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Advanced R pp 159-179 | Cite as

Other Tools for Data Management

  • Matt Wiley
  • Joshua F. Wiley
Chapter

Abstract

Comparing data frames and data tables leads to an interesting question. What if there were more types of data? Particularly, what if there were different ways to store data that are all, at their heart, tables of some sort? In addition to data frames or tables, there are many ways to store data, many of which are just tables. The idea behind dplyr (Wickham and Francois, 2015) is that regardless of what the data back end might be, our experience should be the same. To allow this, dplyr implements generic functions for common data management tasks. For each of these generic functions, specific methods are written that translate the generic operation into whatever code or language is required for a specific back end. Using a layer of abstraction ensures that users get a consistent experience, regardless of the specific data format, or back end, being used. It also makes dplyr extensible, in that support for a new format can be added by simply writing additional functions or methods. The user experience need not change. For this chapter, our checkpoint header needs to have both the tibble and the dplyr packages installed and added:

Keywords

Data Management Function Call Data Frame Character String Formal Argument 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Matt Wiley and Joshua F. Wiley 2016

Authors and Affiliations

  • Matt Wiley
    • 1
  • Joshua F. Wiley
    • 1
  1. 1.Elkhart Group Ltd. & Victoria CollegeColumbia CityUSA

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