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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.


Related in: MedlinePlus

Static vs. dynamic data visualization. A static graph showing a positive relationship between fear and emotionality (A) can quickly be turned into a dynamic visualization (B) which in this example allows a website visitor to select a sub-group (male participants) of interest. Other variables are also available from the drop-down menus on the left and the included statistical analysis updates automatically based on user selections. However, this relies on the data being available to both a user interface and server to process these requests. Previously this was only possible by developing interactive web applications using a combination of HTML, CSS, or Java. However, this is no longer a limiting factor. For those who have a basic knowledge of R, the move from static to dynamic reporting is relatively straightforward.
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Figure 1: Static vs. dynamic data visualization. A static graph showing a positive relationship between fear and emotionality (A) can quickly be turned into a dynamic visualization (B) which in this example allows a website visitor to select a sub-group (male participants) of interest. Other variables are also available from the drop-down menus on the left and the included statistical analysis updates automatically based on user selections. However, this relies on the data being available to both a user interface and server to process these requests. Previously this was only possible by developing interactive web applications using a combination of HTML, CSS, or Java. However, this is no longer a limiting factor. For those who have a basic knowledge of R, the move from static to dynamic reporting is relatively straightforward.

Mentions: However, while static graphical illustrations remain perfectly adequate in many instances, these have become problematic as we move toward larger and more complex data sets that evolve over time (Heer and Kandel, 2012). In a critical review concerning the use of data visualizations in scientific papers, Weissgerber et al. (2015) identified a number of limitations and misrepresentations linked to the current practice of using static figures when presenting continuous data from small sample sizes. Static data visualizations are also limited in the quantity and type of information that can be presented, which is typically directed toward the analysis conducted. These visualizations in isolation often raise additional questions about the data itself or suggest an alternative analysis. Dynamic representations on the other hand can provide an almost limitless supply of additional information; at a basic level, for example, this would enable a regression model to be re-calculated in real-time for male and female participants separately (Figure 1).


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

Ellis DA, Merdian HL - Front Psychol (2015)

Static vs. dynamic data visualization. A static graph showing a positive relationship between fear and emotionality (A) can quickly be turned into a dynamic visualization (B) which in this example allows a website visitor to select a sub-group (male participants) of interest. Other variables are also available from the drop-down menus on the left and the included statistical analysis updates automatically based on user selections. However, this relies on the data being available to both a user interface and server to process these requests. Previously this was only possible by developing interactive web applications using a combination of HTML, CSS, or Java. However, this is no longer a limiting factor. For those who have a basic knowledge of R, the move from static to dynamic reporting is relatively straightforward.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Static vs. dynamic data visualization. A static graph showing a positive relationship between fear and emotionality (A) can quickly be turned into a dynamic visualization (B) which in this example allows a website visitor to select a sub-group (male participants) of interest. Other variables are also available from the drop-down menus on the left and the included statistical analysis updates automatically based on user selections. However, this relies on the data being available to both a user interface and server to process these requests. Previously this was only possible by developing interactive web applications using a combination of HTML, CSS, or Java. However, this is no longer a limiting factor. For those who have a basic knowledge of R, the move from static to dynamic reporting is relatively straightforward.
Mentions: However, while static graphical illustrations remain perfectly adequate in many instances, these have become problematic as we move toward larger and more complex data sets that evolve over time (Heer and Kandel, 2012). In a critical review concerning the use of data visualizations in scientific papers, Weissgerber et al. (2015) identified a number of limitations and misrepresentations linked to the current practice of using static figures when presenting continuous data from small sample sizes. Static data visualizations are also limited in the quantity and type of information that can be presented, which is typically directed toward the analysis conducted. These visualizations in isolation often raise additional questions about the data itself or suggest an alternative analysis. Dynamic representations on the other hand can provide an almost limitless supply of additional information; at a basic level, for example, this would enable a regression model to be re-calculated in real-time for male and female participants separately (Figure 1).

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.


Related in: MedlinePlus