ViSiElse: An innovative R-package to visualize raw behavioral data over time
- 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, time data
- 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 R-package to visualize raw behavioral data over time. PeerJ Preprints 7:e27665v3 https://doi.org/10.7287/peerj.preprints.27665v3
Abstract
The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.
Author Comment
In this revised version, we added a new table to illustrate ViSiElse dataset (Table 1). We also created a new R online documentation that follows the example and R script introduced in the paper (links in the introduction). Finally, we used PeerJ language editing services to improve the clarity of the manuscript.