Dimension and Measure in Tableau
Tableau supports seven different data types and it tableau automatically assigns a data type and role to each column.
In this article, we will discuss one of the important concepts of Tableau: Dimension and Measure (blue vs green) in complete detail.
Introduction
In this article, we will discuss Dimension and Measure in Tableau.
Tableau support 7 distinct data types: string, number, dates, date and time, geographical, Boolean and mixed.
It is smart enough to look at your data and automatically classify them either into Dimension or Measure in the data pane.
Tableau dimensions are typically text fields and dates while tableau measures are typically numerical.
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Table of Content
Tableau Dimension vs Tableau Measures
Data fields are made from the columns in the data source.
Tableau automatically assigns a data type ( string, number, dates, date and time, geographical, Boolean and mixed) and role: Discrete dimension or continuous measurement or vice versa to all the data fields.
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Dimension
- Contain qualitative values (names, dates etc.)
- Use to categorize, segment, and reveal data in the dataset
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Measure
- Contain numeric values that can be measured
- Measures can be aggregated such as sum, average
- When the field is dragged into the view, tableau applies aggregation to that field
Note: We can switch from dimension to measure or measure to a dimension but both processes have a distinct effect on data visualization.
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Letβs understand dimension and measure by an example on the Tableau
This is a sample dataset (sample superstore) provided by the tableau which has different fields.
Tableau automatically arranges the data fields into dimensions and measures.
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Blue vs Green
Tableau represents the data differently in the View depending upon whether the data is discrete or continuous.
1. Measures areas will initially be continuous when you will add them to view with a green background.
2. Dimension areas will initially be discrete when you will add them to view with a blue background.
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Continuous Data:
It is numerical data that refers to the unspecified number of possible measurements between two points.
It typically involves fluctuating numbers between two predefined values.
Example:
- The temperature of a freezer
- Daily wind speed
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Discrete Data:
Data that can only take certain values are discrete data. It only contains finite values.
Discrete data typically involves counting rather than measuring.
Example:
- Number of the computer systems in Shiksha Online team
- Gender of Person
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Conclusion
In this article, we discussed one of the important concepts of Tableau: Dimension and Measures.
Hope this article will help you in your data analyst journey.
This article is a part of a complete tutorial of Tableau, focusing on Tableau certified data analysts and tableau desktop specialists.
Through a series of articles, we will cover all the topics in-depth with examples.
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FAQs
What is the difference between Dimension and Measure in Tableau?
Dimension: 1.Contain qualitative values (names, dates etc.) 2.Use to categorize, segment, and reveal data in the dataset Measure: 1.Contain numeric values that can be measured 2.Measures can be aggregated such as sum, average 3.When the field is dragged into the view, tableau applies aggregation to that field
How do you show dimension and measure in Tableau?
Tableau represents the data differently in the View depending upon whether the data is discrete or continuous. 1. Measures areas will initially be continuous when you will add them to view with a green background. 2. Dimension areas will initially be discrete when you will add them to view with a blue background.
What is Continuous and Discrete data in Tableau?
Continuous 1. It is numerical data that refers to the unspecified number of possible measurements between two points. 2. It typically involves fluctuating numbers between two predefined values. Discrete 1. Data that can only take certain values are discrete data. It only contains finite values. 2. Discrete data typically involves counting rather than measuring.
Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio