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

Exploring multiple variables simultaneously

All right. You have arrived at the last section of exploratory data analysis. Now you will expand your exploration to multiple variables at once. Typical datasets have many variables, but a bivariate analysis limits you to pairwise comparisons. Exploring five variables, two at a time creates 10 pairs, 10 variables create 45, 20 variables create 190, 40 variables create 780, and so on. The impact on workflow is nearly exponential, as shown in the following diagram:

As the number of features (variables) in your dataset grows, your strategy for exploratory data analysis must scale along with your data. Your knowledge of bivariate exploratory data analysis provides you the following two benefits:

  • You have the foundations for exploring multiple variables simultaneously

  • You can use bivariate analysis to further explore any interesting pairs

You will still use the four-question approach of Look-Relationships-Correlation-Significance.


The first question...