What this helps you see
Show weighted transitions from source categories to target categories.
A practical use case
Show transitions between named stages, ensuring that similarly named categories at different stages remain identifiable.
Free example data and exact column mapping
The figure uses the downloadable CSV with the exact column mapping below. Public snapshots retain their data notes and transformations. Constructed teaching scenarios are identified in the method notes; they are not customer results. Editorial layouts may combine comparisons from the same records.
The free example contains 27 observations and these columns: Source, Target, Value. Keep the header row and preserve one observation per row. The table below shows the first records; the CSV retains the full example.
Constructed teaching scenario. Common unit and reporting period; conservation checked at every internal node.
| Source | Target | Value |
|---|---|---|
| Coal | Turbines | 70 |
| Coal | Heat | 20 |
| Gas | Turbines | 30 |
| Gas | Heat | 10 |
| Solar | Inverters | 30 |
| Turbines | Grid | 80 |
How to use it in Datamon
- Sign in with Google, create a visualization and import the free CSV in Data. Check numeric columns, date order, decimal separator and missing values against the example table.
- In Visualize, choose Alluvial diagram. Connect the required columns exactly as listed below, then inspect at least two marks against the data. Optional roles are available only when the chosen type exposes them.
- Keep the observation unit and reporting period explicit. Select the figure size before adjusting text, and check the chart at the size your reader will see.
Column mapping
- Weight
- Value
- Source
- Source
- Target
- Target
How to read the example
Compare positions and values with the table, then inspect the legend or size encoding where used. A visual pattern is a description of the displayed observations. It does not by itself identify a cause, justify extrapolation, or repair missing data.
Refine and export the result
In Refine, edit the title, subtitle, axis descriptions, source and supported series settings. Use color to identify a comparison rather than to decorate every mark. Export SVG, PNG, JPEG, PDF or PowerPoint and inspect the actual file; save an editable project backup for revisions.
Limits and common mistakes
Use one row per source–target connection with a clearly defined numeric weight. Check repeated links, node naming, direction and reporting period. Conserve quantities only where conservation is meaningful, and document losses or additions instead of inventing balancing values.
This visualization is experimental in Datamon. Its current renderer and input contract are shown here, but validate the result with your own data before using it in a report. Specialized settings can differ from the stable chart types.
What is free and what needs an account?
Reading this guide and downloading its CSV and chart images are free. Datamon currently has no paid checkout or subscription plans in this deployment. Creating and saving an editable visualization requires Google sign-in. Provider-backed AI, cloud analysis, publishing and video depend on configured services; this guide does not promise unlimited free connected services.
Questions before you start
Can I visualize alluvial diagram with a free example?
Yes. The CSV and preview images on this page are free to download. Follow the field mapping and steps above. Sign in to create and save the editable project; connected-service availability is separate.
Can I replace the example with my own data?
Yes. Preserve the observation unit and the required numeric, category or geographic fields. Recompute any derived values, update the unit and source, and compare the output with known rows before sharing.
Sources and further reading
Report a correction or an access barrier: Email the editorial contact.
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