R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund.Inter-Rater Reliability Essentials: Practical Guide in R by A.Practical Statistics in R for Comparing Groups: Numerical Variables by A.Network Analysis and Visualization in R by A.GGPlot2 Essentials for Great Data Visualization in R by A.R Graphics Essentials for Great Data Visualization by A.Machine Learning Essentials: Practical Guide in R by A.Practical Guide To Principal Component Methods in R by A.Practical Guide to Cluster Analysis in R by A.Free Training - How to Build a 7-Figure Amazon FBA Business You Can Run 100% From Home and Build Your Dream Life! by ASM.Psychological First Aid by Johns Hopkins University.Excel Skills for Business by Macquarie University.Introduction to Psychology by Yale University.Business Foundations by University of Pennsylvania.IBM Data Science Professional Certificate by IBM.Python for Everybody by University of Michigan.Google IT Support Professional by Google.The Science of Well-Being by Yale University.AWS Fundamentals by Amazon Web Services.Epidemiology in Public Health Practice by Johns Hopkins University.Google IT Automation with Python by Google.Specialization: Genomic Data Science by Johns Hopkins University.Specialization: Software Development in R by Johns Hopkins University.Specialization: Statistics with R by Duke University.Specialization: Master Machine Learning Fundamentals by University of Washington.Courses: Build Skills for a Top Job in any Industry by Coursera.Specialization: Python for Everybody by University of Michigan.Specialization: Data Science by Johns Hopkins University.Course: Machine Learning: Master the Fundamentals by Stanford.# Barplot using RColorBrewerīarplot(c(2,5,7), col = brewer.pal(n = 3, name = "RdBu"))Ĭoursera - Online Courses and Specialization Data science The function brewer.pal() is used to generate a vector of colors. Sp + scale_color_brewer(palette = "Dark2") scale_color_brewer() for lines and pointsīp + scale_fill_brewer(palette = "Dark2").scale_fill_brewer() for box plot, bar plot, violin plot, dot plot, etc. Two color scale functions are available in ggplot2 for using the colorbrewer palettes: You can also view a single RColorBrewer palette by specifying its name as follow : # View a single RColorBrewer palette by specifying its nameĭ(n = 8, name = 'Dark2')īrewer.pal(n = 8, name = "Dark2") # "#1B9E77" "#D95F02" "#7570B3" "#E7298A" "#66A61E" "#E6AB02" "#A6761D" To display only colorblind-friendly brewer palettes, use this R code: (colorblindFriendly = TRUE) colorblindFriendly: if TRUE, display only colorblind friendly palettes.select: A list of palette names to display.Allowed values are one of: “div”, “qual”, “seq”, or “all”. name: A palette name from the lists above.n: Number of different colors in the palette, minimum 3, maximum depending on palette.Display a single RColorBrewer paletteĭ(n = NULL, type = "all", select = NULL, Return the hexadecimal color specification The RColorBrewer package include also three important functions: # 1. The diverging palettes are : BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral Diverging palettes (third list of colors), which put equal emphasis on mid-range critical values and extremes at both ends of the data range.The palettes names are : Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3. They not imply magnitude differences between groups. Qualitative palettes (second list of colors), which are best suited to represent nominal or categorical data.The palettes names are : Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu YlOrBr, YlOrRd. Sequential palettes (first list of colors), which are suited to ordered data that progress from low to high (gradient).
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