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ViSiElse: an innovative R-package to visualize raw behavioral data over time
Author(s) -
Élodie Garnier,
Nastasia Fouret,
Médéric Descoins
Publication year - 2020
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.8341
Subject(s) - raw data , computer science , visualization , timestamp , data visualization , data mining , transparency (behavior) , graph , sample (material) , software , r package , data science , human–computer interaction , theoretical computer science , real time computing , computational science , chemistry , computer security , chromatography , programming language
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 article, 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.

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