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Hands-On Exploratory Data Analysis with R

You're reading from  Hands-On Exploratory Data Analysis with R

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Pages 266 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Radhika Datar Radhika Datar
Profile icon Radhika Datar
LinkedIn
Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce.
See other products by Radhika Datar
Harish Garg Harish Garg
Profile icon Harish Garg
LinkedIn
Harish Garg is a Principal Software Developer, author, and co-founder of a software development and training company, Bignumworks. Harish has more than 19 years of experience in a wide variety of technologies, including blockchain, data science and enterprise software. During this time, he has worked for companies such as McAfee, Intel, etc.
See other products by Harish Garg
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Table of Contents (17) Chapters

Preface 1. Section 1: Setting Up Data Analysis Environment
2. Setting Up Our Data Analysis Environment 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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Authors (2)

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Profile icon Radhika Datar
LinkedIn
Radhika Datar has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce.
See other products by Radhika Datar
Profile icon Harish Garg
LinkedIn
Harish Garg is a Principal Software Developer, author, and co-founder of a software development and training company, Bignumworks. Harish has more than 19 years of experience in a wide variety of technologies, including blockchain, data science and enterprise software. During this time, he has worked for companies such as McAfee, Intel, etc.
See other products by Harish Garg
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