ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency
- Published
- Accepted
- Subject Areas
- Bioinformatics, Science and Medical Education, Statistics, Data Science
- Keywords
- data visualization, raw data, behavior, data transparency, R package, graph, timestamps, actions
- Copyright
- © 2019 Garnier et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. ViSiElse: An innovative visualization R package to ensure behavioral raw data reliability and transparency. PeerJ Preprints 7:e27665v2 https://doi.org/10.7287/peerj.preprints.27665v2
Abstract
Background. In recent years, the scientific community encouraged the use of raw data graphs to improve the reliability and transparency of the results presented in papers. However, methods to visualize raw data are limited to one variable per graph and/or only small samples. In behavioral science as in many other fields, multiple variables need to be plotted together to allow insights of the behavior or the process observations. In this paper, we present ViSiElse, an R-package offering a new approach in raw data visualization.
Methods. ViSiElse was developed with the open-source software R to provide a solution for the complete visualization of the raw time data.
Results. ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding statistical indicators and/or time constraints. ViSiElse provides a global visualization of actions acquired from timestamps in any quantifiable observations.
Author Comment
In this revised version of the manuscript, we changed the supporting example to a more understandable one. The new example describes the actions performed during a typical day. We also added an Application section providing concrete examples like medical procedure (orotracheal intubation) and users' web navigation (online shopping behavior). Finally, we completed this article by comparing ViSiElse to more conventional and commonly used raw time data visualization methods (scatter plot, violin plot and heatmap).