Abstract
This chapter introduces the dplyr suite of functions.
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- 1.
While it is possible to download all of the available packages for R, doing so would certainly take a long time and would clog up your installation with way too many irrelevant features. The fact is that R is a multipurpose platform used in a huge range of disciplines including: bio-statistics, network analysis, economics, data-mining, geography, and hundreds of other disciplines and sub-disciplines. This diversity in the user community is one of the great advantages of R and of open-source software more generally. The diversity of options, however, can be daunting to the novice user, and, to make matters even more unnerving, the online R user community is notoriously specialized and siloed and can appear rather impatient when it comes to newbies asking simple questions. Having said that, the online community is also an incredible resource that you must not ignore. Because the packages developed for R are developed by programmers with at least some amount of ad hoc motivation behind their coding, the packages are frequently weak on documentation and generally assume some, if not extensive, familiarity with the academic discipline of the programmer (even if the package is one with applications that cross disciplinary boundaries).
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L. Jockers, M., Thalken, R. (2020). Introduction to dplyr . In: Text Analysis with R. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-39643-5_11
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DOI: https://doi.org/10.1007/978-3-030-39643-5_11
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