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tourr: An R Package for Exploring Multivariate Data with Projections

Author

Listed:
  • Wickham, Hadley
  • Cook, Dianne
  • Hofmann, Heike
  • Buja, Andreas

Abstract

This paper describes an R package which produces tours of multivariate data. The package includes functions for creating different types of tours, including grand, guided, and little tours, which project multivariate data (p-D) down to 1, 2, 3, or, more generally, d (≤ p) dimensions. The projected data can be rendered as densities or histograms, scatterplots, anaglyphs, glyphs, scatterplot matrices, parallel coordinate plots, time series or images, and viewed using an R graphics device, passed to GGobi, or saved to disk. A tour path can be stored for visualisation or replay. With this package it is possible to quickly experiment with different, and new, approaches to tours of data. This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.

Suggested Citation

  • Wickham, Hadley & Cook, Dianne & Hofmann, Heike & Buja, Andreas, 2011. "tourr: An R Package for Exploring Multivariate Data with Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i02).
    • Handle: RePEc:jss:jstsof:v:040:i02
    DOI: http://hdl.handle.net/10.18637/jss.v040.i02
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    References listed on IDEAS

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    1. Lee, Eun-Kyung & Cook, Dianne & Klinke, Sigbert & Lumley, Thomas, 2005. "Projection pursuit for exploratory supervised classification," SFB 649 Discussion Papers 2005-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    Cited by:

    1. Huang, Bei & Cook, Dianne & Wickham, Hadley, 2012. "tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 49(i06).
    2. Hlávka, Zdeněk & Hlubinka, Daniel & Koňasová, Kateřina, 2022. "Functional ANOVA based on empirical characteristic functionals," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Fischer, Daniel & Berro, Alain & Nordhausen, Klaus & Ruiz-Gazen, Anne, 2019. "REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit," TSE Working Papers 19-1001, Toulouse School of Economics (TSE).
    4. Valero-Mora, Pedro M. & Ledesma, Ruben, 2012. "Graphical User Interfaces for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 49(i01).
    5. Ursula Laa & Dianne Cook & Andreas Buja & German Valencia, 2020. "Hole or grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions," Monash Econometrics and Business Statistics Working Papers 17/20, Monash University, Department of Econometrics and Business Statistics.
    6. Ursula Laa & Dianne Cook & Stuart Lee, 2020. "Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data," Monash Econometrics and Business Statistics Working Papers 36/20, Monash University, Department of Econometrics and Business Statistics.
    7. Niladri Roy Chowdhury & Dianne Cook & Heike Hofmann & Mahbubul Majumder & Eun-Kyung Lee & Amy Toth, 2015. "Using visual statistical inference to better understand random class separations in high dimension, low sample size data," Computational Statistics, Springer, vol. 30(2), pages 293-316, June.

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