Ggplot2 > Statistical Charts > Violin Plots
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Violin Plots in ggplot2

How to make Violin Plots in ggplot2 with Plotly.


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Default violin plot

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin()

plotly::ggplotly(p)

Flip plot orientation

library(plotly)
library(ggplot2)

p <-   
 ggplot(mtcars, aes(mpg, factor(cyl))) +
  geom_violin()

plotly::ggplotly(p)

With geom_violin(), the y-axis must always be the continuous variable, and the x-axis the categorical variable. To create horizontal violin graphs, keep the x- and y-variables as is and add coord_flip().

library(plotly)
library(ggplot2)

district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))

p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
  geom_violin(colour=NA) +
  geom_hline(yintercept=0, alpha=0.5) +
  labs(title = "Democratic performance in the 2018 House elections, by region and density",
       x = "Density Index\nfrom CityLab",
       y = "Margin of Victory/Defeat") +
  coord_flip()

ggplotly(p)

Add data points with jitter

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin() + geom_jitter(height = 0, width = 0.1)

plotly::ggplotly(p)

Scaling maximum width

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(scale = "count")

plotly::ggplotly(p)

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(scale = "width")

plotly::ggplotly(p)

Disabling default trim

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(trim = FALSE)

plotly::ggplotly(p)

Closer density fit

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(adjust = .5)

plotly::ggplotly(p)

Adding fill

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(aes(fill = cyl))

plotly::ggplotly(p)

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(aes(fill = factor(cyl)))

plotly::ggplotly(p)

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(aes(fill = factor(vs)))

plotly::ggplotly(p)

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(aes(fill = factor(am)))

plotly::ggplotly(p)

Changing border colour

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(fill = "grey80", colour = "#3366FF")

plotly::ggplotly(p)

Enabling quartiles

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p <- p + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75))

plotly::ggplotly(p)

Add facetting

Including facetting by region.

Add colour to the facet titles, centre-align the title, rotate the y-axis title, change the font, and get rid of the unnecessary legend. Note that coord_flip() flips the axes for the variables and the titles, but does not flip theme() elements.

Rotated the x-axis text 45 degrees, and used facet_grid to create a 4x1 facet (compared to facet_wrap, which defaults to 2x2).

library(plotly)
library(ggplot2)

district_density <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/district_density.csv", stringsAsFactors = FALSE)
district_density$cluster <- factor(district_density$cluster, levels=c("Pure urban", "Urban-suburban mix", "Dense suburban", "Sparse suburban", "Rural-suburban mix", "Pure rural"))
district_density$region <- factor(district_density$region, levels=c("West", "South", "Midwest", "Northeast"))

p <- ggplot(district_density,aes(x=cluster, y=dem_margin, fill=cluster)) +
  geom_violin(colour=NA) +
  geom_hline(yintercept=0, alpha=0.5) +
  facet_grid(.~region) +
  labs(title = "Democratic performance in the 2018 House elections, by region and density",
       x = "Density Index\nfrom CityLab",
       y = "Margin of Victory/Defeat") +
  theme(axis.text.x = element_text(angle = -45),
        plot.title = element_text(hjust = 0.5),
        strip.background = element_rect(fill="lightblue"),
        text = element_text(family = 'Fira Sans'),
        legend.position = "none")

ggplotly(p)

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)