Book Image

R Data Science Essentials

Book Image

R Data Science Essentials

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (15 chapters)
R Data Science Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Plotting


Let's plot and then see the sample dataset, sampdata. From the output, it is clear that the number of transactions is around 100 and number of items is around 500. The following code also includes saving the output to a local file in the current working directory:

Dataset

image(sampdata)
dev.copy(png,filename="sampdata.png", width=600, height=875);
dev.off ();

The output of the preceding command is as follows:

In the Adult dataset, the number of transactions is huge; if the number of items were fewer, it would appear as a straight line.

Rules

In order to plot the rule, we need to load the arulesviz package to the R environment. If this package is not already installed, use the install.packages function to install it:

install.packages("arulesViz")
library(arulesViz)

We can plot the rules using the plot function. This plot will have the support and confidence in the x axis and y axis, respectively, and the shading is used to represent the lift. We can also change the representation using...