Shape encoding in Tableau - Step-by-Step Guide

Introduction

Shape encoding lets you use different shapes to show categories or groups in your data. It helps you quickly see differences or patterns by changing the shape of marks on your chart.

When you want to show different product categories on a scatter plot using shapes instead of colors.

When your chart has many colors and you want to add another way to distinguish groups.

When printing in black and white and colors are not clear, shapes help separate data points.

When you want to highlight specific customer segments with unique shapes on a map.

When you want to add a legend that explains groups by shape for easier understanding.

Steps

Step 1: Open your Tableau workbook and go to the worksheet with your chart

- Tableau workspace

You see your current chart with data points or marks

Step 2: Drag the dimension you want to use for shape encoding

- Data pane → drag dimension to the Shape button on the Marks card

The marks on the chart change shapes based on the dimension's categories

💡 Use a dimension with a few distinct categories for clear shape differences

Step 3: Click the Shape button on the Marks card

- Marks card → Shape

The Edit Shape palette opens showing available shapes

Step 4: Select a shape set from the drop-down menu

- Edit Shape palette → Shape Set drop-down

The shapes in the palette update to the selected set

Step 5: Click each category and assign a shape

- Edit Shape palette → click category → select shape

Each category is assigned a unique shape on the chart

💡 Choose simple, distinct shapes for easy recognition

Step 6: Click OK to apply the shape assignments

- Edit Shape palette → OK button

The chart updates with the new shapes and a shape legend appears

Before vs After

Before

All data points on the scatter plot are circles of the same shape regardless of category

After

Data points change to different shapes like squares, triangles, and diamonds based on their category, with a shape legend visible

Settings Reference

Shape encoding

📍 Marks card → Shape button

Assign different shapes to data categories to visually separate groups

Default: Automatic shape assignment by Tableau

Edit Shape palette

📍 Marks card → Shape → Edit Shape

Customize which shapes represent each category

Default: Basic Shapes

Common Mistakes

Using a dimension with too many categories for shape encoding

Too many shapes make the chart confusing and hard to read

Choose a dimension with fewer categories or group categories before assigning shapes

Not assigning distinct shapes manually after dragging the dimension

Tableau may assign similar shapes that are hard to distinguish

Open the Edit Shape palette and assign clear, different shapes to each category

Summary

Shape encoding uses different shapes to show categories in your data visually.

It helps separate groups when colors alone are not enough or not suitable.

Choose dimensions with few categories and assign clear shapes for best results.

Practice

1. What is the main purpose of using Shape encoding in Tableau visualizations?

easy

A. To change the color of the chart background

B. To sort data in ascending order

C. To represent different categories using distinct shapes

D. To filter data based on date

Solution

1. Step 1: Understand what shape encoding does

   Shape encoding assigns different shapes to data categories to visually separate them.

2. Step 2: Compare options to the purpose

   Only To represent different categories using distinct shapes describes using shapes for categories, others describe unrelated functions.

3. Final Answer:

   To represent different categories using distinct shapes -> Option C

4. Quick Check:

   Shape encoding = Different shapes for categories [OK]

Hint: Shapes show categories visually, not colors or sorting [OK]

Common Mistakes:

• Confusing shape encoding with color encoding
• Thinking shape encoding sorts or filters data
• Assuming shape encoding changes chart background

2. Which of the following is the correct way to apply shape encoding in Tableau?

easy

A. Drag a dimension to the Shape mark on the Marks card

B. Drag a dimension to the Color mark on the Marks card

C. Drag a measure to the Shape mark on the Marks card

D. Drag a measure to the Size mark on the Marks card

Solution

1. Step 1: Identify what type of field controls shape encoding

   Shape encoding uses categories, which are dimensions, not measures.

2. Step 2: Match the correct mark for shape encoding

   Shapes are assigned by dragging a dimension to the Shape mark on the Marks card.

3. Final Answer:

   Drag a dimension to the Shape mark on the Marks card -> Option A

4. Quick Check:

   Dimension + Shape mark = Shape encoding [OK]

Hint: Drag dimensions, not measures, to Shape mark [OK]

Common Mistakes:

• Dragging measures instead of dimensions to Shape
• Using Color mark instead of Shape mark
• Confusing Size mark with Shape mark

3. Given a dataset with a dimension 'Product Category' having values 'A', 'B', and 'C', and you drag this dimension to the Shape mark, what will Tableau do by default?

medium

A. Assign three different shapes, one for each category

B. Assign the same shape to all categories

C. Show a color legend instead of shapes

D. Create a filter to select categories

Solution

1. Step 1: Understand Tableau's default behavior with shape encoding

   When a dimension with multiple categories is placed on Shape, Tableau assigns a unique shape to each category.

2. Step 2: Evaluate the options

   Assign three different shapes, one for each category matches this behavior; others describe unrelated or incorrect outcomes.

3. Final Answer:

   Assign three different shapes, one for each category -> Option A

4. Quick Check:

   Dimension categories = unique shapes [OK]

Hint: Each category gets a unique shape by default [OK]

Common Mistakes:

• Expecting same shape for all categories
• Confusing shape legend with color legend
• Thinking shape encoding creates filters

4. You dragged a dimension to the Shape mark, but all data points show the same shape. What is the most likely reason?

medium

A. The data source is not connected

B. The dimension has only one unique value

C. The Shape mark is disabled in Tableau

D. You dragged a measure instead of a dimension

Solution

1. Step 1: Check the dimension's uniqueness

   If the dimension has only one unique value, Tableau assigns the same shape to all points.

2. Step 2: Consider other options

   Dragging a measure would not assign shapes properly, but the question states a dimension was dragged. Shape mark cannot be disabled, and data connection issues would prevent visualization.

3. Final Answer:

   The dimension has only one unique value -> Option B

4. Quick Check:

   Single unique value = same shape [OK]

Hint: Check if dimension has multiple unique values [OK]

Common Mistakes:

• Assuming Shape mark can be disabled
• Confusing dimension with measure
• Ignoring data connection status

5. You want to create a scatter plot showing sales by profit, and use shape encoding to distinguish regions. However, some regions have too many categories, making shapes hard to distinguish. What is the best approach to improve clarity?

hard

A. Increase the size of all shapes to make them bigger

B. Use shape encoding on the sales measure instead of regions

C. Remove the shape encoding and use only color encoding

D. Group similar regions into fewer categories before applying shape encoding

Solution

1. Step 1: Identify the problem with too many shape categories

   Too many shapes make it hard to distinguish categories visually.

2. Step 2: Find the best solution to reduce complexity

   Grouping similar regions reduces the number of categories, making shapes clearer.

3. Step 3: Evaluate other options

   Using shape on a measure or removing shape encoding loses category distinction; increasing size doesn't solve shape confusion.

4. Final Answer:

   Group similar regions into fewer categories before applying shape encoding -> Option D

5. Quick Check:

   Reduce categories for clear shapes [OK]

Hint: Group categories to reduce shape variety [OK]

Common Mistakes:

• Applying shape encoding to measures
• Relying on size instead of reducing categories
• Removing shape encoding without replacement