Syllabus

Instructor

Course details

  • Monday, Wednesday
  • August 25 – October 13, 2021
  • 10:30am – 11:50am
  • One Capitol Square, Room 305. Zoom recordings will be available on Canvas

Contacting me

E-mail is preferred. I will try to respond to all course-related e-mails within 1 business day.

Course Description

This is a first-year graduate level course in biostatistical computing. This course is an introduction to R programming. It covers R fundamentals and statistical functions, R packages, data management/manipulation and plotting using Tidyverse, reproducible research with GitHub, interactive apps with Shiny. No knowledge of calculus/algebra is required although some statistical operations will be discussed.

Topics

  • R/RStudio
  • Vector/matrix operations
  • Probability distributions and random number generation
  • Statistical functions
  • RMarkdown, GitHub
  • Functions, Packages
  • Data management, tidyverse
  • R graphics, data visualization
  • Interactive web apps with Shiny

Prerequisites

  • No formal course requirements, but basic knowledge of the following will help
    • Basic linear algebra: vectors, matrices, determinants
    • Simple calculus: derivatives, integrals, gradients
    • Some probability theory: probability, random variables, distributions
    • Basic statistics knowledge: descriptive statistics, estimators.
    • (Linear) modeling
  • Hardware
    • A laptop, Mac or Linux OSs are recommended.

Learning Objectives

  1. Learn advanced R programming and reproducible research practices
  2. Understand principles and tools for R data analysis and visualization
  3. Implement reproducible analysis reports and presentations

About the class

This course is a part of the 3-credit hour course. The class will be conducted in person and include lecture and coding parts. Course material will be publicly available. The syllabus is subject to change.

Attendance

Attendance is not checked, but students are responsible for all assignments and announcements made in class.

Homework

Homework will consist of problem sets for material covered. Files should be named, e.g., LASTNAME-FIRSTNAME-HW01.Rmd. Homework due date is one week after the lecture. Late homework will NOT be accepted unless permission was given by the instructor. Assignments that are not well organized/documented will receive no credit. Working in groups of two is allowed. However, you must do the final writing-up of solutions entirely by yourself. Any two assignments which are word-for-word exactly the same or highly similar in coding style will both receive zero credit. There will be no extra credit projects available.

Final project

  • A take-home final project
  • Final project should be submitted as a fully reproducible GitHub repository
  • The due date is to be announced.

Grading Policy

  • Each homework and the final project will be graded on the scale 0-10, 10 points being the best
  • Total homework grade possible - 100 points
    • Missed deadline - minus 3 points for each missed date
    • Missed deadline and not submitted later - 0 points

Homework and final project grades will be averaged and scaled to the 0-100 range.

Standard A-F grading system will be applied:

  • A: 90-100
  • B: 80-89
  • C: 70-79
  • D: 60-69
  • F: 0-59

Diversity and inclusivity

A primary goal of this course is to be inclusive and of value to the largest number of contributors, with the most varied and diverse backgrounds possible. All participants in this course are encouraged to help to provide a friendly, safe and welcoming environment for all, regardless of age, gender, gender identity or expression, culture, ethnicity, language, national origin, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, and technical ability.

Policies and resources

Students should visit http://go.vcu.edu/syllabus and review all syllabus statement information. The full university syllabus statement includes information on safety, registration, the VCU Honor Code, student conduct, withdrawal and more

VCU Honor System

Observe the VCU Honor Pledge in any class- and homework activities

Student Code of Conduct

Guidelines for a productive return to VCU

The Office of Graduate Education

Keep on Teaching and Keep on Learning

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