Statistics 327-1: Introductory Data Analysis with R

Check for updates to this tentative syllabus.


Students will use R to manipulate data and perform exploratory data analysis using introductory statistics. A student completing Statistics 327-1 can do these things:

  1. Use basic R vocabulary.
  2. Manipulate data in R.
  3. Produce graphics and reports.
  4. Apply statistical methods.
  5. Run basic simulations.
Here is a more detailed course map.

NameOfficePhoneEmail (please use our Q&A forum for most things)
Gillett, JohnMedical Sciences Center 1223   262-6197
Chen, Hao
Mei, Yifan

Class Times
This online course meets during summer session ACC, May 30 to June 18, 2017.

Optional online, public question-and-answer web conferences are on the class days listed in the schedule (linked below) from 9:00-9:30 and again from 1:00-1:30.

Optional online web conference office hours are 11:00-11:30 and 2:00-5:00 each weekday as follows:
Gillett, John  11:00-11:30 each week day
Chen, Hao2:00-4:00 on course days (see Schedule for course days)
Mei, Yifan4:00-5:00 on course days plus 2:00-4:00 on non-course days

To attend public Q&A or office hours, use your browser to visit Canvas > STAT 327-1 > Web conference.

An introductory statistics course. (No programming experience is necessary.)

No textbook is required. We'll provide course notes, and we'll read R documentation and write R code.

Optional Online Reading
R for Data Science by Garrett Grolemund and Hadley Wickham
An Introduction to R (pdf) by W. N. Venables, D. M. Smith and the R Development Core Team
Advanced R by Hadley Wickham (advanced)
Intro to R video lectures by Google Developers
R Programming wikibook
Using R for Data Analysis and Graphics by J. H. Maindonald
The R Inferno by Patrick Burns (advanced)

Optional Reference Books
R for Data Science by Garrett Grolemund and Hadley Wickham
Data Manipulation with R by Phil Spector
Advanced R by Hadley Wickham (advanced)
Introductory Statistics with R by Peter Dalgaard (2008)
R in a Nutshell by Joseph Adler (2009)
A Beginner's Guide to R by Alain F. Zuur, Elena N. Ieno, and Erik Meesters (2009)
Software for Data Analysis: Programming with R by John Chambers (2008) (advanced)

A laptop is required in class.

Many questions outside of class should be posted at our Q&A forum. Please feel free to write answers when you know them. We are eager to help in class and office hours too.

During summer, 2017, this one-credit course runs in three weeks, which is 1/5 of a regular semester. The weekly workload of this course should be like that of a five-credit, one-semester course: 1 credit = (5 credits/semester)*(1/5 semester).)
(During the fall and spring, this course runs in five weeks. The weekly workload of this one-credit, five-week course should be like that of a three-credit, one-semester course: 1 credit = (3 credits/semester)*(1/3 semester).)
These points are available (we might revise this as we write course materials):
≈ 8 online quizzes (Quiz 1, ..., Quiz 8)≈ 93
≈ 4 R or R Markdown scripts (hw1.R, hw2.R, hw3.Rmd, hw4.Rmd)≈ 70
Online exam on reading and writing R code≈ 75
Ask a question or make a comment in our Web conference during class≈   1
Answer a question on piazza≈   1

Total  240

We'll assign grades according to the percentage scale, A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60) (92% of points => A); and according to the percentile scale, A = 70, AB = 60, B = 45, BC = 30, C = 10, D = 5, F = 0 (performing better than 70% of the class => A). Your grade will be the higher of these two grades.

Grades are recorded in Canvas.

If you anticipate religious or other conflicts with course requirements, or if you require accomodation due to disability, you must notify me during the first two weeks of class. You may not make up missed quizzes, homework, or exams, except in the rare case of a documented, serious problem beyond your control.

I encourage you to discuss the course, including the online quizzes, with others, but you must write the R scripts and the exam by yourself and prevent others from copying your work. (See the UW Academic Integrity policy.)

Note that the registrar's deadlines for three-week courses are special: for our session, "ACC" (5/30-6/18/2017), the add deadline is 6/1/2017 and the drop deadline is 6/3/2017.

Here is a tentative schedule.