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R Data Mining

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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Length 442 pages
Edition 1st Edition
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Author (1):
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Andrea Cirillo Andrea Cirillo
Author Profile Icon Andrea Cirillo
Andrea Cirillo
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Table of Contents (16) Chapters Close

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:

  • The pattern to look for, through the argument pattern
  • The replacement to put where the pattern is found...
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