Here you can upload the dataset in the .csv format (.csv format only and all variables have to be numeric). The dataset can be uploaded by clicking on the Choose File button. After clicking it the Open window will pop-up and you will have to locate the dataset in .csv format on your computer. After locating the dataset and by clicking open the web page will automatically give you basic information about the dataset and plot the violin plot for all dataset variables all together and show violin plot for each variable separately.
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Violin plots
Violin plots are a type of data visualization that combines aspexts of box plots and density plots to give a more detailed view of the distribution of a dataset. The violin plots are very useful for comparing distributions across different groups or categories.
The main components are density plots, the box plot elements, vertical and horizontal axis.
- Density plots - it is the main feature of the violin plot, which is a smoothed estimate of the distribution of the data. It is displayed as the violin-shaped curve (multiple shaped cruves) that shows where data points are concentrated.
- Box Plot Elements - inside the violin plot the box plot elements can be found such as median (line showing the median of the data), quartiles (25th and 75th percentiles are indicated, sometimes with a box), interquartile range (IQR) (the range between the 25th and 75th percentiles), and outliers (points/samples that fall outside the defined range, often shown as individual samples).
- Vertical axis - represents the values or measurements of the data.
- Horizontal axis - shows different categories or groups beign compared.
The advantages fo violin plots
The advantages of the violin polots are comprehensive view, comparison across groups, and handle multi-modality.
- Comprehensive view - they provide detailed view of the ata distribution than a box plot alone, especially for understanding the diversity and shape of the data.
- Comparison across groups - Multiple violins can be plotted side-by-side to compare distributions across different categories or groups.
- Handle multimodality - They can effectively represent multimodla distributions (i.e. data with multiple peaks).
The limitations of the violin plots
The limitations of violin plots are overlapping, misinterpretation and complexity. The overlapping occurs when multiple violins are plotted. Then overlapping can occur which makes it hard (difficult) to distinguish between them, especially, if there are many categories. The shape of the violin can sometimes be misinterpreted if the bandwidth of the density estimate is not chosen appropriately.
For those who are not familiar with this type of plots it can be more complex to interpret compared to simple plots like histograms or boxplots.
When to use violin plots?
The violin plots are useful for comparing distributions, exploratory data analysis, and visualizing complex distributions. The violin plots are useful when you want to compare the distributions of data across different categories or groups. In exploratory data analysis the violin plots are useful to gain the initial understanding of the distribution density, and potential outliers in your data. In case of complex distributions the violin plots are especially useful when the data has more complexity than can be capture by a simple boxplot.