Data Visualization taste

Source: github.com/f/prompts.chat · Contributor: f

41 minutes agoNovember 9, 2026 at 11:48 PM

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# Data Visualization taste of a clarity-first data visualization developer
- Start from the question the chart must answer, and write that question as the chart title or subtitle. Confidence: 0.90
- Choose the chart by the comparison: bars for categories, lines for trends over time, scatter plots for relationships. Confidence: 0.90
- Start bar chart value axes at zero; line charts may use a tighter range when the change is what matters. Confidence: 0.90
- Avoid pie charts with more than three slices, and never use 3D charts. Confidence: 0.85
- Sort categorical bars by value unless the categories have a natural order like months or ratings. Confidence: 0.85
- Label lines and series directly at the end of the line instead of making readers match colors with a legend. Confidence: 0.80
- Use a sequential palette for ordered data, a diverging palette around a meaningful midpoint, and a categorical palette for groups. Confidence: 0.85
- Keep categorical palettes to about seven colors and check them with a color-blindness simulator. Confidence: 0.85
- Never encode meaning with color alone; add labels, patterns, shapes or positions as well. Confidence: 0.90
- Remove chart junk: heavy gridlines, borders, shadows and decorative backgrounds. Confidence: 0.85
- Format numbers for humans with thousands separators, sensible rounding and units such as 1.2M or 35 percent. Confidence: 0.85
- Show uncertainty with confidence bands or error bars when the data comes from samples or forecasts. Confidence: 0.75
- Use a declarative library such as Vega-Lite, Observable Plot or ECharts for standard charts, and reach for D3 only for custom visuals. Confidence: 0.80
- Render with SVG for small and medium datasets, and switch to Canvas or WebGL above roughly ten thousand marks. Confidence: 0.75
- Make charts responsive by redesigning for small screens, for example fewer ticks or a horizontal bar layout, not just scaling down. Confidence: 0.80
- Provide a text summary and an accessible data table for every important chart. Confidence: 0.85
- Make tooltips keyboard accessible, and never hide key information only in hover states. Confidence: 0.80
- Aggregate or sample data on the server before sending it to the browser, rather than plotting millions of raw points. Confidence: 0.85
- Keep the same color for the same category across every chart in a dashboard. Confidence: 0.90
- Annotate notable events, like a release or an outage, directly on time series charts. Confidence: 0.75
- Show the data source and the last refresh time under each chart. Confidence: 0.85
- Test charts with real and edge-case data: empty series, single points, very long labels and negative values. Confidence: 0.80

# Data Visualization taste of a clarity-first data visualization developer - Start from the question the chart must answer, and write that question as the chart title or subtitle. Confidence: 0.90 - Choose the chart by the comparison: bars for categories, lines for trends over time, scatter plots for relationships. Confidence: 0.90 - Start bar chart value axes at zero; line charts may use a tighter range when the change is what matters. Confidence: 0.90 - Avoid pie charts with more than three slices, and never use 3D charts. Confidence: 0.85 - Sort categorical bars by value unless the categories have a natural order like months or ratings. Confidence: 0.85 - Label lines and series directly at the end of the line instead of making readers match colors with a legend. Confidence: 0.80 - Use a sequential palette for ordered data, a diverging palette around a meaningful midpoint, and a categorical palette for groups. Confidence: 0.85 - Keep categorical palettes to about seven colors and check them with a color-blindness simulator. Confidence: 0.85 - Never encode meaning with color alone; add labels, patterns, shapes or positions as well. Confidence: 0.90 - Remove chart junk: heavy gridlines, borders, shadows and decorative backgrounds. Confidence: 0.85 - Format numbers for humans with thousands separators, sensible rounding and units such as 1.2M or 35 percent. Confidence: 0.85 - Show uncertainty with confidence bands or error bars when the data comes from samples or forecasts. Confidence: 0.75 - Use a declarative library such as Vega-Lite, Observable Plot or ECharts for standard charts, and reach for D3 only for custom visuals. Confidence: 0.80 - Render with SVG for small and medium datasets, and switch to Canvas or WebGL above roughly ten thousand marks. Confidence: 0.75 - Make charts responsive by redesigning for small screens, for example fewer ticks or a horizontal bar layout, not just scaling down. Confidence: 0.80 - Provide a text summary and an accessible data table for every important chart. Confidence: 0.85 - Make tooltips keyboard accessible, and never hide key information only in hover states. Confidence: 0.80 - Aggregate or sample data on the server before sending it to the browser, rather than plotting millions of raw points. Confidence: 0.85 - Keep the same color for the same category across every chart in a dashboard. Confidence: 0.90 - Annotate notable events, like a release or an outage, directly on time series charts. Confidence: 0.75 - Show the data source and the last refresh time under each chart. Confidence: 0.85 - Test charts with real and edge-case data: empty series, single points, very long labels and negative values. Confidence: 0.80

Data Visualization taste

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Data Visualization taste

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