Statistics 327-1: Introductory Data Analysis with R

Check www.stat.wisc.edu/~jgillett/327-1 for updates to this tentative syllabus.

Goals

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.

Teachers
NameOfficeOffice hourEmail (please use our Q&A forum for most things)
Johnston, LiamMedical Sciences Center 1582   Thur 9:30-11:30 am   ljohnston2@wisc.edu
Qing, LiMedical Sciences Center 1217A   Mon,Thur 10:50-11:50 am    qli295@wisc.edu
TAs
TA Song, Jie jsong@stat.wisc.edu
TA Wang, Jili jili@stat.wisc.edu

Class Times
Lecture 327-001 (Teacher Li, Qing and TA Wang, Jili)Tues, Thur, 9:30-10:45 amSTERLING 2301
Lecture 327-007 (Teacher Li, Qing and TA Song, Jie)Tues, Thur, 4:00-5:15 pmSTERLING 2301
Lecture 327-010 (Teacher Johnston, Liam and TA Song, Jie)Tues, Thur, 8:00-9:15 pmSTERLING 2301
Lecture 327-011 (Teacher Johnston, Liam and TA Song, Jie)Tues, Thur, 1:00-2:15 pmIngraham 122

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

Textbook
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)

Computing
A laptop is required in class.

Help
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.

Grades
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
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  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 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 (9/6-10/8/2017), the add deadline is 9/8/2017 and the drop deadline is 9/22/2017.

Tentative Schedule
Day #: Date Subject Before class Homework due (11:59 p.m.)
01: Th 9/7/17 Install R and RStudio
1. R as a Calculator
Demo of Quiz 1, online lecture, piazza.com
Read email
Listen Q1
Bring questions
 
02: Tu 9/12 2. Vector
Discuss HW1
Most of Q1
Listen Q2
Quiz 1
(login help)
03: Th 9/14 3. Vector (continued) and List
Discuss HW2
Most of Q2
Most of HW1
Listen Q3
Quiz 2
hw1.R (submit)
04: Tu 9/19 4. Data Frame, Factor, Formula (flowers.csv)
R Markdown (screencast)
Most of Q3
Listen Q4
Quiz 3
05: Th 9/21 5. (Base) Graphics
Group practice on graphics (to be continued)
Discuss HW3 (first listen to RMarkdown, above)
Most of Q4
Most of HW2
Listen Q5
Quiz 4
hw2.R (submit)
06: Tu 9/26 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: Th 9/28 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: Tu 10/3 8. Simulation
Group practice on Tests and Intervals (continued) (submit one tests.Rmd per group)
Discuss exam
Most of Q7
Listen Q8
Quiz 7
09: Th 10/5 Exam (rules)
Most of Q8
Most of HW4
Quiz 8
hw4.Rmd (submit)