A **Violin Plot** is used to visualize the distribution of the data and its probability density.

This chart is a combination of a Box plot and a Density Plot that is rotated and placed on each side, to display the distribution shape of the data.

Typically, violin plots will include a marker for the median of the data and a box indicating the interquartile range, as in standard boxplots.

A Violin Plot shows more information than a Box Plot. For example, in a violin plot, you can see whether the distribution of the data is bimodal or multimodal.

This article describes how to create and customize **violin plots** using the **ggplot2** R package.

Contents:

#### Related Book

GGPlot2 Essentials for Great Data Visualization in R## Key R functions

Key function:

`geom_violin()`

: Creates violin plots. Key arguments:`color`

,`size`

,`linetype`

: Border line color, size and type`fill`

: Areas fill color`trim`

: logical value. If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don’t trim the tails.

`stat_summary()`

: Adds summary statistics (mean, median, …) on the violin plots.

## 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.

- Continuous variable:

First, convert the variable `dose`

from a numeric to a discrete factor variable:

```
data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
head(ToothGrowth, 4)
```

```
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 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 violin plots

We start by initiating a plot named `e`

, then we’ll add layers. The following R code creates Violin Plots combined with summary statistics (mean +/- SD) and Box Plots.

Create basic violin plots with summary statistics:

```
# Initiate a ggplot
e <- ggplot(ToothGrowth, aes(x = dose, y = len))
# Add mean points +/- SD
# Use geom = "pointrange" or geom = "crossbar"
e + geom_violin(trim = FALSE) +
stat_summary(
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "pointrange", color = "black"
)
# Combine with box plot to add median and quartiles
# Change fill color by groups, remove legend
e + geom_violin(aes(fill = dose), trim = FALSE) +
geom_boxplot(width = 0.2)+
scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
theme(legend.position = "none")
```

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.

## Create a Violin Plot with multiple groups

Two different grouping variables are used: `dose`

on x-axis and `supp`

as line color (legend variable).

The space between the grouped plots is adjusted using the function `position_dodge()`

.

```
e + geom_violin(aes(color = supp), trim = FALSE, position = position_dodge(0.9) ) +
geom_boxplot(aes(color = supp), width = 0.15, position = position_dodge(0.9)) +
scale_color_manual(values = c("#00AFBB", "#E7B800"))
```

## Conclusion

This article describes how to create a Violin Plot using the ggplot2 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

#### Data science

- 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

#### 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

### 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

Version: Français

## No Comments