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Tableau Chart Types & Visualization Selection

Choosing the right chart type is a core BI skill. Each visualization encodes data attributes differently, and the choice should be driven by the specific question being answered. Anscombe's Quartet demonstrates why: four datasets with identical statistics produce completely different visual shapes.

Key Facts

  • Visualization = set of encoding methods mapped to data
  • For precision (exact values): use length and position on axes
  • For separation (grouping): use color, volume, size
  • Rule: understand what question the user is asking, then choose the chart that answers it
  • Selfmade visualizations = internal analysis (Jupyter, matplotlib); Premade = communication to users (dashboards, presentations)

Patterns

Data Encoding Attributes

Main encoding attributes ranked by precision: 1. Position on common scale (most precise) 2. Length 3. Angle/slope 4. Area 5. Color intensity 6. Color hue (least precise for quantitative data)

Chart Type Selection Guide

Chart Best For Notes
Bar/Column Comparisons between categories Horizontal bars for long labels
Line Trends over time Connect only continuous data
Pie/Donut Part-to-whole Use sparingly, max 5 slices
Matrix/Pivot Cross-tabulation When precise numbers matter
Card Single KPI number Dashboard header factoids
Table Raw tabular data Detail drill-down
Treemap Hierarchical part-to-whole with size Better than pie for many categories
Map/Filled Map Geographic data Check color interpolation
Gauge Progress to goal Single-metric target tracking
Scatter Correlation between two measures Add trend line for clarity
Heatmap Pattern density across two dimensions Sequential color scale

Visualization Categories

Selfmade (for internal analysis): - Business analytics: quick data exploration (Jupyter + matplotlib/seaborn) - Scientific visualization: physical processes, 3D - Search/concept visualization: relationships and principles

Premade (to communicate to users): - Dashboards and presentations: interactive business performance panels - Entity cards / personal accounts: banking-style views (KPI factoids + sparklines) - Data analysis tools: user explores data themselves via interactivity - Infographics and journalism: attention-grabbing, design-heavy

Sparklines and KPI Factoids

  • KPI factoid: single number with label, shows current state (e.g., "Sales $5.7M")
  • Sparkline: tiny inline chart showing trend without axes, placed next to factoid
  • Both placed in upper-left area for maximum attention and quick state assessment

Dashboard Actions (Interactivity)

Action Description
Filter Click element -> filter other charts (most common)
Highlight Click -> highlight related data without filtering
Parameter User action -> change parameter value
Set User action -> add/remove from set
GoToSheet Navigate to another worksheet/dashboard
URL Open URL with dynamic parameters

URL action example: 

https://crm.example.com/deal?object=<object_number>

Gotchas

  • Anscombe's Quartet: always visualize data before trusting summary statistics - four datasets with identical mean, variance, correlation, and regression line look completely different
  • Pie charts with more than 5 slices become unreadable - use bar chart or treemap instead
  • Large colorful objects attract attention regardless of position (contrast overrides F-pattern reading)
  • Incorrect color interpolation in heatmaps and maps makes charts misleading - always verify
  • Each worksheet generates one query to the source - more charts = more queries = slower dashboard

See Also