2019
DOI: 10.1080/15366367.2019.1565254
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ggplot2: Elegant Graphics for Data Analysis (2nd ed.)

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Cited by 723 publications
(535 citation statements)
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“…Each subplot depicts mean rCBF values (y-axis) at each time-interval (x axis), for each method of administration in each ROI. Regression lines were fitted using a non-parametric regression method that fits multiple regressions in local neighbourhood (Locally estimated scatterplot smoothing -LOESS) to the time-course of mean rCBF in each ROI for each method of administration separately (the shadows denote the 95% confidence interval for each fitted line) 97 . To avoid the potential confounding effects of nominal changes in global CBF across time or between methods of administration, we used demeaned values of mean ROI rCBF estimates, which correspond to subtracting global CBF estimates from the raw mean rCBF in each ROI.…”
Section: Discussionmentioning
confidence: 99%
“…Each subplot depicts mean rCBF values (y-axis) at each time-interval (x axis), for each method of administration in each ROI. Regression lines were fitted using a non-parametric regression method that fits multiple regressions in local neighbourhood (Locally estimated scatterplot smoothing -LOESS) to the time-course of mean rCBF in each ROI for each method of administration separately (the shadows denote the 95% confidence interval for each fitted line) 97 . To avoid the potential confounding effects of nominal changes in global CBF across time or between methods of administration, we used demeaned values of mean ROI rCBF estimates, which correspond to subtracting global CBF estimates from the raw mean rCBF in each ROI.…”
Section: Discussionmentioning
confidence: 99%
“…Perplexity was set to 30 (default). t-SNE plots were generated using R package ggplot2 63 . Clustering was done first by establishing a shared nearest neighbor and then conducting Luvain-Jaccard analysis on the resulted graph using FindClusters function from Seurat with default setting.…”
Section: Methodsmentioning
confidence: 99%
“…Different trees were constructed for (I) PKs from Sbi; (II) PKs from Ssp; and (III) PKs from both Sbi and Ssp. The dendrogram visualization and plotting were generated using R statistical software (R Core Team, 2013) with the ggtree (Yu et al, 2017) and ggplot2 (Villanueva and Chen, 2019) packages.…”
Section: Methodsmentioning
confidence: 99%