R Independent Study

Announcement

Spring 2018 is the last semester we offer this course. All course material is avaliable here and on this GitHub page for anyone who wants to study the material on their own.

Syllabus

This course is designed for Stat 200 students in sections L1 and Online who are interested in learning R, but other students who have taken Stat 200 L1, L2 or Online are also welcome. If you have a solid background in computer programming and have taken STAT 200 or STAT 212, consider taking STAT 385 instead.

This is a 2-credit online independent study. You are expected to learn the material on your own. There are no class meetings or face-to-face discussion groups. Anticipate spending 6 – 8 hours a week. Not everyone can learn well in this setting, so this is not a course for everyone. All the required course work are posted on the lessons page. Your grade will be determined by the weekly Lon Capa assignments due every Sunday at 11:59 pm.

Credit Hours: This is a 2-hour credit course. Make sure that's what you chose on UI Enterprise, otherwise you'll get short-changed. If there is special reason (like going over hours) you prefer to only get 1-hour credit, email us fireman@illinois.edu. Note that you still have to do the same amount of work even if you take it as a one-credit hour.

Pre-requisite: You should have taken a basic one-semester statistics course like Stat 100, and are concurrently taking/have taken Stat 200. All students can access the material of this course from the course website except the Lon Capa assignments.

Course Staff

Instructor: Ellen Fireman (email: fireman@illinois.edu)
Course Assistant: David Collier and Karle Flanagan

Website: http://courses.las.illinois.edu/stat/stat200/RProgramming/
(short URL: go.illinois.edu/stat390)

TA Office Hours: TBA

Course Goal and Philosophy

Upon completion of this course, you will be expected to be able to use R to perform various statistical analyses you have learnerd in Stat 100 and Stat 200.

R is more than a set of separate little calculator-like commands. It's a full programming language with an internal logic. As with any language, acquiring fluency requires real practice. Our exercises are mostly not cut-and-paste phrases. Instead we build toward real-world use.

Materials

Fortunately there are already many high-quality free resources available for R learners and users. We structure the course around those materials. We will use mainly three resources in this course.

Textbook: R Programming for Data Science by Roger D. Peng.
This is an ebook. The suggested price is $20, but you can get it free if you want. More information on the book and how to purchase it can be found in Week 1's notes.

swirl: A software package written in R. It provides interactive lessons for beginners to learn R. Instructions for installing swirl are given in Week 1's notes.

Weekly Reading Assignment: There is an html reading assignment every week. The links to the notes are posed on our lessons page. In these notes, we demonstrate how R can be used to tackle problems encountered in Stat 100 and Stat 200.

Lon Capa Assignments: We will integrate these materials into a set of lesson plans with weekly Lon Capa homework assignments, mostly using individually randomized data sets and graded automatically. Most of these problems are not very hard, but since each of you has a slightly different data set, copying answers will not work! Starting from Week 7, you will also be given problems that require you to write codes and explain the process of analyzing the data. These problems will be hand-graded by the TAs.

There will be two types of assignments: weekly regular homework assignment and weekly quiz. The purpose of the regular homework assignments is to give you practice for the R commands and using R to solve problems in statistics. You will get instant feedbacks on whether or not your answers are correct. The quizzes are designed to test your knowledge of R commands and skill in using R to solve problems. Unlike the regular homework assignments, you won't get any feedbacks on your submitted answers to those quizzes until after the due dates, but you can change your answers many times before the due dates. You won't get any help from the TAs on the quizzes, except for clarification of questions.

Late assignments are NOT accepted on Lon-Capa. However, Lon-Capa grades each problem in the assignment separately, so if you do 70% of the homework correctly before the due date, you'll get credit for that 70%. Your lowest regular homework score and your lowest quiz score from Weeks 2-12 will be dropped at the end of the semester (all assignments in Weeks 13 and 14 will be counted).

Grading

The grades will be based on the weekly Lon Capa assignments due every Sunday at 11:59pm starting on January 28th. The regular homework assignments count 90% and quizzes count 10% of the total grade. There are no exams or projects. That means everyone can do well just by hard work. Your grades will appear on Compass. (Lon Capa's gradebook is too confusing, so check your grades on Compass.)

Overall grade is translated into a letter grade as follows:

Bonus Points: There will be several bonus assignments. The first is the syllabus quiz, given on the first week and due by the end of the second week (Jan 28th). The last is a survey on this course, which will be given at the end of the semester. Additional bonus R problems will be available later in the semester. Bonus points can only help you. You can still get 100% without doing any bonus work. Bonus points are figured into your grade as follows:

(Percentage on Required Work) + 0.25×(Percentage on Bonus Points)
100 + 0.25×(Percentage on Bonus Points)

Suppose at the end of the semester you have an 80% average and you get 90% of the bonus work. Your course total will be (80 + 0.25×90)/(100 + 0.25×90) = 102.5/122.5 = 83.67%. So your grade would be raised from a B- to a B.

Course Schedule

Lessons are posted here for the entire semester.

^* Note that Week 13 is 1.5-week long.