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Hands-On Data Science with R

You're reading from  Hands-On Data Science with R

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Pages 420 pages
Edition 1st Edition
Languages
Authors (4):
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Profile icon Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Profile icon Doug Ortiz
Nataraj Dasgupta Nataraj Dasgupta
Profile icon Nataraj Dasgupta
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Profile icon Ricardo Anjoleto Farias
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Getting Started with Data Science and R 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Retrieving text from the web

There are numerous ways to retrieve text from the web. The previous section used the Hypertext Transfer Protocol (HTTP) through the httr package to retrieve text from the web. A combination of substr() and regexpr() was then used to extract only a small piece of information from it.

This section will show you how to retrieve text from the web using two different packages:

  • rvest: This can easily perform common web scrapping tasks
  • rtweet: It works with Twitter's web API to gather data

There are numerous ways to use data gathered this way. To name a few, it could be used to develop stock trading, marketing strategies, train chatbots, run sentiment analysis, seeks candidates for a job, or phrase click baits. Our final goal in this chapter will be to check which packages are most tweeted by the R community. Before going any further, there is a very...

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