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.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
About the Author
About the Reviewers
R Packages Used in the Book
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...