Introduction to R for Business Intelligence

Learn how to leverage the power of R for Business Intelligence
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Introduction to R for Business Intelligence

Jay Gendron

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Learn how to leverage the power of R for Business Intelligence
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Book Details

ISBN 139781785280252
Paperback228 pages

Book Description

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

Chapter 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
Chapter 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
Chapter 3: Exploratory Data Analysis
Understanding exploratory data analysis
Analyzing a single data variable
Analyzing two variables together
Exploring multiple variables simultaneously
Summary
Chapter 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
Chapter 5: Data Mining with Cluster Analysis
Explaining clustering analysis
Partitioning using k-means clustering
Clustering using hierarchical techniques
Summary
Chapter 6: Time Series Analysis
Analyzing time series data with linear regression
Introducing key elements of time series analysis
Building ARIMA time series models
Summary
Chapter 7: Visualizing the Datas Story
Visualizing data
Plotting with ggplot2
Geo-mapping using Leaflet
Creating interactive graphics using rCharts
Summary
Chapter 8: Web Dashboards with Shiny
Creating a basic Shiny app
Creating a marketing-campaign Shiny app
Deploying your Shiny app
Summary

What You Will Learn

  • Extract, clean, and transform data
  • Validate the quality of the data and variables in datasets
  • Learn exploratory data analysis
  • Build regression models
  • Implement popular data-mining algorithms
  • Visualize results using popular graphs
  • Publish the results as a dashboard through Interactive Web Application frameworks

Authors

Table of Contents

Chapter 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
Chapter 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
Chapter 3: Exploratory Data Analysis
Understanding exploratory data analysis
Analyzing a single data variable
Analyzing two variables together
Exploring multiple variables simultaneously
Summary
Chapter 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
Chapter 5: Data Mining with Cluster Analysis
Explaining clustering analysis
Partitioning using k-means clustering
Clustering using hierarchical techniques
Summary
Chapter 6: Time Series Analysis
Analyzing time series data with linear regression
Introducing key elements of time series analysis
Building ARIMA time series models
Summary
Chapter 7: Visualizing the Datas Story
Visualizing data
Plotting with ggplot2
Geo-mapping using Leaflet
Creating interactive graphics using rCharts
Summary
Chapter 8: Web Dashboards with Shiny
Creating a basic Shiny app
Creating a marketing-campaign Shiny app
Deploying your Shiny app
Summary

Book Details

ISBN 139781785280252
Paperback228 pages
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