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  • Book Overview & Buying Introduction to R for Business Intelligence
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Introduction to R for Business Intelligence

Introduction to R for Business Intelligence

By : Gendron
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Introduction to R for Business Intelligence

Introduction to R for Business Intelligence

By: Gendron
Buy this Book

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 (13 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Extract, Transform, and Load
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1. Extract, Transform, and Load
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Understanding big data in BI analytics
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Extracting data from sources
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Transforming data to fit analytic needs
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Loading data into business systems for analysis
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Summary
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2. Data Cleaning
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2. Data Cleaning
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Summarizing your data for inspection
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Finding and fixing flawed data
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Converting inputs to data types suitable for analysis
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Adapting string variables to a standard
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Summary
3
3. Exploratory Data Analysis
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3. Exploratory Data Analysis
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Understanding exploratory data analysis
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Analyzing a single data variable
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Analyzing two variables together
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Exploring multiple variables simultaneously
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Summary
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4. Linear Regression for Business
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4. Linear Regression for Business
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Understanding linear regression
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Checking model assumptions
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Using a simple linear regression
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Refining data for simple linear regression
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Introducing multiple linear regression
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Summary
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5. Data Mining with Cluster Analysis
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5. Data Mining with Cluster Analysis
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Explaining clustering analysis
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Partitioning using k-means clustering
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Clustering using hierarchical techniques
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Summary
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6. Time Series Analysis
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6. Time Series Analysis
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Analyzing time series data with linear regression
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Introducing key elements of time series analysis
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Building ARIMA time series models
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Summary
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7. Visualizing the Datas Story
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7. Visualizing the Datas Story
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Visualizing data
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Plotting with ggplot2
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Geo-mapping using Leaflet
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Creating interactive graphics using rCharts
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Summary
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8. Web Dashboards with Shiny
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8. Web Dashboards with Shiny
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Creating a basic Shiny app
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Creating a marketing-campaign Shiny app
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Deploying your Shiny app
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Summary
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A. References
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A. References
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B. Other Helpful R Functions
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B. Other Helpful R Functions
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Chapter 1 - Extract, Transform, and Load
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Chapter 2 - Data Cleaning
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C. R Packages Used in the Book
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C. R Packages Used in the Book
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D. R Code for Supporting Market Segment Business Case Calculations
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D. R Code for Supporting Market Segment Business Case Calculations

Summary

Congratulations! You have learned a lot of topics in this chapter. Data cleaning is a very important part of business intelligence analysis. In this chapter, you learned that cleaning data is a four-step process that can be remembered by SFCA (summarize-fix-convert-adapt).

Summarizing the data gives you a big-picture overview and provides a perspective on the data. This shapes your data cleaning strategies. Fixing flawed data can be tedious, but there are common practices to use. Converting data is important to get it in the right data type to support your analysis. Dates and times can be difficult, but tools help with this. Adapting your data to a standard is the key to setting a foundation for a successful data analysis. Standards may be given or you may design one.

Continuing to learn is also important as the packages and methods change frequently. Lastly, the topic of data cleaning is full of interesting ideas that you may find helpful. A recommended resource is An Introduction...

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Programming languages
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Introduction to R for Business Intelligence
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