Violin plot

Violin plot

Violinplot-hiv-paper-plot-pathogens

Violin plot is a method of plotting numeric data and can be understood as a combination of a box plot and a kernel density plot. It provides a way to visualize the distribution of the data, showing the probability density of the data at different values. Violin plots are similar to box plots, but they also include a rotated kernel density plot on each side, which gives a better indication of the distribution of the data. They are particularly useful for comparing the distribution of data across several levels of a categorical variable.

Overview

A violin plot features a kernel density estimation of the underlying distribution of the data. The plot is mirrored and rotated, forming a shape that resembles a violin. The width of the plot at different values indicates the density of the data at that value, with wider sections representing higher density (more data points). This makes it easier to see the distribution of the data, especially when comparing multiple groups.

The central mark in a violin plot is a median of the data, and sometimes it also includes a box plot inside, which shows the interquartile range. The combination of box plot and kernel density plot allows viewers to understand the central tendency, variability, and distribution of the data.

Usage

Violin plots are used in exploratory data analysis to visualize the distribution of the data and any potential multimodality. They are particularly helpful when the data distribution is multimodal (where multiple peaks are present). Violin plots can also be used to compare the distribution of data across different categories.

Advantages

• Comprehensive Data Visualization: Violin plots provide a more comprehensive visualization of the data distribution compared to box plots.
• Comparison Across Groups: They are useful for comparing the distribution of data across different categories or groups.
• Identification of Modality: Violin plots can help in identifying whether the data is multimodal.

Disadvantages

• Complexity: For those unfamiliar with violin plots, they can be more complex to interpret compared to simpler plots like box plots.
• Density Estimation: The kernel density estimation process can be influenced by the choice of bandwidth, potentially affecting the appearance of the plot.

Creating Violin Plots

Violin plots can be created using various software and programming languages that support statistical data visualization, such as R, Python (with libraries like Matplotlib, Seaborn), and others. The process generally involves specifying the data points and the categorical variable for comparison.

Examples

An example of a violin plot might involve comparing the distribution of exam scores across different classrooms. Each violin plot would represent a classroom, with the shape of the plot indicating the distribution of scores within that classroom. This could quickly show which classroom has the most variation in scores or if any classroom's scores are skewed towards high or low ends.

See Also

• Box plot
• Kernel density estimation
• Data visualization
• Exploratory data analysis

‎