Book Image

Introduction to R for Business Intelligence

By : Gendron
Book Image

Introduction to R for Business Intelligence

By: 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
Gendron
Aug, 2016 | 228 pages
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Table of Contents (13 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Extract, Transform, and Load
1. 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
2. Data Cleaning
2. 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
3. Exploratory Data Analysis
3. Exploratory Data Analysis
Understanding exploratory data analysis
Analyzing a single data variable
Analyzing two variables together
Exploring multiple variables simultaneously
Summary
4
4. Linear Regression for Business
4. 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
5. Data Mining with Cluster Analysis
5. Data Mining with Cluster Analysis
Explaining clustering analysis
Partitioning using k-means clustering
Clustering using hierarchical techniques
Summary
6
6. Time Series Analysis
6. Time Series Analysis
Analyzing time series data with linear regression
Introducing key elements of time series analysis
Building ARIMA time series models
Summary
7
7. Visualizing the Datas Story
7. Visualizing the Datas Story
Visualizing data
Plotting with ggplot2
Geo-mapping using Leaflet
Creating interactive graphics using rCharts
Summary
8
8. Web Dashboards with Shiny
8. Web Dashboards with Shiny
Creating a basic Shiny app
Creating a marketing-campaign Shiny app
Deploying your Shiny app
Summary
9
A. References
A. References
10
B. Other Helpful R Functions
B. Other Helpful R Functions
Chapter 1 - Extract, Transform, and Load
Chapter 2 - Data Cleaning
11
C. R Packages Used in the Book
C. R Packages Used in the Book
12
D. R Code for Supporting Market Segment Business Case Calculations
D. 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...

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