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Ten simple rules for finding and selecting R packages.

Caroline J Wendt1,2, G Brooke Anderson3

  • 1Department of Statistics, Colorado State University, Fort Collins, Colorado, United States of America.

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This summary is machine-generated.

This guide presents 10 rules for R users to find and select the best data science packages. Learn how to evaluate package quality, community support, and utility for your specific analytics needs.

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Area of Science:

  • Data Science
  • Statistical Computing
  • Software Engineering

Background:

  • R is a popular software environment for data analytics and statistical computing.
  • R packages significantly enhance its functionality and address data science challenges.
  • Effective package selection is crucial for efficient data analysis.

Purpose of the Study:

  • To provide a systematic approach for R users to discover and evaluate packages.
  • To offer 10 practical rules for selecting the most suitable R packages for data science tasks.
  • To empower users with strategies for assessing package quality, support, and development.

Main Methods:

  • A framework of 10 rules is presented for package discovery and evaluation.
  • Guidance on defining search criteria based on user purpose.
  • Best practices for collecting and assessing R package options are detailed.

Main Results:

  • Users will learn to navigate package profiles and online resources effectively.
  • Methods for evaluating community assessment, popularity, and utility are outlined.
  • Techniques for investigating package development and code are introduced.

Conclusions:

  • Adhering to these rules ensures informed R package selection.
  • This approach enhances confidence in chosen packages and their long-term viability.
  • Users can optimize their data science workflows through strategic R package utilization.