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

Plotting with ggplot2

Wilkinson (2005) developed The Grammar of Graphics as a way of approaching data visualization by describing them as individual components working together. Wickham (2010) used this grammar to develop the ggplot2 package. In ggplot2, you can create plots by adding each component of the visualization as a layer. In this section, you will recreate a scatterplot from Chapter 4 , Linear Regression for Business that you built using base R graphics. Convert emp_size to a factor to see its effect in visualizing information:

plot_dat <- read.csv("./data/Ch7_marketing.csv") 
plot_dat$emp_size <- cut(plot_dat$employees, breaks = 3, 
          labels = c("Employees: 3 - 6", "7 - 9", "10+")) 
library(ggplot2); library(scales) 
plot <- ggplot(data = plot_dat, aes(x = marketing_total, 
               y = revenues)) 

First, you will use the ggplot() function to create a basic plot object and pass it the plot_dat dataset. Note that the command...