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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (16) Chapters

Preface

1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER

2. A First Primer on Data Mining Analysing Your Bank Account Data

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

Performing network analysis on textual data

One hypothesis we could make is that these companies, or at least a good part of them, are from the same industry. Is that true?

We actually have the data to figure this out. If we look back at our information dataset, we can easily see that some of the records start with the industry token. These are the records reproducing the line related to the customer's industry, which is contained within every customer card.

Let's filter out all the other records to retain only those records that specify the industry of the company:

information %>%
 filter(grepl("industry", text))

This is fine; nonetheless, we still have that industry: token, which is meaningless. Let's remove it by using the gsub() function. This function basically substitutes a pattern with a replacement within a character vector. Therefore, to apply it, you have to specify the following: