Basic violin plot
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
# fill=name allow to automatically dedicate a color for each group
p <- ggplot(data, aes(x=name, y=value, fill=name)) + geom_violin()
#p
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.Lengt
h 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()
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 (%)")
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("")
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_O
neNumSevCatSubgroupsSevObs.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)
