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R for Data Science

You're reading from R for Data Science Learn and explore the fundamentals of data science with R

Product type Paperback

Published in Dec 2014

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Dan Toomey

View More author details

Table of Contents (14) Chapters

Preface

1. Data Mining Patterns FREE CHAPTER

2. Data Mining Sequences

3. Text Mining

4. Data Analysis – Regression Analysis

5. Data Analysis – Correlation

6. Data Analysis – Clustering

7. Data Visualization – R Graphics

8. Data Visualization – Plotting

9. Data Visualization – 3D

10. Machine Learning in Action

11. Predicting Events with Machine Learning

12. Supervised and Unsupervised Learning

Index

Questions

Factual

How will you exclude white people from the eclat results?
Describe the different transitions that occur in the sequence plots.
In the TraMineRmvad data summary, there are marked differences in regional responses. Can you guess why?

When, how, and why?

Describe what is going on with the few outliers in seqiplot. There are several data points that don't seem to fit.
What would be going on in the data presented in seqHtplot when the line curves upward?
How will you apply the sequence finding routines discussed?

Challenges

Determine what the item numbers represent in the market basket data.
The TraMineR package includes much more than what was covered in this chapter. You could investigate the additional functionality further.

The rest of the chapter is locked

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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