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Descriptive statistics and visualization of data from the R datasets package with implications for clusterability.

Naomi C Brownstein1,2, Andreas Adolfsson3, Margareta Ackerman3

  • 1Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL, 32612, USA.

Data in Brief
|July 19, 2019
PubMed
Summary

This study visualizes R datasets to explore clusterability using descriptive statistics and dimensionality reduction techniques. Findings offer insights into data patterns for clustering analysis.

Keywords:
DatasetsDimension reductionHistogramsPairwise distancesPrincipal component analysis

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Area of Science:

  • Data Science
  • Statistical Computing
  • Machine Learning

Background:

  • The R statistical software package includes a rich collection of datasets.
  • Understanding the inherent clusterability of these datasets is crucial for various analytical tasks.
  • Existing literature provides methods for cluster analysis, but direct visualization of dataset clusterability is less explored.

Purpose of the Study:

  • To describe and visualize datasets available in the R statistical software.
  • To provide insights into the clusterability of these datasets through descriptive statistics and visualizations.
  • To facilitate the understanding of data structure for potential clustering applications.

Main Methods:

  • Utilized descriptive statistics (means, extrema, quartiles, standard deviation, standard error) for dataset summarization.
  • Generated two-dimensional plots for all variable pairs within each dataset.
  • Applied dimensionality reduction techniques, including pairwise distances and principal component analysis (PCA), to create single-dimensional representations.
  • Created histograms for each dataset using both dimensionality reduction methods.

Main Results:

  • Provided comprehensive descriptive statistics and visualizations for R datasets.
  • Visualized pairwise variable relationships to identify potential clusters.
  • Demonstrated data reduction via PCA and pairwise distances, with histograms illustrating the resulting distributions.
  • Included original data references and linked to further resources on clusterability.

Conclusions:

  • The described visualizations and statistical summaries offer a clear overview of R dataset characteristics.
  • The dimensionality reduction techniques provide alternative perspectives on data structure relevant to clustering.
  • This work serves as a valuable resource for researchers exploring cluster analysis within the R environment.