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

Ellis DA, Merdian HL - Front Psychol (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Lancaster University Lancaster, UK.

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.

No MeSH data available.


Showing a variety of visualization options within Example 3.
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Figure 2: Showing a variety of visualization options within Example 3.

Mentions: Examples 2 and 3 are developed directly from Example 1. Marked-up code is available in the Supplementary Material, example2 and example3. These can be run in an identical fashion to example1. Example 2 adds boxplots and statistical output, which again relies on standard graphical and mathematical functions in R. This version also allows the user to build linear regression models after choosing any predictor and response variable (e.g., the predictive value of Honest-Humility); statistical output is presented underneath the scatter plot, providing information relating to effect sizes and statistical significance. Box plots can be used to directly compare the distribution of scores on these variables, or to compare levels of crime-related fear between men and women directly. Example 3 (Figure 2) adds two additional functions, which handle a variety of potential visualization options. This provides separate regression outputs for male and female participants and/or those who have previously been a victim of crime.


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

Ellis DA, Merdian HL - Front Psychol (2015)

Showing a variety of visualization options within Example 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664644&req=5

Figure 2: Showing a variety of visualization options within Example 3.
Mentions: Examples 2 and 3 are developed directly from Example 1. Marked-up code is available in the Supplementary Material, example2 and example3. These can be run in an identical fashion to example1. Example 2 adds boxplots and statistical output, which again relies on standard graphical and mathematical functions in R. This version also allows the user to build linear regression models after choosing any predictor and response variable (e.g., the predictive value of Honest-Humility); statistical output is presented underneath the scatter plot, providing information relating to effect sizes and statistical significance. Box plots can be used to directly compare the distribution of scores on these variables, or to compare levels of crime-related fear between men and women directly. Example 3 (Figure 2) adds two additional functions, which handle a variety of potential visualization options. This provides separate regression outputs for male and female participants and/or those who have previously been a victim of crime.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Lancaster University Lancaster, UK.

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.

No MeSH data available.