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Treemap in Tableau

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Treemap in Tableau is a visualization technique that represents hierarchical data using nested rectangles. The size and color of each rectangle represents different dimensions or measurements, allowing users to quickly understand complex data relationships. In Tableau, tree maps are particularly useful for displaying large amounts of hierarchical data in a compact and visually appealing way. They facilitate easy comparison across multiple categories, simplifying the identification of trends, patterns, and anomalies.

When to Use Treemap in Tableau

Visualizing hierarchical data

When working with hierarchical datasets such as organizational structures, product categories, or geographic regions, tree maps clearly show how different segments relate to one another.

Comparing categories

Tree maps are great for comparing categories based on two measures – size and color. For example, you can use the size of the rectangles to represent sales volume and color intensity for profit margins, which helps identify which products or regions are performing well or poorly.

Displaying part-to-whole relationships

Tree maps shine when depicting how individual components contribute to a whole. They can show how different departments contribute to a company’s overall revenue.

Limited space

Tree maps work well in dashboards, where screen space is limited. They pack a lot of information into a small area, making efficient use of the available space compared to traditional charts.

Importance of Treemap in Tableau

Space-efficient representation

By using the area of rectangles to display data, tree maps can display large amounts of data points compactly, making them ideal for dashboards and reports.

Immediate visual impact

The combination of shape and color in a tree map allows viewers to quickly understand complex information, helping to identify key trends, outliers, and areas that need attention.

Facilitate hierarchical insights

Tree maps effectively depict the hierarchical structure of data, helping users understand the relationships between categories and subcategories. This can enhance decision-making by highlighting important segments.

Encourage exploratory analysis

The interactive nature of tree maps in Tableau allows users to dive into categories for more detailed analysis, making it easier to explore data and uncover hidden insights.

Steps to create Treemap in Tableau

Step 1: Open Tableau

Step 2: Click on Text file to connect with Tableau

Step 3: Browse csv file (or other file format as your need) and click on Open

Step 4: Click on Sheet 1 (Worksheet) to make visual

Step 5: Drag-n-drop Sales Amount over Color, Size, Label (Marks card) and Sub Category over Label

Note:

  • Smartphones have the highest sales at 70,000. Other top categories include laptops with sales of 66,000 and sofas with sales of 61,000. Smaller product categories like vegetables (8,500) and fruits (11,000) have very low sales.
  • The size of each block in the treemap represents the total sales of that product sub-category. Larger blocks like smartphones and laptops represent higher sales, while smaller blocks like vegetables represent lower sales.
  • Colour shading helps differentiate different product sub-categories.
  • You can customize the color by clicking on Color (Marks Card)

Conclusion

Tree maps in Tableau are powerful visualization tools for presenting hierarchical data in an easily digestible format. They help users visualize complex data relationships, compare different categories, and understand how individual components contribute to a larger dataset. By combining shape and color to convey multiple dimensions of information, tree maps enable quick and effective analysis of large amounts of data. Whether in dashboards, reports, or exploratory analysis, tree maps enhance data visualization, facilitate informed decision making, and help analysts uncover insights that might otherwise go unnoticed. Incorporating tree maps into your data analysis toolkit can greatly improve your ability to effectively interpret and present complex datasets.

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