Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business

Intermediate "R"


This is the second course in the "R" series and will build on the material from "Introduction to "R".   Attendance at the introductory course is not required for those with  practical experience in a professional setting. For those with little prior experience, please contact Larry Porter for resources to prepare for this class.  This course will cover the application of R for the entire data science workflow – data acquisition, wrangling, visualization, analytic modeling, and communication.  There will be an emphasis on using hands on exercises and real world datasets.

Course Fee:  $750 includes breakfasts, lunches, refreshments and free parking for both days.

Course Location:
U-Square. Room 359
225 Calhoun Street
Cincinnati. OH 45219   (google map link)

Upon successfully completing this course, students will:

  • Be able to work in a fully reproducible literate statistical environment
  • Have mastered the data wrangling process to include handling text data and scraping structured and unstructured online data
  • Understand how to minimize code duplication by applying control statements, the apply family of functions, along with developing their own functions
  • Be fluent with exploratory data analyses
  • Understand the analytic modeling process
  • Be able to communicate their analysis through a variety of mediums

Day One

Day Two

Introduction (45 min):

  • Course intro
  • Recap of the basics

Working in a Reproducible Environment (50 min)

  • RStudio projects
  • R Markdown
  • R Notebooks

Wrangling (90 min)

  • Tibbles & Data frames
  • Pipe function
  • tidyr & dplyr
  • Mastering data types & structures

Regular Expressions (60 min)

  • Regex syntax
  • Regex functions
  • Normalizing text

Scraping (60 min)

  • Scraping tabular & spreadsheet files
  • Scraping HTML text
  • Scraping HTML tables
  • Scraping with APIs

Iteration (90 min)

  • Control statements (if, ifelse, for, while, repeat)
  • Apply family (apply, lapply, sapply, tapply)

Functions (90 min)

  • When to write functions
  • Function components
  • Function arguments
  • Scoping rules
  • Concept of lazy evaluation
  • Returning function outputs
  • Handling invalid parameters
  • Sourcing your own functions

Exploratory Data Analysis (60 min)

  • Describing your data visually
  • Describing your data numerically

Modeling Basics (90 min)

  • Simple models
  • Visualizing models
  • Mastering model formulas
  • Interpolation vs. extrapolation

Modeling Building Process (120 min)

  • Case study 1
  • Case study 2
  • Case study 3
  • Managing many models

Communicating Your Results (120 min)

  • Interactive graphics
  • R Markdown reporting
  • Intro to flexdashboards
  • Intro to Shiny




Course Instructor: Brad Boehmke

Brad Boehmke

Hello! I’m a computational economist focused on applying advanced evidence-based analytics to provide decision makers robust understanding of economic behavior, performance, and potential policy impacts across an organization.

I work on empirical econometric research, write data analysis softwareteach people about business analytics and R programming, write books, and create digital projects.

I do my work as an Assistant Professor of Logistics and Supply Chain Management at the Air Force Institute of Technology Department of Operational Sciences and as an Adjunct Assistant Professor of Business Analytics at the University of Cincinnati Lindner College of Business. Previously, I was a Senior Operations Research Analyst with the Air Force and spent many years developing life cycle forecasting, risk and decision analysis models.