**This webpage is only for the prospective students. You can enroll in this course officially and find more accurate details on Canvas.**

Students will use R to manipulate data and perform exploratory data analysis using introductory statistics. A student completing this course can do these things:

- Use basic R vocabulary.
- Manipulate data in R.
- Produce graphics and reports.
- Apply statistical methods.
- Run basic simulations.

Here is a more detailed course map.

Check the syllabus on Canvas.

- The online section is totally asynchronous, that is, no live class time.
- A quick start guide is shown on Canvas.
- There are eight topics in the schedule labeled “1. R as a Calculator” through “8. Simulation”. For each topic, there are links to four types of course materials:
- There are lecture notes, each consisting of 1- to 3-page “.pdf” file on the schedule.
- There is an online quiz to provide low-stakes practice. You may repeat it many times. Your best score before its deadline counts. Each quiz includes 5 to 18 questions. Each question can be answered by writing a couple of lines of R code (please save them just in case). Then, my TA will collect your grades to post them on Canvas manually later.
- There are screencast videos, linked from the quiz or schedule, to guide you through the lecture notes and prepare you for quiz questions and homework.
- There are 8 lecture codes with detailed comments posted on Canvas. Please download and run them line by line together with videos and pdf file. Also, read all my comments carefully.

- There are four homework assignments, each requiring several to about 100 lines of code, most of them already written in a sketch.
- There is an online exam implemented as a Canvas quiz to be released later.
- For each day in the schedule, do the things listed in its “Before Class” column before the date that fits your schedule. Complete the things in the “Assignment Due” column before 11:59 p.m.

An introductory statistics course. To be exact, no programming experience is necessary, but the basic stat, such as distributions, hypothesis testing, estimation, regression and simulation, is definitely needed!

The optional textbook is R in Action: Data Analysis and Graphics with R (2nd Ed) (with its liveBook) by Robert Kabacoff (2015) (good for Intermediate and Advanced R). Just use it for reference. Moreover, we’ll provide course notes, and we’ll read R documentation and write R code.

- R for Data Science by Garrett Grolemund and Hadley Wickham (2017) (good for Intermediate and Advanced R, using some packages)
- Introductory Statistics with R by Peter Dalgaard (2008) (good for mastering basic statistics and Base R)
- R for Excel Users by John Taveras (2016) (good for beginners knowing Excel)

- R for Data Science by Garrett Grolemund and Hadley Wickham (2017) (good for Intermediate and Advanced R, using some packages)
- An Introduction to R (pdf) by W. N. Venables, D. M. Smith and the R Development Core Team
- Advanced R (2nd Ed) 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)

- Data Manipulation with R by Phil Spector
- Advanced R (2nd Ed) by Hadley Wickham (advanced)
- 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 (better with a backup computer). If you have trouble with R, RStudio or R mark down in your own computer, try RStudio Cloud (without installation) or visit Computer lab locations - UW-Madison Information Technology.

- Search the solutions by using R help (?) and Google on your own.
- Ask a qustion via our asynchronous piazza Q&A forum by following its posting guidelines. Please feel free to write answers when you know them (guide line and general thinking only), but don’t post the detailed code.
- Schedule a web meeting with TA and me, if needed.

The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform me of their need for instructional accommodations by the end of the second week of the semester, or as soon as possible after a disability has been incurred or recognized. I, will work either directly with the student or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student’s educational record, is confidential and protected under FERPA.

Any student signed up for honors should approach me within the first two weeks of the course to discuss a potential project. A timeline will be set up that helps students create their own project. Honors projects will be due at the end of the semester. If honors projects fail, a Q score will be assigned at the end of the session and the Dean Office can be contacted for further paperwork.

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):

Total | 240 (Then, divided by 2.4 to convert to the points out of 100) |
---|---|

\(\approx\) 8 online quizzes (Quiz 1, …, Quiz 8) | \(\approx\) 93 |

\(\approx\) 4 R or R Markdown scripts (hw1.R, hw2.R, hw3.Rmd, hw4.Rmd) | \(\approx\) 70 |

Online exam on reading and writing R code | \(\approx\) 75 |

Make a brief note in Piazza to introduce yourself before the end of the first week | \(\approx\) 1 |

Answer a question on Piazza | \(\approx\) 1 |

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

Grades are recorded in Canvas and you need to report any grading errors by the deadlines I will email you later.

If you anticipate religious or other conflicts with course requirements, 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 our AEE session courses are special.

Check the Canvas Calendar.

- Grading should be done around two weeks after the final. Then, you can check it on Canvas and Registrar System.

- Considering to take our intermediate R course? Please check Interm R (STAT 304) for more details.