How to create violin chart using ggplot2 in R programming

 

Violin Chart

 

Violin charts can be produced with ggplot2 thanks to the geom_violin() function. The first chart of the series below describes its basic utilization and explain how to build violin chart from different input format.

Building a violin plot with ggplot2 is pretty straightforward thanks to the dedicated geom_violin() function.

# Library

library(ggplot2)

# create a dataset

data <- data.frame(

  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),

  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )

)

# Most basic violin chart

p <- ggplot(data, aes(x=name, y=value, fill=name)) + # fill=name allow to automatically dedicate a color for each group

  geom_violin()

p

 

Ggplot2 expects input data to be in a long format: each row is dedicated to one observation. Your input needs 2 column:
- a categorical variable for the X axis: it needs to be have the class factor
- a numeric variable for the Y axis: it needs to have the class numeric

→ From long format

You already have the good format. It’s going to be a breeze to plot it with geom_violin() as follow:

# Library

library(ggplot2)

library(dplyr)

# Create data

data <- data.frame(

  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),

  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) ) %>% round(2)

)

name	value
A	11.02
A	5.54
A	18.05
A	6.57

# Basic violin

ggplot(data, aes(x=name, y=value, fill=name)) + 

  geom_violin()

From wide format

In this case we need to reformat the input. This is possible thanks to the gather() function of the tidyr library that is part of the tidyverse.

# Let's use the iris dataset as an example:

data_wide <- iris[ , 1:4]

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
5.1	3.5	1.4	0.2
4.9	3.0	1.4	0.2
4.7	3.2	1.3	0.2
4.6	3.1	1.5	0.2

library(tidyr)

library(ggplot2)

library(dplyr)

data_wide %>% 

  gather(key="MesureType", value="Val") %>%

  ggplot( aes(x=MesureType, y=Val, fill=MesureType)) +

    geom_violin()

Horizontal violin plot with ggplot2

Building a violin plot with ggplot2 is pretty straightforward thanks to the dedicated geom_violin() function. Here, calling coord_flip() allows to flip X and Y axis and thus get a horizontal version of the chart. Moreover, note the use of the theme_ipsum of the hrbrthemes library that improves general appearance.

# Libraries

library(ggplot2)

library(dplyr)

library(tidyr)

library(forcats)

library(hrbrthemes)

library(viridis)

# Load dataset from github

data <- read.table("https://raw.githubusercontent.com/zonination/perceptions/master/probly.csv", header=TRUE, sep=",")

# Data is at wide format, we need to make it 'tidy' or 'long'

data <- data %>% 

  gather(key="text", value="value") %>%

  mutate(text = gsub("\\.", " ",text)) %>%

  mutate(value = round(as.numeric(value),0)) %>%

  filter(text %in% c("Almost Certainly","Very Good Chance","We Believe","Likely","About Even", "Little Chance", "Chances Are Slight", "Almost No Chance"))

# Plot

p <- data %>%

  mutate(text = fct_reorder(text, value)) %>% # Reorder data

  ggplot( aes(x=text, y=value, fill=text, color=text)) +

    geom_violin(width=2.1, size=0.2) +

    scale_fill_viridis(discrete=TRUE) +

    scale_color_viridis(discrete=TRUE) +

    theme_ipsum() +

    theme(

      legend.position="none"

    ) +

    coord_flip() + # This switch X and Y axis and allows to get the horizontal version

    xlab("") +

    ylab("Assigned Probability (%)")

p

Violin plot with included boxplot and sample size in ggplot2

Building a violin plot with ggplot2 is pretty straightforward thanks to the dedicated geom_violin() function. It is possible to use geom_boxplot() with a small width in addition to display a boxplot that provides summary statistics.

Moreover, note a small trick that allows to provide sample size of each group on the X axis: a new column called myaxis is created and is then used for the X axis.

# Libraries

library(ggplot2)

library(dplyr)

library(hrbrthemes)

library(viridis)

# create a dataset

data <- data.frame(

  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),

  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )

)

# sample size

sample_size = data %>% group_by(name) %>% summarize(num=n())

# Plot

data %>%

  left_join(sample_size) %>%

  mutate(myaxis = paste0(name, "\n", "n=", num)) %>%

  ggplot( aes(x=myaxis, y=value, fill=name)) +

    geom_violin(width=1.4) +

    geom_boxplot(width=0.1, color="grey", alpha=0.2) +

    scale_fill_viridis(discrete = TRUE) +

    theme_ipsum() +

    theme(

      legend.position="none",

      plot.title = element_text(size=11)

    ) +

    ggtitle("A Violin wrapping a boxplot") +

    xlab("")

Grouped violin chart with ggplot2

A grouped violin plot displays the distribution of a numeric variable for groups and subgroups. Here, groups are days of the week, and subgroups are Males and Females. Ggplot2 allows this kind of representation thanks to the position="dodge" option of the geom_violin() function. Groups must be provided to x, subgroups must be provided to fill.

# Libraries

library(ggplot2)

library(dplyr)

library(forcats)

library(hrbrthemes)

library(viridis)

# Load dataset from github

data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/10_OneNumSevCatSubgroupsSevObs.csv", header=T, sep=",") %>%

  mutate(tip = round(tip/total_bill*100, 1))

  # Grouped

data %>%

  mutate(day = fct_reorder(day, tip)) %>%

  mutate(day = factor(day, levels=c("Thur", "Fri", "Sat", "Sun"))) %>%

  ggplot(aes(fill=sex, y=tip, x=day)) + 

    geom_violin(position="dodge", alpha=0.5, outlier.colour="transparent") +

    scale_fill_viridis(discrete=T, name="") +

    theme_ipsum()  +

    xlab("") +

    ylab("Tip (%)") +

    ylim(0,40)