2011
DOI: 10.1111/j.1541-0420.2011.01616.x
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ggplot2: Elegant Graphics for Data Analysis by WICKHAM, H.

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Cited by 5,686 publications
(6,350 citation statements)
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References 5 publications
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“…Additionally, in order to investigate the relationship between median dN/dS and genomic features associated to each genome, we applied the "cor.test" function with the Spearman method. Mean comparisons and correlation plots were visualized through the ggplot2 package (Wilkinson 2011). Also, we explored the distance between epipelagic and mesopelagic Marinimicrobia genomes employing the genomic features and median dN/dS values through a PCA analysis with the "prcomp" function available on R. Euclidean distance was visualized using the "ggbiplot" function within the ggplot2 package (Wilkinson 2011).…”
Section: Marinimicrobia Genomes Distribution and Statistical Analysesmentioning
confidence: 99%
“…Additionally, in order to investigate the relationship between median dN/dS and genomic features associated to each genome, we applied the "cor.test" function with the Spearman method. Mean comparisons and correlation plots were visualized through the ggplot2 package (Wilkinson 2011). Also, we explored the distance between epipelagic and mesopelagic Marinimicrobia genomes employing the genomic features and median dN/dS values through a PCA analysis with the "prcomp" function available on R. Euclidean distance was visualized using the "ggbiplot" function within the ggplot2 package (Wilkinson 2011).…”
Section: Marinimicrobia Genomes Distribution and Statistical Analysesmentioning
confidence: 99%
“…The correlation matrix and the significance test were visualized by R function "corrplot" [55]. Pie chart, donut chart, violin plot, box plot, heatmap, bar chart and circle bar chart were plotted using ggplot2 [56].…”
Section: Bioinformatics Analysis and Statistical Analysismentioning
confidence: 99%
“…We used Kendall's τ correlation coefficient to compare the association between increasing bin number and corresponding d N /d S (coding) and substitution rates (non-coding) for each gene. Statistical analysis was conducted using the scipy library in Python, and graphs were produced using the 'tidyverse' package in R (Wilkinson 2011) and the 'matplotlib' package in Python. Phylogenies were produced using the 'phytools' package in R (Revell 2012).…”
Section: Phenotype and Pathway Ontologies Protein Databases And Statmentioning
confidence: 99%