, Rmd; splitting and combining strings: slides, html, Rmd
- Lab 1f: html, Rmd
Sunday: "Interactive R Online", an introductory course, to be completed by 6pm
Week 2: Mon Sept 5 -- Fri Sept 9. Basic text manipulation.

Monday: no class (Labor Day)

Wednesday:
- Reading in text: slides, html, Rmd; summarizing text: slides, html, Rmd
- Lab 2w: html, Rmd
Friday:
- Bag-of-words: slides, html, Rmd
- Lab 2f: html, Rmd
Sunday: Homework 1 due at 6pm
Week 3: Mon Sept 12 -- Fri Sept 16. Regular expressions.

Monday:
- Regex patterns: slides, html, Rmd
- Lab 3m: html, Rmd
Wednesday:
- Quantifiers and scope: slides, html, Rmd; extractions and replacements: slides, html, Rmd
- Lab 3w: html, Rmd
Friday:
- Splitting and searching with regexes: slides, html, Rmd
- Lab 3f: html, Rmd
Sunday: Homework 2 due at 6pm
Week 4: Mon Sept 19 -- Fri Sept 23. Plotting.

Monday:
- Plot basics: slides, html, Rmd; adding to plots: slides, html, Rmd
- Lab 4m: html, Rmd
Wednesday:
- Histograms and heatmaps: slides, html, Rmd
- Lab 4w: html, Rmd
Friday:
- Curves, surfaces, and colors: slides, html, Rmd
- Lab 4f: html, Rmd
Sunday: Homework 3 due at 6pm
Week 5: Mon Sept 26 -- Fri Sept 30. Functions.

Monday:
- Function basics: slides, html, Rmd
- Lab 5m: html, Rmd
Wednesday:
- Return values and side effects: slides, html, Rmd
- Lab 5w: html, Rmd
Friday:
- Function design: slides, html, Rmd
- Lab 5f: html, Rmd
Sunday: Homework 4 due at 6pm
Week 6: Mon Oct 3 -- Fri Oct 7. Iteration and simulation.

Monday:
- Iteration basics: slides, html, Rmd
- Lab 6m: html, Rmd
Wednesday:
- Simulation basics: slides, html, Rmd
- Lab 6w: html, Rmd
Friday:
- Iteration and simulation: slides, html, Rmd
- Lab 6f: html, Rmd
Sunday: Homework 5 due at 6pm
Week 7: Mon Oct 10 -- Fri Oct 14. Data frames and apply.

Monday:
- Data frames: slides, html, Rmd
- Lab 7m: html, Rmd
Wednesday:
- The apply family: slides, html, Rmd
- Lab 7w: html, Rmd
Friday:
- More on apply: slides, html, Rmd
- Lab 7f: html, Rmd
Sunday: Homework 6 due at 6pm
Week 8: Mon Oct 17 -- Fri Oct 21. Debugging.

Monday:
- Debugging basics: slides, html, Rmd
- Lab 8m: html, Rmd
Wednesday:
- Debugging with R tools: slides, html, Rmd
- Lab 8w: html, Rmd
Friday: no class (Midsemester Break)

Sunday: Homework 7 due at 6pm
Week 9: Mon Oct 24 -- Fri Oct 28. Reading in, reordering, and merging data.

Monday:
- Defensive programming: slides, html, Rmd
- Lab 9m: html, Rmd
Wednesday:
- Reading in and reordering data: slides, html, Rmd
- No lab
Friday:
- Merging data: slides, html, Rmd
- Lab 9f: html, Rmd
Week 10: Mon Oct 31 -- Fri Nov 4. Fitting models to data.

Monday:
- Exploratory data analysis: slides, html, Rmd
- Lab 10m: html, Rmd
Tuesday: Homework 8 due at 11:59pm

Wednesday:
- Linear models: slides, html, Rmd
- No lab
Friday:
- Beyond linear models: slides, html, Rmd
- Lab 10f: html, Rmd
Week 11: Mon Nov 7 -- Fri Nov 11. Split-apply-combine and plyr.

Monday:
- Split-apply-combine: slides, html, Rmd
- Lab 11m: html, Rmd
Tuesday: Homework 9 due at 11:59pm

Wednesday:
- Plyr: a*ply() and l*ply() slides, html, Rmd
- No lab
Friday:
- Plyr: d*ply() slides, html, Rmd
- Lab 11f: html, Rmd
Week 12: Mon Nov 14 -- Fri Nov 18. Relational databases.

Monday:
- SQL: Queries slides, html, Rmd
- Lab 12m: html, Rmd
Tuesday: Homework 10 due at 11:59pm

Wednesday:
- SQL: Computations slides, html, Rmd
- No lab
Friday:
- SQL: Join slides, html, Rmd
- Lab 12f: html, Rmd
Week 13: Mon Nov 21 -- Fri Nov 25. Eat food, be thankful.
- Monday: no class (Thanksgiving Holiday)
- Tuesday: Homework 11 due at 11:59pm
- Wednesday: no class (Thanksgiving Holiday)
- Friday: no class (Thanksgiving Holiday)
Final exam released: html, Rmd
Due Wednesday December 7 at 11:59pm
Week 14: Mon Nov 28 -- Fri Dec 2. Statistical prediction.

Monday:
- Training and testing errors: slides, html, Rmd
- Lab 13m: html, Rmd
Tuesday: no homework due, work on final projects

Wednesday:
- Sample-splitting and cross-validation: slides, html, Rmd
- No lab
Friday:
- Statistical prediction machines: slides, html, Rmd
- Lab 13f: html, Rmd
Week 15: Mon Dec 5 -- Fri Dec 9. Final project week.

Monday: no class (work on projects)
Wednesday: no class (work on projects), final take-home exam due at 11:59pm
Friday: final projects autograding

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Supplementary materials

Supplementary textbooks on R programming:

The R Cookbook, by Paul Teetor
The R Graphics Cookbook, by Winston Chang
The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff
Data Manipulation with R, by Phil Spector
Software for Data Analysis: Programming with R, by John Chambers (advanced book)

Online resources for R programming:

The official intro, "An Introduction to R", available online in html and pdf versions
simpleR, by John Verzani
Google R Style Guide offers some rules for naming, spacing, etc., which are generally good ideas
Quick-R, this is primarily aimed at those who already know a commercial statistics package like SAS, SPSS or Stata, but it's very clear and well-organized, and others may find it useful as well
The R Inferno, by Patrick Burns ("If you are using R and you think you're in hell, this is a map for you")
R Fundamentals and Programming Techniques, by Thomas Lumley

The website Software Carpentry is not specifically R related, but contains a lot of valuable advice and information on scientific programming.

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