**Statistical Computing with R (Stat 226) **

**Fall 2022**

Professor |
Bradley A. Hartlaub |

Office |
305 Rutherford B. Hayes Hall |

Phone |
PBX 5405 |

e-mail |
hartlaub@kenyon.edu This is the best way to contact me, and I will respond within 24 hours. |

Office Hours |
MWF 2:00 - 3:00 and Tuesday 2:00 - 4:00 Additional appointments are available; please don't hesitate to contact me for help. |

__Textbook__

*Modern Data Science with R*, Second Edition, (2021), Taylor & Francis Group, LLC, Boca Raton, FL

__Useful Resource (Optional)__

*Introduction to Scientific Computing and Simulation Using R*, Second Edition, (2014), Owen Jones, Robert Maillardet, and Andrew Robinson, Chapman & Hall/CRC The R Series

__Learning Goals__

- Improve technical communication skills
- Introduce RStudio statistical software
- Develop statistical computing skills
- Foster productive work habits with peers, including joint presentations and assignments
- Build an understanding of computational methods: their importance and use

__Statistical Packages &
Computing__

R and RStudio will be used extensively throughout the course. Assignments and course announcements will be sent to you via e-mail or posted on the course web page. Data sets and programs will be placed in folders on Google Drive. Proper maintenance of computer accounts, files, etc. is your responsibility.

Our class meets in a classroom where you will be expected to use your laptop, and we will be using statistical software extensively in the course. During regular class hours, the use of computers is restricted to students enrolled in the course. Furthermore, the use of the computers is also restricted to activities deemed appropriate by the instructor. Playing computer games, reading e-mail, conversing in a chat room, surfing the web, and working on assignments for other courses are a few examples of inappropriate activities that can be distracting to the instructor and other students in the course. Inappropriate computer use may result in penalties ranging from warnings to loss of computer privileges for the period. In cases of extreme and/or repeated violations, grade penalties or expulsion from the course may result.

__Accessibility Accomodations__

A student who thinks they may need an accommodation to access a campus program, activity, or service should contact Ruthann Daniel Harteis in Student Accessibility and Support Services (SASS) at danielharteis1@kenyon.edu to discuss specific needs. Advance notice is required to review documentation, evaluate accommodation requests and provide notice or arrangements for any accommodation.

__Title IX Responsibilities__

As a member of the Kenyon College faculty, I am concerned about the well-being and development of students, and am available to discuss any concerns. However, I want you to know that faculty members are legally obligated to share certain information with the College’s Civil Rights & Title IX Coordinator. This requirement is to ensure your safety and welfare is being addressed. These disclosures include, but are not limited to: reports of discrimination or harassment due to a protected characteristic, including sexual harassment, sexual assault, relational/domestic violence, and stalking.

__Homework__

Homework assignments from the textbook will be given
throughout
the semester. I encourage you to work on as many problems as possible,
including problems which have not been assigned. Subsets of the
homework
assignments will be collected and graded. Working with other students
is
encouraged, but each student __must__ submit her/his own solution
for
problems to be collected.
For more infomation, see the departmental guidelines for collaboration on homework, which I expect you to follow.

__Late Policy__

Your work must be turned in at the __beginning__
of the class period on the assigned due date. **No credit** will be
given for late papers. If for any reason you cannot turn in your paper
on the assigned date, you must contact me or send e-mail to
hartlaub@kenyon.edu
__before__
class.

__Attendance Policy:__

__Quizzes and Activities__

Short quizzes or take home activities will be given occasionally throughout the semester. The goal is to help you comprehend and apply the important concepts and techniques that we have been studying in a relatively short amount of time. In other words these quizzes and activities are designed to help you practice your programming skills.

__Problem Sessions__

During the semester we will have weekly problem sessions which will be conducted by you (the students). These sessions are designed to improve your understanding of statistical ideas and enhance your mathematical reasoning skills by requiring a clear, detailed presentation of the material to your peers. During these sessions, you will be responsible for solving an assigned problem and presenting the solution to the rest of the class. Answering all questions about your solution is a required part of the presentation. Being able to solve problems and being able to present the solutions to a group in a logical and coherent fashion are two different tasks. Our goal is to master both tasks.

__Small Group Projects__

You will be asked to solve practical simulation problems with at least one other member of the class. A written component (paper or poster) and/or an oral presentation to the class will be required. The deadlines and more detailed instructions for the projects will be announced in class and posted on our course web page.

__Final Project__

Each student will conduct a detailed simulation to solve a probability or statistical problem of interest. Ideally, this simulation will be related to a research problem of interest to you. The case studies and student projects in our textbook and other resources serve as great examples for reasonable projects. Summaries of your proposed simulation must be submitted on or before Monday, November 28. Final papers explaining the problem of interest, your analysis, and your conclusions must be submitted on or before the final exam time assigned by the Registrar. The deadline for the 10:10 section is December 15 at 1:30 pm and the deadline for the 11:10 is December 16 at 1:30.

__Grades__

Students are expected to spend an average of nine hours per week preparing for this course outside of direct instruction in class. Your course grade will be based on your overall percentage. The categories used to determine your overall percentage and their respective weights are listed below.

Homework | 20% |

Small Group Projects | 20% |

Quizzes and Activities | 20% |

Problem Sessions | 15% |

Final Project | 25% |

Class participation will be used to help make borderline decisions.

__Course Outline__

- Chapter 1: Prologue: Why data science?
- Chapter 2: Data Visualization
- Chapter 3: A grammar for graphics
- Chapter 4: Data wrangling on one table
- Chapter 5: Data wrangling on multiple tables
- Chapter 6: Tidy data
- Chapter 7: Iteration
- Chapter 9: Statistical foundations
- Supplemental material: Discrete distributions
- Supplemental material: Continuous distributions
- Chapter 10: Predictive modeling (possible group project)
- Chapter 11: Supervised Learning (possible group project)
- Chatper 12: Unsupervised Learning (possible goup project)
- Chapter 13: Simulation
- Supplemental material: Monte Carlo power studies
- Chatper 14: Dynamic and customized data graphics (possible group project)
- Chatper 15: Database querying using SQL (possible group project)
- Chapter 17: Working with geospatial data (possible group project)
- Chapter 18: Geospatial computations (possible group project)
- Chapter 19: Text as data (possible group project)
- Chapter 20: Network science (possible group project)
- Chapter 21: Epilogue: Towards "big data"
- Case Studies
- Student Projects