# install.packages("ggplot2") # Uncomment if not installed
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.1.3
To create a violin plot using ggplot2
in R that displays the distribution of a continuous variable with separate violins for each group using an in-built dataset.
We use the ggplot2
package for plotting. Install it if not already available.
# install.packages("ggplot2") # Uncomment if not installed
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.1.3
We’ll use the built-in iris
dataset. This dataset includes measurements of sepal and petal lengths/widths for three species of iris flowers.
data(iris)
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
str(iris)
'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
We’ll use geom_violin()
from ggplot2, with custom fill and transparency.
ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
geom_violin(trim = FALSE, alpha = 0.6, color = "black") +
labs(
title = "Distribution of Petal Length by Iris Species",
x = "Species",
y = "Petal Length (cm)"
+
) theme_minimal(base_size = 14)
Violin Plot: Combines box plot features with a kernel density plot on each side.
trim = FALSE: Ensures full density is shown rather than clipped at extreme values.
fill = Species: Automatically assigns different fill colors to each group.
alpha: Adjusts the transparency.
theme_minimal(): Applies a clean layout for the plot.
The violin plot gives a detailed visualization of the distribution and density of the Petal.Length
across different Iris species. It’s a powerful alternative to box plots when you want to see the full shape of the distribution.