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

Chapter 3. Exploratory Data Analysis

One way to learn new things is through discovery. Exploratory data analysis is a term attributed to the statistician John Tukey in a book of the same name (Tukey, 1977). Exploratory data analysis means examining a dataset to discover its underlying characteristics with an emphasis on visualization. It helps you during analysis design to determine if you should gather more data, suggest hypotheses to test, and identify models to develop. In this chapter, we will cover the following four topics related to exploratory data analysis:

  • Understanding exploratory data analysis

  • Analyzing a single data variable

  • Analyzing two variables together

  • Exploring multiple variables simultaneously

You will learn common techniques that statisticians and analysts use to characterize data. These include tabular and graphical methods to explore the dataset. There are many interesting things to discover in a dataset, but in business or science you are exploring to determine the aspects...