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

Carl H. Lindner College of Business

Introduction to "R"

This is the first course in the R Data Science Series: Date TBD

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 to 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

 

 

Future Training Fall 2018

Analytics in Excel Aug 23 & 24
Tableau Training Aug 23 & 24
MS Power BI  Oct 25 & 26
Python for Data Science Nov 29 & 30
Data Visualization TBD
Tableau Training Dec 6 & 7

Course Outline

Day One

Introduction

  • 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: $795 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:

Brad Boehmke  R Instructor

Brad Boehmke, PhD, is the Director of Data Science at 84.51°, Professor at three universities, author of the Data Wrangling with R book, and creator of multiple R open source packages and data science short courses. He focuses on developing algorithmic processes, solutions, and tools that enable 84.51° and its analysts to efficiently extract insights from data and provide solution alternatives to decision-makers. He has a wide analytic skill set covering descriptive, predictive, and prescriptive analytic capabilities applied across multiple domains including retail, healthcare, cyber intelligence, finance, Department of Defense, and aerospace. Summary of his works is available online at bradleyboehmke.github.io.