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Hands-On Exploratory Data Analysis with R

You're reading from   Hands-On Exploratory Data Analysis with R Become an expert in exploratory data analysis using R packages

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Length 266 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Radhika Datar Radhika Datar
Author Profile Icon Radhika Datar
Radhika Datar
Harish Garg Harish Garg
Author Profile Icon Harish Garg
Harish Garg
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment FREE CHAPTER 3. Importing Diverse Datasets 4. Examining, Cleaning, and Filtering 5. Visualizing Data Graphically with ggplot2 6. Creating Aesthetically Pleasing Reports with knitr and R Markdown 7. Section 2: Univariate, Time Series, and Multivariate Data
8. Univariate and Control Datasets 9. Time Series Datasets 10. Multivariate Datasets 11. Section 3: Multifactor, Optimization, and Regression Data Problems
12. Multi-Factor Datasets 13. Handling Optimization and Regression Data Problems 14. Section 4: Conclusions
15. Next Steps 16. Other Books You May Enjoy

Getting data into R by scraping the web using the rvest package

In this section, we will focus on web scraping and how to implement it using the rvest package.

Web scraping is the procedure of converting unstructured data into a structured format. Structured data can be easily accessed and used. We will use R for scraping the data of most popular feature films from the IMDb website.

The following steps are implemented to get data into R using the rvest package:

  1. Install the rvest package. It is mandatory to install it, as it does not come as a built-in library:
> install.packages('rvest') 
package 'rvest' successfully unpacked and MD5 sums checked The downloaded binary packages are in C:\Users\Radhika\AppData\Local\Temp\RtmpMvNUA5\downloaded_packages
  1. Include the installed package in R's workspace:
> library(rvest)
  1. Let's start web scraping...
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