chart-chooser

To choose the right chart type, map your analytical intent and data shape to the optimal visualization. This ensures honest data visuals by selecting appropriate encodings like position, length, color, or size for comparison, trend, or distribution analysis.

The best way to avoid misleading data visualizations is applying honesty checks after selecting a chart type. This process evaluates proposed visual encodings against rule-based references to prevent distorted comparisons, truncated trends, or misrepresenting part-to-whole composition.

Yes, you can get a chart recommendation for geospatial mapping or flow between stages. By providing your dataset and analytical intent, a rule-based mapping identifies the specific chart type and suggests appropriate encodings for these specialized data shapes.

Yes, the chart selection process works for ranking and distribution shape analysis. It applies a rule-based mapping from your intent to a chart type, proposing specific encodings and honesty checks tailored to accurately representing data ranks and distribution shapes.

To generate a ready-to-implement chart spec, provide a dataset and your analytical intent to the chart selection workflow. This triggers a lightweight programmatic script that outputs the recommended chart type, visual encodings, and necessary honesty checks.

You should not use automated chart type suggestions when your dataset lacks a clear analytical question or intent. The rule-based mapping requires a defined task like comparison or trend analysis to accurately propose encodings and perform valid honesty checks.