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Working with Large and Complex Datasets

In: Using R for Biostatistics

Author

Listed:
  • Thomas W. MacFarland

    (Nova Southeastern University Fort Lauderdale, Senior Research Associate, Office of Institutional Effectiveness
    Nova Southeastern University Fort Lauderdale, Associate Professor, College of Computing and Engineering)

  • Jan M. Yates

    (Nova Southeastern University Fort Lauderdale, Professor Emerita, Abraham S. Fischler College of Education)

Abstract

The purpose of this lesson on working with large and complex datasets is to provide a realistic demonstration of how R is used for challenging analyses, challenging because the dataset is fairly large, challenging because there are many variables requiring attention, challenging because there are missing data, challenging because certain subjects require special accommodation, challenging because selected data in the original dataset need to be put into filtered subsets, etc. Fortunately, R can accommodate these challenges, as demonstrated in this lesson and the accompanying addenda. Give special attention to the way different Boolean-type selection processes are used to address focused inquiries of importance for subjects in selected breakout groups, as opposed to large-scale analyses against the entire dataset.

Suggested Citation

  • Thomas W. MacFarland & Jan M. Yates, 2021. "Working with Large and Complex Datasets," Springer Books, in: Using R for Biostatistics, chapter 0, pages 585-882, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-62404-0_8
    DOI: 10.1007/978-3-030-62404-0_8
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