Violin plot
A violin plot is a statistical visualization that represents the distribution of numeric data with a mirrored, smoothed density shape, often accompanied by summary statistics.
D3.js in Action, Third Edition
A violin plot is a distribution chart that uses a mirrored, smoothed density shape to show where numeric observations are concentrated.
- How it is used
- Compares salary distributions across data-visualization roles while retaining their overall shapes.
- How deep it goes
- Builds violins step by step from filtered values, bins, scales, histogram bars, smoothed areas, and summary markers.
- Examples
- Uses salary distributions for analyst, developer, designer, scientist, and leadership roles.
- What you can do after
- Build it from scratch with D3 by converting category bins into mirrored smoothed areas and adding summary markers.
Read how D3.js in Action, Third Edition explains violin plot →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A violin plot visualizes the distribution and density of numeric values, often alongside summary statistics such as the median.
- How it is used
- Interprets how model features influence predictions through SHAP-value distributions, importance, direction, and feature-value associations.
- How deep it goes
- Explains width, shape, median, sign, color, and outliers, but does not develop the underlying density estimation.
- Examples
- Uses accommodation-price features such as availability, coordinates, bedrooms, and beds to illustrate prediction effects.
- What you can do after
- Apply it via SHAP to inspect which features matter and whether their values raise or lower predictions.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains violin plot →
Statistics Slam Dunk
A violin plot displays the distribution and shape of numeric data by combining a mirrored probability-density shape with optional summary statistics.
- How it is used
- Uses continuous-data distributions to compare shape, frequency, and summary statistics in practical analyses.
- How deep it goes
- Explains density shape, embedded summaries, and skewness, then demonstrates ggpubr construction without developing the density calculation itself.
- Examples
- Demonstrates games played and games won for current and past NBA franchises from 1946 through 2020.
- What you can do after
- Builds distribution graphics in R with ggpubr, adds boxplots and mean markers, and interprets density shape and skewness.
Related concepts
- box-and-whisker plotcontrasts with · A box-and-whisker plot is a statistical graphic that summarizes a numerical distribution using quartiles, a median, and whiskers or markers indicating spread and potential outliers.
- histogramalternative to · A histogram is a representation that groups observations into bins or value ranges and records their frequencies or other distribution summaries.
- density plotuses · A density plot is a visualization that estimates and displays the shape of a continuous variable’s probability distribution as a smooth curve.
- probability densityuses · A probability density function is a nonnegative function describing the distribution of a continuous random variable, whose integral over a region gives the probability of values in that region.
- meanuses · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
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