Why statisticians learn to program

Independence: otherwise, you rely on someone else giving you exactly the right tool
Honesty: otherwise, you end up distorting your problem to match the tools you have
Clarity: often, turning your ideas into something a machine can do refines your thinking
Fun: these were the best of times (the worst of times)

Cool example: catching an intruder

How this class will work

Professor Ryan Tibshirani
TAs Shannon Gallagher, Kwhangho Kim, Kevin Lin, Alan Mishler, Zixu Wang
No programming knowledge assumed
Some statistics knowledge assumed
Focus almost entirely on R
Class will be cumulative, so keep up with the material and assignments!

Each class period: 1 or 2 “mini-lectures” (10-20 minutes), then “lab” (30-40 minutes)
Lab work due at 11:59pm on each class day
Graded \(\approx\) half on attendence, half on completion
Homework due at 6pm on each Sunday
Final group project (2-3 weeks)

Grading breakdown:
- Labs: 30%
- Homework: 50%
- Final project: 20%
Class website: http://www.stat.cmu.edu/~ryantibs/statcomp-F16/
Piazza group: for announcements and discussions
Blackboard: used to collect submissions, and keep track of grades

R, R Studio, R Markdown

R is a programming language for statistical computing
R Studio is an integrated development environment for R programming
R Markdown is a markup language for combining R code with text

All 3 are free, and all 3 will be used extensively in this course

(survey, demo)

Read the syllabus

It’s on the course website

Copying

Don’t do it

Things you should have done, by the end of this week

Install R Studio on your laptop
Get comfortable with R Studio, knitting Rmd documents into HTML files
Get comfortable with navigating R help files
Get comfortable with installing R packages
Complete “Interactive R Online” introductory course, by 6pm on Sunday

Note: you should do the labs, as usual (but they won’t be graded this week)