Book Image

Introduction to R for Business Intelligence

By : Jay Gendron
Book Image

Introduction to R for Business Intelligence

By: Jay Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

Adapting string variables to a standard

You are almost done with the basics of data cleaning. At this point in the process, you have summarized, fixed, and converted your input data. This means that it is time for you to accomplish the fourth SFCA step, adapting your data to a standard.

The term standard has many possible meanings. It may be that an R package will set a standard for you. In other cases, you may wish to establish one. For instance, notice in the previous data view that the sources variable is a character data type. You will see that it contains the advertising source where the customer learned about bike sharing. Leaving this as a character data type seems reasonable, but R cannot group character items to summarize them in analysis.

Your implied standard is that sources should be a categorical variable. What might happen if you use the as.factor(bike$sources) function? This will convert the data, but before you do that, you should consider a couple of questions:

  • How many unique...