GGPlot Stripchart Best Reference - Datanovia (2024)

444334534839482734

• Key R functions
• Data preparation
• Loading required R package
• Basic stripcharts
• Combine with box plots and violin plots
• Create a stripchart with multiple groups
• Conclusion

Related Book

GGPlot2 Essentials for Great Data Visualization in R

Key R functions

• Key function: geom_jitter()
• key arguments: color, fill, size, shape. Changes points color, fill, size and shape

Data preparation

• Demo dataset: ToothGrowth
  • Continuous variable: len (tooth length). Used on y-axis
  • Grouping variable: dose (dose levels of vitamin C: 0.5, 1, and 2 mg/day). Used on x-axis.

First, convert the variable dose from a numeric to a discrete factor variable:

data("ToothGrowth")ToothGrowth$dose <- as.factor(ToothGrowth$dose)head(ToothGrowth, 3)

## len supp dose## 1 4.2 VC 0.5## 2 11.5 VC 0.5## 3 7.3 VC 0.5

Loading required R package

Load the ggplot2 package and set the default theme to theme_classic() with the legend at the top of the plot:

library(ggplot2)theme_set( theme_classic() + theme(legend.position = "top") )

Basic stripcharts

We start by initiating a plot named e, then we’ll add layers. The following R code creates stripcharts combined with summary statistics (mean +/- SD), boxplots and violin plots.

• Change points shape and color by groups
• Adjust the degree of jittering: position_jitter(0.2)
• Add summary statistics:

# Initiate a ggplote <- ggplot(ToothGrowth, aes(x = dose, y = len))# Stripcharts with summary statistics# Change color by dose groupse + geom_jitter(aes(shape = dose, color = dose), position = position_jitter(0.2), size = 1.2) + stat_summary(aes(color = dose), size = 0.4, fun.data="mean_sdl", fun.args = list(mult=1))+ scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

The function mean_sdl is used for adding mean and standard deviation. It computes the mean plus or minus a constant times the standard deviation. In the R code above, the constant is specified using the argument mult (mult = 1). By default mult = 2. The mean +/- SD can be added as a crossbar or a pointrange.

Combine with box plots and violin plots

# Combine with box plote + geom_boxplot() + geom_jitter(position = position_jitter(0.2)) # Strip chart + violin plot + stat summarye + geom_violin(trim = FALSE) + geom_jitter(position = position_jitter(0.2)) + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), color = "red")

Create a stripchart with multiple groups

The R code is similar to what we have seen in dot plots section. However, to create dodged jitter points, you should use the function position_jitterdodge() instead of position_dodge().

e + geom_jitter( aes(shape = supp, color = supp), size = 1.2, position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8) ) + stat_summary( aes(color = supp), fun.data="mean_sdl", fun.args = list(mult=1), size = 0.4, position = position_dodge(0.8) )+ scale_color_manual(values = c("#00AFBB", "#E7B800"))

Conclusion

This article describes how to create a stripchart using the ggplot2 package.

Coursera - Online Courses and Specialization

Data science

• Course: Machine Learning: Master the Fundamentals by Stanford
• Specialization: Data Science by Johns Hopkins University
• Specialization: Python for Everybody by University of Michigan
• Courses: Build Skills for a Top Job in any Industry by Coursera
• Specialization: Master Machine Learning Fundamentals by University of Washington
• Specialization: Statistics with R by Duke University
• Specialization: Software Development in R by Johns Hopkins University
• Specialization: Genomic Data Science by Johns Hopkins University

Popular Courses Launched in 2020

• Google IT Automation with Python by Google
• AI for Medicine by deeplearning.ai
• Epidemiology in Public Health Practice by Johns Hopkins University
• AWS Fundamentals by Amazon Web Services

Trending Courses

• The Science of Well-Being by Yale University
• Google IT Support Professional by Google
• Python for Everybody by University of Michigan
• IBM Data Science Professional Certificate by IBM
• Business Foundations by University of Pennsylvania
• Introduction to Psychology by Yale University
• Excel Skills for Business by Macquarie University
• Psychological First Aid by Johns Hopkins University
• Graphic Design by Cal Arts

Amazon FBA

Amazing Selling Machine

• Free Training - How to Build a 7-Figure Amazon FBA Business You Can Run 100% From Home and Build Your Dream Life! by ASM

Books - Data Science

Our Books

• Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
• Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
• Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
• R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
• GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
• Network Analysis and Visualization in R by A. Kassambara (Datanovia)
• Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
• Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)

Others

• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
• Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
• Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
• An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
• Deep Learning with R by François Chollet & J.J. Allaire
• Deep Learning with Python by François Chollet