The sample analysis was shown only in class and is not viewable in this version of the notes.

• Course overview

• Introduction to R, RStudio and R Markdown

• Programming basics

• No programming knowledge presumed

• Some stats knowledge presumed. E.g.:
  • Hypothesis testing (t-tests, confidence intervals)
  • Linear regression

• Synchronous attendance is encouraged, but not required

• Class will be very cumulative

• Two 80 minute lectures a week:
  • First 60-70 minutes: concepts, methods, examples
  • Last 10-20 minutes: short labs
• Class participation (10%)
• Quizzes (10%)
• Weekly homework (40%)
• Final project (2.5 weeks) (40%)
  • Disclaimer: To pass the class, you must achieve a passing score on the final project
    (at least 21 / 40)

• Class participation (10%)
  • Labs: Each lecture has an accompanying lab assignment.
  • Course website shows how participation grade will be calculated
• Quizzes (10%)
  • 4 quizzes in the second half of term. Dates TBA.
• Homework assignments (40%)
  • There will be 5 weekly HW assignments
  • Single lowest HW score will be dropped
  • HW assigned on Wednesdays, due Wednesdays at 1:30PM ET
  • Late homework will not be accepted for credit
• Final project (40%)
  • You will write a report analysing a policy question using a publicly available data set

• Assignments, office hours, class notes, grading policies, useful references on R: http://www.andrew.cmu.edu/~achoulde/94842/

• Canvas for gradebook and for turning in homework

• Piazza for discussion forum (embedded in Canvas)
  • Please post class/homework related question on Piazza instead of emailing the teaching staff

• Check the class website for everything else

• No required textbook, but I highly recommend:
  • Garrett Grolemund and Hadley Wickham, R for Data Science

• Free (open-source)

• Programming language (not point-and-click)

• Excellent graphics

• Offers broadest range of statistical tools

• Easy to generate reproducible reports

• Easy to integrate with other tools

Basic interaction with R is through typing in the console

This is the terminal or command-line interface

• You type in commands, R gives back answers (or errors)

• Menus and other graphical interfaces are extras built on top of the console

• We will use RStudio in this class

1. Download R: http://lib.stat.cmu.edu/R/CRAN

2. Then download RStudio: http://www.rstudio.com/

RStudio has 4 main windows (‘panes’):

• Source
• Console
• Workspace/History
• Files/Plots/Packages/Help

1. Source pane: create a file that you can save and run later

2. Console pane: type or paste in commands to get output from R

3. Workspace/History pane: see a list of variables or previous commands

4. Files/Plots/Packages/Help pane: see plots, help pages, and other items in this window.