How To Make Violin Plots with ggplot2 in R?

Violin plots help us to visualize numerical variables from one or more categories. They are similar to box plots in the way they show a numerical distribution using five summary-level statistics. But violin plots also have the density information of the numerical variables. It allows visualizing the distribution of several categories by displaying their densities.

In this article, we will discuss how to plot a violin plot with the help of the ggplot2 library in R Programming Language. To plot a violin plot using the ggplot2 package we use the geom_violin() function.

Syntax: ggplot( dataframe, aes( x, y, fill, color)) + geom_violin()

Parameters:

• dataframe: determines the dataset used in the plot.
• fill: determines the color of background of interior of the plot.
• color: determines the color of boundary of plot.

Creating basic Violin Plots

Here, is a basic violin plot made using the geom_violin() function. We have used the diamonds data frame in this plot which is provided by the R language natively.

R

# load library ggplot2
library(ggplot2)

# Basic violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R language natively.
ggplot(diamonds, aes(x=cut, y=price)) +

# geom_violin() function is used to plow violin plot
  geom_violin()

Output:

 Violin Plots with ggplot2 in R

Color Customization

We can change the color of the violin plot using the color parameter of aes() function of ggplot2. This changes the color of the boundary of the violin plot according to the category of data. Here, plots are colored according to the category of their cut by putting cut as a parameter color. 

R

# load library ggplot2
library(ggplot2)

# Basic violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R language natively
# color parameter is used to color the boundary of
# plot according to category
ggplot(diamonds, aes(x=cut, y=price, color=cut)) +

# geom_violin() function is used to plow violin plot
  geom_violin()

Output:

 Violin Plots with ggplot2 in R

We can change the background color of the violin plot using the fill parameter of aes() function of ggplot2. This changes the color of the background of the interior of the violin plot according to the category of data.

Here, plots are colored according to the category of their cut by putting cut as parameter fill. 

R

# load library ggplot2
library(ggplot2)

# Basic violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R language natively
# fill parameter is used to color the background of
#plot according to category
ggplot(diamonds, aes(x=cut, y=price, fill=cut)) +

# geom_violin() function is used to plow violin plot
  geom_violin()

Output:

Violin Plots with ggplot2 in R

Horizontal Violin Plot

To convert a normal violin plot to a horizontal violin plot we add the coord_flip() function to the ggplot() function. This flips the coordinate axis of the plot and converts any ggplot2 plot into a horizontal plot. 

Syntax: plot+ coord_flip()

Here, is a horizontal violin plot made using the coord_flip() function. 

R

# load library ggplot2
library(ggplot2)

# Horizontal violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R language natively.
ggplot(diamonds, aes(x=cut, y=price)) +

# geom_violin() function is used to plow violin plot
geom_violin()+

# coord_flip() function is used to make horizontal
# violin plot
coord_flip()

Output:

Violin Plots with ggplot2 in R

Mean marker customization

In ggplot2, we use the stat_summary() function to compute new summary statistics and add them to the plot. We use the stat_summary() function with ggplot() function. 

Syntax:

plot+ stat_summary(fun.y, geom, size, color)

Here, 

• fun.y: determines the function according to which marker has to be placed i.e. mean, median, etc.
• geom: determines the shape of marker
• size: determines size of marker
• color: determines the color of marker

Example:

In this example, we will compute the mean value of the y-axis variable using fun.y argument in the stat_summary() function. 

R

# load library ggplot2
library(ggplot2)

# Basic violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R
# language natively.
ggplot(diamonds, aes(x=cut, y=price)) +

# geom_violin() function is used to plow violin plot
  geom_violin()+

# Stat_summary() function adds mean marker on plot
stat_summary(fun.y="mean", geom="point", size=2, color="red")

Output:

Violin Plots with ggplot2 in R

Here, the point in the center of the violin shows the variation of the mean of the y-axis for each category of data on the x-axis.

Adding box plot in violin plot:

Adding a box plot in the violin plot we will use the geom_boxplot function which helps to make a box plot.

R

# Load library ggplot2
library(ggplot2)

# Basic violin plot
# diamonds dataframe has been used here
# diamonds dataframe is provided by R language natively.
ggplot(diamonds, aes(x = cut, y = price)) +
  # geom_violin() function is used to plot violin plot
  geom_violin() +
  # Add geom_boxplot() to include box plot
  geom_boxplot(width = 0.2, fill = "white", color = "black")

Output:

Violin Plots with ggplot2 in R

Customization of the box plot:

Add some color and other features in the box plot we use several inbuilt functions.

R

library(ggplot2)

# Set a custom theme
theme_set(theme_minimal())

# Modify the appearance of the plot
ggplot(diamonds, aes(x = cut, y = price)) +
  geom_violin(fill = "#66C2A5", color = "#084B83", alpha = 0.8) +
  geom_boxplot(width = 0.2, fill = "#EFCB68", color = "#8F2D56", alpha = 0.8) +

  # Customize axes labels and title
  labs(x = "Cut", y = "Price", title = "Violin Plot with Box Plot") +

  # Adjust axis text size and color
  theme(axis.text = element_text(size = 12, color = "#333333")) +

  # Add a background color to the plot area
  theme(plot.background = element_rect(fill = "#F7F7F7")) +

  # Modify the legend appearance
  theme(legend.background = element_rect(fill = "#F7F7F7"),
        legend.position = "bottom") +

  # Adjust the plot margins
  theme(plot.margin = margin(20, 20, 20, 20))

Output:

Violin Plots with ggplot2 in R