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

Carl H. Lindner College of Business

Introduction to "R"

October 19 & 20

Course Description: R is one of the fastest growing programimng languages and tool of choice for analysts and data scientists.  In part, R owes its popularity to its open source distribution and massive user community.  In this course, we will help both new and existing R users master the basics of R.   There will be an emphasis on using hands on exercises and real world datasets.

Upon successfully completing this course, students will:

  • Be up and running with R
  • Understand the different types of data R can work with
  • Understand the different structures in which R holds data
  • Be able Import data into R
  • Perform basic data wrangling activities with R
  • Compute basic descriptive statistics with R
  • Visualize their data with base R and ggplot graphics






Introduction to Python  Sept 14 & 15
Tableau Training Sept 21 & 22
Tableau Training Nov 2 & 3
MS PowerBI Training Nov 16 & 17 -Details coming soon
Analytics In Excel Dec 14 & 15
Text Mining with "R"   Spring 2018

Course Outline

Day One


  • Downloading R & RStudio
  • The R environment
  • Getting help
  • Managing your directory
  • R as a Calculator
  • Simple objects & assignment
  • Vectors
  • Working with packages

 Importing/Exporting Data

  • Built-in data
  • Text files
  • Excel files
  • Scraping online tabular files

 Data Structures

  • Vectors
  • Matrices
  • Lists
  • Data frames

 Data Types

  • Numbers
  • Character strings
  • Factors
  • Dates
  • Logical

 Tidy Data

  • Managing wide & long data
  • Splitting & uniting variables

Day Two

Transforming & Manipulating Data

  • Selecting variables
  • Filtering variables
  • Summarizing
  • Ordering
  • Creating new variables
  • Merging data sets

 Base R Plotting

  • Strip charts
  • Histograms
  • Density plots
  • Box plots
  • Bar charts
  • Dot plots
  • Scatter plot
  • Line charts

 Advanced Plotting with ggplot

  • geoms
  • Overfitting
  • Color, size & shape aesthetics
  • Small multiples (faceting)
  • Scales, axes & legends
  • Themes

 Putting it all together

  • Case study 1
  • Case study 2



Course Fee: $695 includes breakfasts, lunches, refreshments and free parking for both days.

Course Location:
U-Square. Room 352
225 Calhoun Street
Cincinnati. OH 45219   (google map link)

Course Instructor:

Yichen Qin is an assistant professor in the Department of Operations, Business Analytics, and Information Systems in the Lindner College of Business at the University of Cincinnati.  He earned his Ph.D. degree in Applied Mathematics and Statistics from the Johns Hopkins University in 2013.  At UC, he has been teaching both undergraduate and graduate level courses, including Business Analytics, Forecasting and Time Series Methods, Data Analysis Methods.  His research focuses on computational statistics, robust statistics, and mixture models.