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



"Most organizations early on in the data-science learning curve spend most of their time assembling data and not analyzing it. Mature data science organizations realize that in order to be successful they must enable their members to access and use all available data—not some of the data, not a subset, not a sample, but all data. A lawyer wouldn’t go to court with only some of the evidence to support their case—they would go with all appropriate evidence. ...The fundamental building block of a successful and mature data science capability is the ability to ask the right types of questions of the data. This is rooted in the understanding of how the business runs... The mature data science organization has a collaborative culture in which the data science team works side by side with the business to solve critical problems using data. ... [it] includes one or more people with the skills of a data artist and a data storyteller. Stories and visualizations are where we make connections between facts. They enable the listener to understand better the context (What?), the why (So what?), and “what will work” in the future (Now what?)."

 --Peter Guerra and Kirk Borne in Ten Signs of Data Science Maturity (2016)

Guerra and Borne (2016) highlight the importance of a diverse and inquisitive team approach to data science. Business intelligence also benefits from this approach. Introduction to R for Business Intelligence gives you a way to explore the world of business intelligence through the eyes of an analyst working in a successful and growing startup company. You will learn R through use cases supporting different business functions.

This book provides data-driven and analytically focused approaches to help you answer business questions in operations, marketing, and finance—a diverse perspective. You will also see how asking the right type of questions and developing the stories and visualizations helps you connect the dots between the data and the business.


Enjoy the journey as you code solutions to business intelligence problems using R.

 --Jay Gendron

What this book covers

This book is written in three parts that represent a natural flow in the data science process: data preparation, analysis, and presentation of results.

In Part 1, you will learn about extracting data from different sources and cleaning that data.

Chapter 1, Extract, Transform, and Load, begins your journey with the ETL process by extracting data from multiple sources, transforming the data to fit analysis plans, and loading the transformed data into business systems for analysis.

Chapter 2, Data Cleaning, leads you through a four-step cleaning process applicable to many types of datasets. You will learn how to summarize, fix, convert, and adapt data in preparation for your analysis process.

In Part 2, you will look at data exploration, predictive models, and cluster analysis for business intelligence, as well as how to forecast time series data.

Chapter 3, Exploratory Data Analysis, continues the adventure by exploring an unfamiliar dataset using a structured approach. This will provide you insights about features important for shaping further analysis.

Chapter 4, Linear Regression for Business, (co-authored with Rick Jones) walks you through a classic predictive analysis approach for single and multiple features. It also reinforces key assumptions the data should meet in order to use this analytic technique.

Chapter 5, Data Mining with Cluster Analysis, presents two methods of unsupervised learning with examples using k-means and hierarchical clustering. These two data mining techniques allow you to unearth patterns hidden in the data.

Chapter 6, Time Series Analysis, introduces a difficult topic not often taught in data science courses. You will explore non-machine learning methods to forecast future values with data that are dependent on past observations.

Finally, in Part 3 you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.

Chapter 7, Visualizing the Data’s Story, explores more than just techniques to interactively visualize your results. You will learn how your audience cognitively interprets data through color, shape, and position.

Chapter 8, Web Dashboards with Shiny, (authored by Steven Mortimer) culminates your adventure by explaining how to create a web-based, business intelligence application using R Shiny.

There are a number of appendices providing additional information and code.

Appendix A, References, provides a list of the references used throughout the book.

Appendix B, Other Helpful R Functions, provides a list of functions and their descriptions. These are useful in data science projects and this appendix allows you to explore how they may help you work with data.

Appendix C, R Packages Used in the Book, gives a complete list of all the R packages used in each chapter. This allows you to install all the packages you will need by referring to a single list. It also contains instructions on installing packages.

Appendix D, R Code for Supporting Market Segment Business Case Calculations, gives a detailed code base for computing the geo-based information used in Chapter 5, Data Mining with Cluster Analysis.

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 when developing business strategy. Along the way, you will find helpful tips about R and business intelligence.

What you need for this book

You need the R statistical package, available free as an open source download from CRAN ( This book used R version 3.3.1 for Windows. You will get the same results using the macOS or Linux software.

Optionally, it is also recommended that you use RStudio Desktop, an open source user interface that works with R ( This book used version 0.99.903 for Windows. It is also available for macOS and Linux.

Lastly, you will need to install a variety of R packages to perform the various analyses. All these are all freely available using the install.packages() function within R. You can get a refresher on installing packages in Appendix C, R Packages Used in the Book.

This book provides code using the R statistical programming language. Perhaps it has been a while since you last used or learned R. If that is the case, then you may find the following material quite useful. It provides a listing of R skills that you should know before working on the use cases provided in each of the chapters.

Video links (Peng, 2015; Peng, 2014; LawrenceStats, 2016; Peng, 2012) for each skill provide an explanation of techniques that you may want to review before continuing:

Who this book is for

This book is for business analysts who want to increase their skills in R and learn analytic approaches to business problems. Data science professionals will benefit from this book as they apply their R skills to business problems and learn the language of business.


In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "You can load the Bike Sharing data file into the R environment by using the read.csv() function."

A block of code is set as follows:

bike$holiday <- factor(bike$holiday, levels = c(0, 1),
                       labels = c("no", "yes"))
bike$workingday <- factor(bike$workingday, levels = c(0, 1),
                          labels = c("no", "yes"))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

query <- "SELECT * FROM marketing" 
bike <- sqlQuery(connection, query) 

Any command-line input or output is written as follows:

[1] TRUE

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "RStudio will automatically create a ui.R and server.R file, if you create a new project and choose New Directory and Shiny Web Application as the type."


Warnings or important notes appear in a box like this.


Tips and tricks appear like this.

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