Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron.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.
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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.Scale_color_manual(values=c("#00AFBB","#FC4E07"))Ĭoursera - Online Courses and Specialization Data science Scale_linetype_manual(values=c("solid", "dashed"))+ # Change manually line type and color manually To be able to apply these functions, you should create a geom_line, which line types, color and size should be controlled by groups. scale_size_manual(): change the size of lines.scale_color_manual(): change line colors.scale_linetype_manual(): change line types.Change the appearance of line types manually:.Geom_line(aes(linetype = supp, color = supp))+ Ggplot(df2, aes(x = dose, y = len.mean, group = supp)) + # Compute the mean of `len` grouped by dose and supp Create a line plot for multiple groups.Ggplot(data = df, aes(x = dose, y = len.mean, group = 1)) + Add a legend to the plot and set legend lty Plot(x, y1, type = "b", frame = FALSE, pch = 19, The option cex is used to set the legend text size. Finally, we add a legend on the plot using the R base function legend(), which take the same col and lty arguments as the lines function. Next, we add a second line with a dashed line style ( lty = 2).
We start by plotting a first single line with a solid line type ( lty = 1).