Dataskera

Violin Plot

The Violin Plot shows true distributions instead of averages: the shape of a measure per category, with an optional box plot, mean marker and observation dots inside. Everywhere an average hides the story, a distribution tells it: salaries by department, delays by carrier, basket sizes by store.

Data Requirements

Your Tableau worksheet should include:

  • 1 Dimension, optional for the Category encoding. One violin per member; leave it empty for a single violin.
  • 1 Measure for the Value encoding. The observed variable.
  • 1 Dimension, optional for the Color encoding. Colors the violins; defaults to the category.

Add Violin Plot to your worksheet

First, download the manifest file. It tells Tableau where to load the extension from.

Download violinplot.trex

Direct link: https://dataskera.com/downloads/violinplot.trex

Then, in Tableau:

  1. On the Marks card, open the Mark type dropdown, and under Viz Extensions choose Add Extension.
  2. In the Add an Extension dialog, choose Access Local Viz Extensions, then open the violinplot.trex file you just downloaded.
  3. When Tableau asks, allow the extension to access the data in the workbook.
  4. The welcome screen appears. Sign in with your Dataskera account, or on Tableau Desktop choose Use on Desktop without an account. See Signing in & access below.
  5. Build the chart: drag a measure onto Value and, usually, a dimension onto Category. Then put a high-cardinality identifier (order id, row id, timestamp) on the built-in Detail shelf, so each summary row is one observation. That step is the whole data contract: a violin needs observations, and a sheet without Detail delivers aggregates.
Tableau Server or Cloud:
A site admin must allow the extension by adding its URL to the site's safe list, and the view needs the Download Summary Data permission. Extension URL: https://vizextension.dataskera.com/violinplot/violinplot.html

Signing in & access

Violin Plot is free. Whether you need to sign in depends only on what you are doing. Viewing a dashboard never asks for an account, and authoring is free too.

Viewing a dashboard
No account

People who only view a dashboard are never asked to sign in and are never blocked.

Editing on Tableau Desktop
Optional

Sign in, or continue without an account in a single click.

Editing on Server or Cloud
Free account

Sign in with your Dataskera account to author on the web.

No account yet? Create one free in under a minute. No credit card required.

Format panel

Open the Format panel from the extension menu to customize the chart. It is for the dashboard author and is not shown to viewers. It has four tabs:

  • Violin: the density computed from exact bin counts (the default, auditable by hand) or smoothed with a gaussian kernel, the bin count or the smoothing strength, equal-area violins or width by observation count, the maximum width, number format and font.
  • Overlays: the inner box plot (median, quartiles, whiskers at 1.5 IQR), the mean marker, and observation dots with a sampling cap.
  • Color: a palette, a precise colour per category, and the overall opacity.
  • Legend: show or hide it, its position around the chart, an optional show/hide toggle for viewers, the colour swatch, and the content of each entry.
  • Tooltip: show or hide it, its colours, decimals, and a template you write yourself from the placeholders the chart provides.
The Detail shelf is the contract:
Summary data only carries what the worksheet's level of detail carries. When the sheet delivers only a few rows per category, the chart teaches this pattern on screen instead of drawing a meaningless shape from aggregates.
Two densities, both honest:
Bin counts are exact: the width of a violin at a height is the number of observations in that range, and the widths sum to the category's n. Smoothed applies a gaussian kernel with Silverman's bandwidth, the standard of statistics packages, scaled by the slider.
Too few observations:
Under five observations a density is fiction: the category shows its dots only. Zero variance draws a thin bar at the value.
Deterministic dots:
The jitter of the observation dots is computed from the data itself, so the same data always draws the same picture, and two screenshots can be compared.