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Mastering Data analysis with R

You're reading from Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

Product type Paperback

Published in Sep 2015

Publisher Packt

ISBN-13 9781783982028

Length 396 pages

Edition 1st Edition

Languages

R

Concepts

Data Analysis

Author (1):

Gergely Dar√≥czi

View More author details

Table of Contents (17) Chapters

Preface

1. Hello, Data!

2. Getting Data from the Web FREE CHAPTER

3. Filtering and Summarizing Data

4. Restructuring Data

5. Building Models (authored by Renata Nemeth and Gergely Toth)

6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)

7. Unstructured Data

8. Polishing Data

9. From Big to Small Data

10. Classification and Clustering

11. Social Network Analysis of the R Ecosystem

12. Analyzing Time-series

13. Data Around Us

14. Analyzing the R Community

A. References

Index

Summary

In the past few pages, I have tried to cover a variety of data science and R programming topics, although many important methods and questions were not addressed due to page limitation. To this end, I've compiled a short reading list in the References chapter of the book. And don't forget: now it's your turn to practice everything you learned in the previous chapters. I wish you a lot of fun and success in this journey!

And once again, thanks for reading this book; I hope you found it useful. If you have any questions, comments, or any kind of feedback, please feel free to get in touch, I'm looking forward to hearing from you!

The rest of the chapter is locked

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Authors (1)

Daróczi

Daróczi

Gergely Darczi is a former assistant professor of statistics and an enthusiastic R user and package developer. He is the founder and CTO of an R-based reporting web application at http://rapporter.net and a PhD candidate in sociology. He is currently working as the lead R developer/research data scientist at https://www.card.com/ in Los Angeles. Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance (both by Packt Publishing) by providing and reviewing the R source code. He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well.

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