R Web Scraping Quick Start Guide

More Information
Learn
  • Write and create regEX rules
  • Write XPath rules to query your data
  • Learn how web scraping methods work
  • Use rvest to crawl web pages
  • Store data retrieved from the web
  • Learn the key uses of Rselenium to scrape data
About

Web scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming.

You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules.

We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them.

Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R.

Features
  • Techniques, tools and frameworks for web scraping with R
  • Scrape data effortlessly from a variety of websites
  • Learn how to selectively choose the data to scrape, and build your dataset
Page Count 114
Course Length 3 hours 25 minutes
ISBN 9781789138733
Date Of Publication 31 Oct 2018

Authors

Olgun Aydin

Olgun Aydin is a PhD candidate at the Department of Statistics at Mimar Sinan University, and is studying deep learning for his thesis. He also works as a data scientist.

Olgun is familiar with big data technologies, such as Hadoop and Spark, and is a very big fan of R. He has already published academic papers about the application of statistics, machine learning, and deep learning.

He loves statistics, and loves to investigate new methods and share his experience with other people.