By Simon Munzert
A fingers on consultant to net scraping and textual content mining for either novices and skilled clients of R
- Introduces primary thoughts of the most structure of the internet and databases and covers HTTP, HTML, XML, JSON, SQL.
- Provides easy concepts to question net files and knowledge units (XPath and typical expressions).
- An broad set of routines are presented to advisor the reader via every one technique.
- Explores either supervised and unsupervised thoughts in addition to complex ideas akin to facts scraping and textual content management.
- Case experiences are featured all through in addition to examples for every method presented.
- R code and solutions to workouts featured in the booklet are supplied on a aiding website.
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Additional info for Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining
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. While some studies find that Wikipedia is comparable to established encyclopedias (Chesney 2006; Giles 2005; Reavley et al. 2012), others suggest that the quality might, at times, be inferior (Clauson et al. 2008; Leithner et al. 2010; Rector 2008). But how do you know when relying on one specific article? It is always recommended to find a second source and to compare the content.
Costs of collection, compatibility of new sources with existing research, but also very subjective factors like acceptance of the data source by others. Also think about possible ways to validate the quality of your data. Are there other, independent sources that provide similar information so that random cross-checks are possible? In case of secondary data, can you identify the original source and check for transfer errors? 5. Make a decision! Choose the data source that seems most suitable, document your reasons for the decision, and start with the preparations for the collection.