Automated Data Collection with R: A Practical Guide to Web by Simon Munzert

By Simon Munzert

A fingers on advisor to net scraping and textual content mining for either newbies and skilled clients of R

  • Introduces basic strategies of the most structure of the net and databases and covers HTTP, HTML, XML, JSON, SQL.
  • Provides easy ideas to question internet files and information units (XPath and general expressions).
  • An vast set of workouts are presented to consultant the reader via each one technique.
  • Explores either supervised and unsupervised strategies in addition to complicated ideas equivalent to information scraping and textual content management.
  • Case reports are featured all through in addition to examples for every method presented.
  • R code and solutions to routines featured in the booklet are supplied on a assisting website.

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Extra resources for Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

Example text

We recommend reading Chapter 8 in either case, as text manipulation basics are also a fundamental technique for web scraping purposes. r If you are a teacher, you might want to use the book as basic or supplementary literature. We provide a set of exercises after most of the chapters in Parts I and II for this purpose. com for about half the exercises, so you can assign them as homework or use them for test questions. SQL 14 AUTOMATED DATA COLLECTION WITH R For all others, we hope you will find the structure useful as well.

See below for some examples: 1 2 3 4

heading of level 1 -- this will be BIG

heading of level 2 -- this will be big

... 7 Listing content with

    ,

      , and

      Several tags exist to list content. They are used depending on whether they wrap around an ordered list (

        ), an unordered list (ul), or a description list (

        ). The former two tags make use of nested

      1. elements to define list items, while the latter needs two further elements:
        for keyword and

        for its description.

        If so, does it make sense to use data from the Web? We think the answer is yes. Regarding the transparency of the data generation, web data do not differ much from other secondary sources. Consider Wikipedia as a popular example. It has often been debated whether it is legitimate to quote the online encyclopedia for scientific and journalistic purposes. The same concerns are equally valid if one cares to use data from Wikipedia tables or texts for analysis. It has been shown that Wikipedia’s accuracy varies.

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