Thinking Outside the Box: Developing Dynamic Data Visualizations for Psychology with Shiny

David A Ellis,Hannah L Merdian

doi:10.3389/fpsyg.2015.01782

David A Ellis, Hannah L Merdian

Open Access

https://doi.org/10.3389/fpsyg.2015.01782

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Abstract

The study of human perception has helped psychologists effectively communicate data rich stories by converting numbers into graphical illustrations and data visualization remains a powerful means for psychology to discover, understand, and present results to others. However, despite an exponential rise in computing power, the World Wide Web, and ever more complex data sets, psychologists often limit themselves to static visualizations. While these are often adequate, their application across professional psychology remains limited. This is surprising as it is now possible to build dynamic representations based around simple or complex psychological data sets. Previously, knowledge of HTML, CSS, or Java was essential, but here we develop several interactive visualizations using a simple web application framework that runs under the R statistical platform: Shiny. Shiny can help researchers quickly produce interactive data visualizations that will supplement and support current and future publications. This has clear benefits for researchers, the wider academic community, students, practitioners, and interested members of the public.

Highlights

Psychological data analysis continues to develop with a recent shift in focus from significance testing to the exploration of effect sizes and confidence intervals (Schmidt, 1996; Sainani, 2009)
We aim to address both aspects by introducing Shiny, a data-sharing and visualization platform with low threshold requirements for most psychologists
While research has often focused on the enhancement of existing static visualization tools, such as violin plots to express both density and distribution of data (Marmolejo-Ramos and Matsunaga, 2009), these remain limited due to their static nature

Summary

Introduction

Psychological data analysis continues to develop with a recent shift in focus from significance testing to the exploration of effect sizes and confidence intervals (Schmidt, 1996; Sainani, 2009). The focus has often been to maximize the expressive power of figures, both with regards to conveying the content and structure of the data as well as informing the analysis process (Campitelli and Macbeth, 2014; Marmolejo-Ramos, 2014). This has included a number of computational developments, such as the expansion of boxplots to include information about both distribution and density of the data (Marmolejo-Ramos and Matsunaga, 2009; Marmolejo-Ramos and Tian, 2010) or explorations of different data visualizations for skewed data sets (Ospina et al, 2014). Static data visualizations are limited in the quantity and type

Objectives

Conclusion