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Related Experiment Videos

Comparison of beginning R students' perceptions of peer-made plots created in two plotting systems: a randomized

Leslie Myint1, Aboozar Hadavand2, Leah Jager2

  • 1Department of Mathematics, Statistics, and Computer Science, Macalester College, 1600 Grand Ave, Saint Paul, MN 55105.

Journal of Statistics Education : an International Journal on the Teaching and Learning of Statistics
|March 25, 2021
PubMed
Summary

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Beginning R users

Area of Science:

  • Data Visualization
  • Scientific Communication
  • R Programming

Background:

  • Effective data visualization is crucial for scientific communication.
  • R is a popular programming language for statistical analysis and graphics.
  • Two primary plotting systems in R are base R graphics and the ggplot2 package.

Purpose of the Study:

  • To compare the perceived quality of scientific graphics created by novice R users using base R versus ggplot2.
  • To identify differences in the use of specific plot features between the two systems.
  • To inform educational strategies for teaching data visualization in R.

Main Methods:

  • An empirical study was conducted with students in a data science course.
  • Students were randomized to create bivariate scatterplots and multivariate plots using either base R or ggplot2.
Keywords:
AssessmentData ScienceData VisualizationGraphical DisplaysRRandomized TrialStatistical Perception

Related Experiment Videos

  • Peer evaluation focused on plot clarity, labeling, and overall presentation of scientific relationships.

    • Main Results:

    • Graphics from both systems were rated similarly on many quality aspects.
    • ggplot2 graphics were perceived as slightly clearer, especially for multivariate relationships.
    • Expert analysis revealed differential use of features like trend lines, labels, legends, panels, and color between the systems.

    Conclusions:

    • ggplot2 may offer advantages for novice users in creating clear scientific graphics, particularly for complex data.
    • Understanding feature usage differences can guide educators in addressing common student errors.
    • The study provides insights into the practical application of R plotting systems for scientific visualization.