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Violin plot
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From Wikipedia, the free encyclopedia
Method of plotting numeric data
Example of a violin plot<br>Example of a violin plot in a scientific publication in PLOS Pathogens.<br>A violin plot (also known as a bean plot ) is a statistical graphic for comparing probability distributions. It is similar to a box plot, but has enhanced information with the addition of a rotated kernel density plot on each side.[1]
History<br>[edit]
The violin plot was proposed in 1997 by Jerry L. Hintze and Ray D. Nelson as a way to display even more information than box plots, which were created by John Tukey in 1977.[2] The name comes from the plot's alleged resemblance to a violin.[2]
Description<br>[edit]
Violin plots are similar to box plots, except that they also show the probability density of the data at different values, usually smoothed by a kernel density estimator. A violin plot will include all the data that is in a box plot: a marker for the median of the data; a box or marker indicating the interquartile range; and possibly all sample points, if the number of samples is not too high.
While a box plot shows a summary statistics such as median and interquartile ranges, the violin plot shows the full distribution of the data. The violin plot can be used in multimodal data (more than one peak). In this case a violin plot shows the presence of different peaks, their position and relative amplitude.
Like box plots, violin plots are used to represent comparison of a variable distribution (or sample distribution) across different "categories" (for example, temperature distribution compared between day and night, or distribution of car prices compared across different car makers).
A violin plot can have multiple layers. For instance, the outer shape represents all possible results. The next layer inside might represent the values that occur 95% of the time. The next layer (if it exists) inside might represent the values that occur 50% of the time.
Violin plots are less popular than box plots. Violin plots may be harder to understand for readers not familiar with them. In this case, a more accessible alternative is to plot a series of stacked histograms or kernel density plots.
The original meaning of "violin plot" was a combination of a box plot and a two-sided kernel density plot.[1] However, currently "violin plots" are sometimes understood just as two-sided kernel density plots, without a box plot or any other elements.[3][4]
See also<br>[edit]
Sina plot
Box plot
References<br>[edit]
1 2 "Violin Plot". NIST DataPlot. National Institute of Standards and Technology. 2015-10-13.
1 2 Hintze, Jerry L.; Nelson, Ray D. (May 1998). "Violin Plots: A Box Plot-Density Trace Synergism". The American Statistician. 52 (2): 181–184. doi:10.1080/00031305.1998.10480559. ISSN 0003-1305.
↑ Wilke, Claus O. Fundamentals of Data Visualization.
↑ "Violin plot — geom_violin". ggplot2.tidyverse.org. Retrieved 2023-11-19.
External links<br>[edit]
Wikimedia Commons has media related to Violin plots.
Vioplot add-in for Stata
Violinplot from a wide-form dataset with the seaborn statistical visualization library based on matplotlib
This article incorporates public domain material from Dataplot reference manual: Violin plot. National Institute of Standards and Technology.
Statistics
Outline
Index
Descriptive statistics
Continuous data<br>Center<br>Mean<br>Arithmetic
Arithmetic-Geometric
Contraharmonic
Cubic
Generalized/power
Geometric
Harmonic
Heronian
Heinz
Lehmer
Median
Mode
Dispersion<br>Average absolute deviation
Coefficient of variation
Interquartile range
Percentile
Range
Standard deviation
Variance
Shape<br>Central limit theorem
Moments<br>Kurtosis
L-moments
Skewness
Count data<br>Index of dispersion
Summary tables<br>Contingency table
Frequency distribution
Grouped data
Dependence<br>Partial correlation
Pearson product-moment correlation
Rank correlation<br>Kendall's τ
Spearman's ρ
Scatter plot
Graphics<br>Bar chart
Biplot
Box plot
Control chart
Correlogram
Fan chart
Forest plot
Histogram
Pie chart
Q–Q plot
Radar chart
Run chart
Scatter plot
Stem-and-leaf display
Violin plot
Heatmap
Scatter Plot Matrix
ECDF plot
Line chart
Statistical data processing
Transformations<br>Data transformation
Log transformation
Power transform<br>Box–Cox transformation
Yeo–Johnson transformation
Variance-stabilizing transformation
Anscombe transform
Fisher transformation
Scaling and normalization<br>Feature scaling
Normalization
Standardization (z-score)
Min–max normalization
Unit vector normalization
Data cleaning<br>Data cleaning
Outlier
Winsorizing
Truncation
Missing data
Data reduction<br>Dimensionality reduction
Principal component analysis
Factor...