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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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

Chapter 2. Data Cleaning

Clean data is an essential element of good data analysis. Poor data quality is a primary reason for problems in business intelligence analysis. Data cleaning is the process of transforming raw data into usable data. Cleaning data, checking quality, and standardizing data types accounts for the majority of an analytic project schedule.

Anthony Goldbloom, the CEO of Kaggle, said: Eighty percent of data science is cleaning data and the other twenty percent is complaining about cleaning data (personal communication, February 14, 2016).

This chapter covers four key topics using some of the newer packages available within the R environment:

  • Summarizing your data for inspection

  • Finding and fixing flawed data

  • Converting inputs to data types suitable for analysis

  • Adapting string variables to a standard

Business analysts spend a lot of time cleaning data before moving to the analysis phase. Data cleaning does not have to be a dreaded task. This chapter provides business analysts...

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