2019
DOI: 10.7287/peerj.preprints.27665v3
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ViSiElse: An innovative R-package to visualize raw behavioral data over time

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 da… Show more

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Cited by 13 publications
(14 citation statements)
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“…Since we included same year density as a control variable, we kept these models as is, despite high VIFs. Plots were generated using ggplot2 (Wickham, 2009), ggeffects (Lüdecke, 2018), and viridis (Garnier, 2018). All analysis and plotting were conducted in R v. 3.6.3 (R Development Core Team 2020).…”
Section: Methodsmentioning
confidence: 99%
“…Since we included same year density as a control variable, we kept these models as is, despite high VIFs. Plots were generated using ggplot2 (Wickham, 2009), ggeffects (Lüdecke, 2018), and viridis (Garnier, 2018). All analysis and plotting were conducted in R v. 3.6.3 (R Development Core Team 2020).…”
Section: Methodsmentioning
confidence: 99%
“…Finally, we ordered blocks according to flowering sequence and we created one heat map (packages ‘ ggplot2 ’ and ‘ viridis ’, Wickham 2016, Garnier 2018) for each urban class with tiles coloured as a function of site density (i.e. the number of sites included at any time t i in a given block).…”
Section: Methodsmentioning
confidence: 99%
“…We also used R packages ape 55 , data.table 56 , dplyr 57 , ggplot2 58 , gridExtra 59 , mapdata 60 , plyr 61 , png 62 , RcolorBrewer 63 , rgdal 64 , raster 65 and viridis 66 .…”
Section: Methodsmentioning
confidence: 99%