DRAFT

**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:

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

Teachers | |||

Name | Office Hours | Phone | Email (please use our Q&A forum for most things) |

Gillett, John | Medical Sciences Center 1223 | 262-6197 | jgillett@wisc.edu |

TAs | |||

TA lastname, first? | Medical Sciences Center ? | TA email? | |

**Class Times**

This is an online course meeting during summer session ACC, May
30 to June 18, 2017. There will be live online meetings at the
following times:

Lecture 327-001 (Gillett, John and TA ?) | days? times? | optional room? |

**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**

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 three-week courses are special: for our session, "ACC" (5/30-6/18/2017), the add deadline is 6/1/2017 and the drop deadline is 6/3/2017.

**Tentative Schedule**

Day #: Date |
Subject |
Before class |
Homework due (11:59 p.m.) |

01: Tu 5/30/17 | Install R and RStudio R as a Calculator Quiz 1 demo online lecture demo piazza.com demo auditors: quiz and piazza sign up |
Read email | |

02: We 5/31 | Vector (online lecture and Quiz 2) Discuss HW1 |
Do most of Q1 Listen to Q2 Start Q2 Bring questions |
Quiz 1 (calculator) (login help) |

03: Fr 6/2 |
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) |

04: Mo 6/5 |
Data Frame, Factor, Formula
(online lecture and Quiz 4) (flowers.csv) R Markdown |
Do most of Q3 Listen to Q4 Start Q4 |
Quiz 3 (more vector, list) |

05: Tu 6/6 |
(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) hw2.R (submit) |

06: We 6/7 |
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) |

07: Fr 6/8 |
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) hw3.Rmd (submit) |

08: Mo 6/12 | 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) |

09: We 6/14 |
Simulation (online lecture and Quiz 8) Review Q & A |
Do most of Q8 Do most of HW4 |
Quiz 8 (simulation) hw4.Rmd (submit) |

10: Fr 6/16 | Exam (rules) |
Study for exam |