By Simon Munzert, Christian Rubba, Dominic Nyhuis, Peter Meiner
A fingers on consultant to net scraping and textual content mining for either rookies and skilled clients of R Introduces primary recommendations of the most structure of the internet and databases and covers HTTP, HTML, XML, JSON, SQL.
Provides uncomplicated options to question net files and information units (XPath and general expressions). an in depth set of workouts are awarded to lead the reader via each one method.
Explores either supervised and unsupervised strategies in addition to complicated options similar to facts scraping and textual content administration. Case reviews are featured all through besides examples for every procedure provided. R code and strategies to routines featured within the e-book are supplied on a helping site.
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Additional resources for Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
Is the information systematically flawed? Although this is not a book on research design or advanced statistical methods to tackle noise in data, we want to emphasize these questions before we start harvesting gigabytes of information. When you look at online data, you have to keep its origins in mind. Information can be firsthand, like posts on Twitter or secondhand data that have been copied from an offline source, or even scraped from elsewhere. There may be situations where you are unable to retrace the source of your data.
1 What is parsing? Before showing the application of a parser, let us think about why we need to parse the contents of marked up web documents such as HTML compared to merely reading them into an R session. The difference between reading and parsing is not just a semantic one. Instead, reading functions differ from parsing functions in that the former do not care to understand the formal grammar that underlies HTML but merely recognize the sequence of symbols included in the HTML file. To see that, let us employ base R’s readLines() function, which loads the content of an HTML file.
Parsing HTML occurs at both steps—by the browser to display HTML content nicely, and also by parsers in R to construct useful representations of HTML documents in our programming environment. In the remainder of this chapter we begin by motivating the use of parsers and then discuss some of the problems inherent in the process as well as their solutions. 1 What is parsing? Before showing the application of a parser, let us think about why we need to parse the contents of marked up web documents such as HTML compared to merely reading them into an R session.