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Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining by Dominic Nyhuis, Peter Meissner, Christian Rubba, Simon Munzert

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Preface

The rapid growth of the World Wide Web over the past two decades tremendously changed the way we share, collect, and publish data. Firms, public institutions, and private users provide every imaginable type of information and new channels of communication generate vast amounts of data on human behavior. What was once a fundamental problem for the social sciences—the scarcity and inaccessibility of observations—is quickly turning into an abundance of data. This turn of events does not come without problems. For example, traditional techniques for collecting and analyzing data may no longer suffice to overcome the tangled masses of data. One consequence of the need to make sense of such data has been the inception of “data scientists,” who sift through data and are greatly sought after by researchers and businesses alike.

Along with the triumphant entry of the World Wide Web, we have witnessed a second trend, the increasing popularity and power of open-source software like R. For quantitative social scientists, R is among the most important statistical software. It is growing rapidly due to an active community that constantly publishes new packages. Yet, R is more than a free statistics suite. It also incorporates interfaces to many other programming languages and software solutions, thus greatly simplifying work with data from various sources.

On a personal note, we can say the following about our work with social scientific data:

  • our financial resources are sparse; ...

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