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6 min read

How to Use Sets for Data Analysis

Master the use of sets for advanced data analysis in Tableau

What are Sets?

Sets divide data into two parts:

  • IN the set
  • OUT of the set

Example: "Top 10 Customers" set

  • IN: Top 10 customers
  • OUT: All other customers

Sets vs Groups

SetsGroups
IN or OUTMultiple categories
Dynamic or staticAlways static
Two partsMany parts

Creating Sets

Method 1: From Visualization

  1. Select marks on chart
  2. Right-click
  3. Select "Create Set"
  4. Name it and click OK

Method 2: From Data Pane

  1. Right-click a dimension
  2. Select "Create" → "Set"
  3. Choose type (Manual, Condition, Top)
  4. Click OK

Types of Sets

1. Manual Set

You choose specific values:

  • Select "New York", "Los Angeles", "Chicago"
  • These are IN the set

2. Condition Set

Based on a rule:

  • Customers where Sales > $10,000
  • Products where Profit > 0

3. Top N Set

Based on ranking:

  • Top 10 Products by Sales
  • Bottom 5 Customers by Orders

Using Sets

In Filter

  1. Drag set to Filters
  2. Choose "In" or "Out"

In Color

  1. Drag set to Color
  2. IN = One color, OUT = Another

In Calculations

IF [My Set] THEN "VIP" ELSE "Regular" END

Example: Top 10 Products

  1. Right-click Product Name
  2. Create → Set
  3. Select "Top" tab
  4. Set: Top 10 by Sales
  5. Click OK

Now use this set to highlight top performers!

Combined Sets

Combine two sets:

  1. Select two sets (Ctrl+Click)
  2. Right-click → "Create Combined Set"
  3. Choose: All members, Shared members, etc.

Dynamic Sets

Sets with conditions update automatically:

  • "Customers with Sales > $10K" updates when data changes
  • No manual updating needed!

Use Cases

  • Top/Bottom performers
  • VIP customers
  • High-profit products
  • Regional focus
  • Outlier detection

Tips

  1. Name sets clearly
  2. Use conditions for dynamic sets
  3. Combine sets for complex analysis
  4. Use in/out for highlighting

Summary

Sets split data into IN and OUT. Use manual selection, conditions, or Top N. Great for focusing on specific data subsets!