Text Analysis & GenerationData VisualizationAnalytics

Data Visualization Recommender

Returns a structured, single-chart recommendation for your data and goal, including concise encodings, data-prep, rationale, alternatives, pitfalls, and assumptions when information is missing.

Prompt Content

Recommend the single best chart type to visualize the described data and relationship, then give concise design guidance. 1) Review DATA_DESCRIPTION and INSIGHT_GOAL. 2) Select one primary chart from common types (bar, grouped/stacked bar, line, area, scatter, bubble, histogram, box plot, heatmap, treemap, pie/donut, waterfall, slope, dot plot, map variants, gantt, funnel, violin). If none fit, choose the closest standard. 3) Return exactly this structure: - Primary chart: <type> - Why: <1-2 sentences> - Encoding: x=..., y=..., color=..., size=..., shape=..., facet=... (only what applies) - Data prep: <aggregation/binning/sorting as needed> - Alternatives: <up to 2 types with when-to-use> - Pitfalls: <brief do/avoid> 4) If information is missing, state brief assumptions. • Be decisive and concise. • Prefer: bars for categorical comparisons; lines for time trends; scatter for relationships; hist/box/violin for distributions; pie/donut only for few parts; maps only with geographic fields; treemap for many categories; heatmap for two categorical axes. • Avoid: 3D effects, excessive slices (>6) in pie/donut, unnecessary dual y-axes. <example> DATA_DESCRIPTION: 24 months of monthly revenue by region (month: datetime; region: category; revenue: numeric). INSIGHT_GOAL: Show trend over time and compare regions. Expected Primary chart: Multi-series line chart; Encoding: x=month, y=revenue, color=region. </example> DATA_DESCRIPTION: <data-description> Data Description </data-description> INSIGHT_GOAL: <insight-goal> Insight Goal </insight-goal>

Variables

Data Description: Describe fields and their types (categorical, numeric, datetime), record grain, key measures/dimensions, volume, and any sample values.; Example: Orders table with order_date (datetime, daily), product_category (categorical, 8), region (categorical, 5), revenue (numeric), quantity (numeric). ~50k rows over 2 years.
Insight Goal: The question or message to convey (compare categories, show trend, distribution, relationship, part-to-whole, ranking, change attribution, geographic pattern).; Example: Compare quarterly revenue trends by region and highlight seasonality.