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.

NameOffice HoursPhoneEmail (please use our Q&A forum for most things)
Gillett, JohnMedical Sciences Center 1223   262-6197
Li, QingMedical Sciences Center 1217A
Shi, IreneMedical Sciences Center B248
Johnston, LiamMedical Sciences Center 1582

Class Times
Lecture 327-001 (Gillett, John and TA Shi, Irene)TuTh 9:30-10:45Sterling 3425
Lecture 327-004 (Li, Qing and Johnston, Liam)TuTh 1:00-2:15Sterling 3425
Lecture 327-007 (Gillett, John and TA Shi, Irene)TuTh 4:00-5:15Sterling 3425

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
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
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.

The weekly workload of this one-credit, five-week course should be like that of a three-credit 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≈ 91
≈ 4 R scripts≈ 70
≈ 2 group practice exercises≈ 10
Written exam on reading and writing R code≈ 75
Ask a question or make a comment in class≈   1
Answer a question on piazza≈   1

Total  248

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.

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 five-week courses are special: for our session, "AEE", the add deadline is 1/19/2017 and the drop deadline is 2/3/2017.

Tentative Schedule
Day #: Date Subject Before class Homework due (11:59 p.m.)
01: Tu 1/17/17 Install R and RStudio
R as a Calculator
Quiz 1 demo
online lecture demo demo
auditors: quiz and piazza sign up
Read email  
02: Th 1/19 Vector (online lecture and Quiz 2)
Discuss HW1
9:30 emails
Do most of Q1
Listen to Q2
Start Q2
Bring questions
Quiz 1 (calculator)
(deadline extended to 1/24)
(login help)
03: Tu 1/24 Vector (continued) and List (online lecture and Quiz 3)
Discuss HW2
Do most of Q2
Do most of HW1
Listen to Q3
Start Q3
Quiz 2 (vector)
hw1.R (submit)
(deadlines extended to 1/26)
04: Th 1/26 Data Frame, Factor, Formula (online lecture and Quiz 4)
R Markdown
Do most of Q3
Listen to Q4
Start Q4
Quiz 3 (more vector, list)
(alternate Q3 videos)
05: Tu 1/31 (Base) Graphics (online lecture and Quiz 5)
Group practice on graphics (to be continued next time)
Discuss HW3
Do most of Q4
Do most of HW2
Listen to Q5
Start Q5
Quiz 4 (data frame)
(alternate Q4 videos)
hw2.R (submit)
06: Th 2/2 Statistical Tests and Confidence Intervals (online lecture and Quiz 6)
Group practice on graphics (continued) (submit one graphics.Rmd per group)
Do most of Q5
Listen to Q6
Start Q7
Quiz 5 (graphics)
(alternate Q5 videos)
07: Tu 2/7 Regression (online lecture and Quiz 7) (day7.R)
Discuss HW4
Group practice on Tests and Intervals (to be continued next time)
Do most of Q6
Do most of HW3
Listen to Q7
Start Q7
Quiz 6 (test, interval)
(alternate Q6 videos)
hw3.Rmd (submit)
08: Th 2/9 Group practice on Tests and Intervals, continued (submit one tests.Rmd per group)
Discuss exam
Do most of Q7
Listen to Q8
Quiz 7 (regression)
(alternate Q7 videos)
09: Tu 2/14 Simulation (online lecture and Quiz 8)
Review Q & A
Do most of Q8
Do most of HW4
Quiz 8
(alternate Q8 videos)
hw4.Rmd (submit)
10: Th 2/16 Exam (rules)
Study for exam