# Split Violin Plots

#### Tom Kelly

#### 2022-12-09

##Violin Plots

Therefore violin plots are a powerful tool to assist researchers to visualise data, particularly in the quality checking and exploratory parts of an analysis. Violin plots have many benefits:

- Greater flexibility for plotting variation than boxplots
- More familiarity to boxplot users than density plots
- Easier to directly compare data types than existing plots

As shown below for the `iris` dataset, violin plots show distribution information that the boxplot is unable to.

###General Set up

    library("vioplot")

We set up the data with two categories (Sepal Width) as follows:

    data(iris)
    summary(iris$Sepal.Width)

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##   2.000   2.800   3.000   3.057   3.300   4.400

    table(iris$Sepal.Width > mean(iris$Sepal.Width))

    ##
    ## FALSE  TRUE
    ##    83    67

    iris_large <- iris[iris$Sepal.Width > mean(iris$Sepal.Width), ]
    iris_small <- iris[iris$Sepal.Width <= mean(iris$Sepal.Width), ]

###Boxplots

First we plot Sepal Length on its own:

    boxplot(Sepal.Length~Species, data=iris, col="grey")

An indirect comparison can be achieved with par:

    {
      par(mfrow=c(2,1))
    boxplot(Sepal.Length~Species, data=iris_small, col = "lightblue")
    boxplot(Sepal.Length~Species, data=iris_large, col = "palevioletred")
    par(mfrow=c(1,1))
    }

### Violin Plots

First we plot Sepal Length on its own:

    vioplot(Sepal.Length~Species, data=iris)

An indirect comparison can be achieved with par:

    {
      par(mfrow=c(2,1))
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line")
    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line")
    par(mfrow=c(1,1))
    }

### Split Violin Plots

A more direct comparision can be made with the `side` argument and `add = TRUE` on the second plot:

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T)
    title(xlab = "Species", ylab = "Sepal Length")
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")

#### Custom axes labels

Custom axes labels are supported for split violin plots. However, you must use these arguments on the first call of `vioplot`.

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right", xlab = "Iris species", ylab = "Length", main = "Sepals", names=paste("Iris", levels(iris$Species)))
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T)
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Width")

Note that this is disabled for the second `vioplot` call to avoid overlaying labels.

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T, xlab = "Iris species", ylab = "Length", main = "Sepals", names=paste("Iris", levels(iris$Species)))

    ## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: names can only be changed on first call of vioplot (when add = FALSE)

    ## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: x-axis labels can only be changed on first call of vioplot (when add = FALSE)

    ## Warning in vioplot.formula(Sepal.Length ~ Species, data = iris_small, col = "lightblue", : Warning: y-axis labels can only be changed on first call of vioplot (when add = FALSE)

    ## Warning in vioplot.default(x, ...): Warning: names can only be changed on first call of vioplot (when add = FALSE)

    ## Warning in vioplot.default(x, ...): Warning: main title can only be changed on first call of vioplot (when add = FALSE)

    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Width")

#### Median

The line median option is more suitable for side by side comparisions but the point option is still available also:

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "point", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "point", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
    title(xlab = "Species", ylab = "Sepal Length")
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")

It may be necessary to include a `points` command to fix the median being overwritten by the following plots:

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "point", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "point", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
    points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_large[grep(species, iris_large$Species),]$Sepal.Length))), pch = 21, col = "palevioletred4", bg = "palevioletred2")
    title(xlab = "Species", ylab = "Sepal Length")
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")

Similarly points could be added where a line has been used previously:

    vioplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right", pchMed = 21, colMed = "palevioletred4", colMed2 = "palevioletred2")
    vioplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", pchMed = 21, colMed = "lightblue4", colMed2 = "lightblue2", add = T)
    points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_large[grep(species, iris_large$Species),]$Sepal.Length))), pch = 21, col = "palevioletred4", bg = "palevioletred2")
    points(1:length(levels(iris$Species)), as.numeric(sapply(levels(iris$Species), function(species) median(iris_small[grep(species, iris_small$Species),]$Sepal.Length))), pch = 21, col = "lightblue4", bg = "lightblue2")
    title(xlab = "Species", ylab = "Sepal Length")
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")

Here it is aesthetically pleasing and intuitive to interpret categorical differences in mean and variation in a continuous variable.

#### Sources

These extensions to `vioplot` here are based on those provided here:

- https://gist.github.com/mbjoseph/5852613

These have previously been discussed on the following sites:

- https://mbjoseph.github.io/posts/2018-12-23-split-violin-plots/

- http://tagteam.harvard.edu/hub_feeds/1981/feed_items/209875

- https://www.r-bloggers.com/split-violin-plots/