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

Converting inputs to data types suitable for analysis

Another aspect of data cleaning is investigating the input data and shaping it to conform to your analysis design needs. The third step of the SFCA approach is convert. Specifically, converting the data from one data type to another.

Converting between data types

The R environment stores data in one of the several data types. You will experience five different data types in the Bike Sharing Analysis Project:

Data type




A number having a decimal value



A number without decimals



A string variable



A categorical variable that has a character and integer representation

"ad campaign", "blog": 1,2


A date or time in various formats

2016-02-16 18:56:57 EST

Each data type has different properties consistent with definitions used in disciplines such as computer science, mathematics, and statistics. Some R library packages require input...