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Learning R Programming

Learning R Programming

By : Ren
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Learning R Programming

Learning R Programming

By: Ren

Overview of this book

R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques.
Table of Contents (16 chapters)
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Analysing HTML code and extracting data

In the previous sections, we learned the basics of HTML, CSS, and XPath. To scrape real-world web pages, the problem now becomesa question of writing the proper CSS or XPath selectors. In this section, we introduce some simple ways to figure out working selectors.

Suppose we want to scrape all available R packages at https://cran.rstudio.com/web/packages/available_packages_by_name.html. The web page looks simple. To figure out the selector expression, right-click on the table and select Inspect Element in the context menu, which should be available in most modern web browsers:

Analysing HTML code and extracting data

Then the inspector panel shows up and we can see the underlying HTML of the web page. In Firefox and Chrome, the selected node is highlighted so it can be located more easily:

Analysing HTML code and extracting data

The HTML contains a unique <table> so we can directly use table to select it and use html_table() to extract it out as a data frame:

page <- read_html("https://cran.rstudio.com/web/packages...
CONTINUE READING
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Learning R Programming
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