A Quantile-quantile plot (or QQPlot) is used to check whether a given data follows normal distribution.
The data is assumed to be normally distributed when the points approximately follow the 45-degree reference line.
This article describes how to create a qqplot in R using the ggplot2 package.
Key R functions
- Key function:
- Key arguments:
sizeto change point color, shape and size.
Create some data (
wdata) containing the weights by sex (M for male; F for female):
set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58)) ) # head(wdata, 4)
Loading required R package
Load the ggplot2 package and set the default theme to
theme_minimal() with the legend at the top of the plot:
library(ggplot2) theme_set( theme_minimal() + theme(legend.position = "top") )
Create a qq-plot of weight. Change color by groups (sex)
ggplot(wdata, aes(sample = weight)) + stat_qq(aes(color = sex)) + scale_color_manual(values = c("#00AFBB", "#E7B800"))+ labs(y = "Weight")
Alternative plot using the function
ggqqplot() [in ggpubr]. The 95% confidence band is shown by default.
library(ggpubr) ggqqplot(wdata, x = "weight", color = "sex", palette = c("#0073C2FF", "#FC4E07"), ggtheme = theme_pubclean())
This article shows how to create a qqplot using the ggplot2 and the ggpubr package.
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Coursera - Online Courses and Specialization
- Course: Machine Learning: Master the Fundamentals by Standford
- 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
- 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
Books - Data Science
- 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)
- 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