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.

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Introduction to R for Business Intelligence
Jay Gendron
Aug, 2016 | 228 pages
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Table of Contents (19 chapters)
Introduction to R for Business Intelligence
Introduction to R for Business Intelligence
Credits
Credits
About the Author
About the Author
Acknowledgement
Acknowledgement
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Extract, Transform, and Load
Extract, Transform, and Load
Understanding big data in BI analytics
Extracting data from sources
Transforming data to fit analytic needs
Loading data into business systems for analysis
Summary
2
Data Cleaning
Data Cleaning
Summarizing your data for inspection
Finding and fixing flawed data
Converting inputs to data types suitable for analysis
Adapting string variables to a standard
Summary
3
Exploratory Data Analysis
Exploratory Data Analysis
Understanding exploratory data analysis
Analyzing a single data variable
Analyzing two variables together
Exploring multiple variables simultaneously
Summary
4
Linear Regression for Business
Linear Regression for Business
Understanding linear regression
Checking model assumptions
Using a simple linear regression
Refining data for simple linear regression
Introducing multiple linear regression
Summary
5
Data Mining with Cluster Analysis
Data Mining with Cluster Analysis
Explaining clustering analysis
Partitioning using k-means clustering
Clustering using hierarchical techniques
Summary
6
Time Series Analysis
Time Series Analysis
Analyzing time series data with linear regression
Introducing key elements of time series analysis
Building ARIMA time series models
Summary
7
Visualizing the Datas Story
Visualizing the Datas Story
Visualizing data
Plotting with ggplot2
Geo-mapping using Leaflet
Creating interactive graphics using rCharts
Summary
8
Web Dashboards with Shiny
Web Dashboards with Shiny
Creating a basic Shiny app
Creating a marketing-campaign Shiny app
Deploying your Shiny app
Summary
References
References
Other Helpful R Functions
Other Helpful R Functions
Chapter 1 - Extract, Transform, and Load
Chapter 2 - Data Cleaning
R Packages Used in the Book
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations
R Code for Supporting Market Segment Business Case Calculations

Building ARIMA time series models

The term ARIMA is made up of the letters that represent a modeling approach for time series data. ARIMA models contain the following three elements:

  • AR: Auto regressive, specified with p or P

  • I: Integrated (differencing), specified with d or D

  • MA: Moving average, specified with q or Q

Auto regressive means that earlier lagged points in the data influence later points in the sequence. This creates a dependence condition. The type of AR model chosen is based on how many steps away (lags) the points in the past affect the points in the future. Data that has a greater lingering effect on future points has a higher lag. The higher the lag, the higher the AR number. You will see models referred to as AR(1), AR(2), and so forth to represent an autoregressive model of the number of p lags specified in the parentheses.

Integrated refers to differencing that you learned earlier. The d value represents the number of differences used in the model. It is typically...

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