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.
Related BookGGPlot2 Essentials for Great Data Visualization in R
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.
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