University of Wisconsin-Madison
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.

NameOfficeOffice hourEmail (please use our Q&A forum for most things)
Yang, BoMedical Sciences Center 1227    Thur 11:50-12:50 am or by appointment
Li, QingMedical Sciences Center 1217A   Tues,Thur 3:00-4:00 pm
TA Kim, Yongjoon

Class Times
Lecture 327-001 (Teacher Li, Qing)Tues, Thur, 11:00 am-12:15 pm STERLING 2301
Lecture 327-004 (Teacher Yang, Bo)Tues, Thur, 1:00-2:15 pmSTERLING 2301
Lecture 327-007 (Teacher Yang, Bo)Tues, Thur, 2:30-3:45 pmSTERLING 2301

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.

This one-credit course has an expectation of a total of 45 hours of student engagement:

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
≈ 2 group practice exercises≈ 10
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  250

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 for 327-010 and D2L for 327-001 & 327-007.

If you anticipate religious or other conflicts with course requirements, or if you require accomodation due to disability, you must notify your instructor 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.

We 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, AEE (1/23-2/25/2018), the add deadline is 1/25/2018 and the drop deadline is 2/9/2018.

Tentative Schedule
Day #: Date Subject Before class Homework due (11:59 p.m.)
01: Tu 1/23/18 Install R and RStudio
1. R as a Calculator
Demo of Quiz 1, online lecture,
Read email
Listen Q1
Bring questions
02: Th 1/25 2. Vector
Discuss HW1
Most of Q1
Listen Q2
Quiz 1
(login help)
03: Tu 1/30 3. Vector (continued) and List
Discuss HW2
Most of Q2
Most of HW1
Listen Q3
Quiz 2
hw1.R (submit)
04: Th 2/1 4. Data Frame, Factor, Formula (flowers.csv)
Most of Q3
Listen Q4
Quiz 3
05: Tu 2/6 5. (Base) Graphics
Group practice on graphics (to be continued)
R Markdown
Discuss HW3
Most of Q4
Most of HW2
Listen Q5
Listen R Markdown
Quiz 4
hw2.R (submit)
06: Th 2/8 6. Statistical Tests and Confidence Intervals
Group practice on graphics (continued) (submit one graphics.Rmd per group)
Most of Q5
Listen Q6
Quiz 5
07: Tu 2/13 7. Regression
Discuss HW4
Group practice on Tests and Intervals (to be continued)
Most of Q6
Most of HW3
Listen Q7
Quiz 6
hw3.Rmd (submit)
08: Th 2/15 8. Simulation
Group practice on Tests and Intervals (continued) (submit one tests.Rmd per group)
Discuss exam
Most of Q7
Listen Q8
Quiz 7
9: Tu 2/20 Review Q & A Most of Q8
Most of HW4
Read email
Quiz 8
hw4.Rmd (submit)
10: Th 2/22 Exam (rules)
Study for exam Exam