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R Programming By Example

You're reading from   R Programming By Example Practical, hands-on projects to help you get started with R

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788292542
Length 470 pages
Edition 1st Edition
Languages
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Authors (2):
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Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to R 2. Understanding Votes with Descriptive Statistics FREE CHAPTER 3. Predicting Votes with Linear Models 4. Simulating Sales Data and Working with Databases 5. Communicating Sales with Visualizations 6. Understanding Reviews with Text Analysis 7. Developing Automatic Presentations 8. Object-Oriented System to Track Cryptocurrencies 9. Implementing an Efficient Simple Moving Average 10. Adding Interactivity with Dashboards 11. Required Packages

Building the corpus with tokenization and data cleaning

The first thing we need to create when working with text data is to extract the tokens that will be used to create our corpus. Simply, these tokens are all the terms found in every text in our data, put together, and removed the ordering or grammatical context. To create them, we use the tokens() function and the related functions from the quanteda package. As you can imagine, our data will not only contain words, but also punctuation marks, numbers, symbols, and other characters like hyphens. Depending on the context of the problem you're working with, you may find it quite useful to remove all of them as we do here. However, keep in mind that in some contexts some of these special characters can be meaningful (for example, the hashtag symbol (#) can be relevant when analyzing Twitter data):

tokens <- tokens(
  ...
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