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

Visualizing the connectivity


We can visualize the hierarchical cluster generated using the plot function. To this function, we will pass the output of the hclust function and a few other graphical parameters related to the plotting. Let's see how the plot would look:

plot(cluster, cex=0.5, cex.lab=1, cex.axis=1, cex.main=1, cex.sub=1, which.plots=2)

The output is as follows:

We can further divide the hierarchical cluster into different groups using the rect.hclust function. To this function, we pass the hierarchical cluster output, number of groups to be formed, and the border color to partition the output as parameters:

rect.hclust(cluster, k=5,
 border="red")

The output is as follows: