Tableau Pills: Continuous and Discrete Data Roles


Tableau Pills: Continuous and Discrete Data Roles

by Michael Treadwell

This is the second in a three part series related to the four types of pills in Tableau. As mentioned previously, the four data types are discrete dimension, continuous dimension, discrete measure, and continuous measure. The first part of this series discussed the differences between dimensions and measures. This article will focus on the difference between the two data roles: continuous and discrete. Its purpose is to provide an understanding of data roles from a business perspective.


If you think back to math class, you might remember the counting numbers. These are the numbers we count and zero. Discrete variables behave in a similar way in that they can only take on certain values within a range. Between zero and ten, there are eleven discrete values for variables. There will be no 2.3 or 5.78 because we don’t count these numbers when counting to 10. Discrete variables can be binned because they take on a finite number of values.

In my previous post, I noted that the most common Tableau pill types were discrete dimensions and continuous measures. Discrete dimensions are common because dimensions are typically values like Customer Name or Row ID or State. These are discrete values. There are no names halfway between Michael and Michelle. Similarly, there is not a row between rows 3 and 4 in your crosstab. Discrete values are shown as blue pills on the shelves and blue icons in the data window.


Think back one more time to math class (last time, I promise) and try to remember the real numbers. These numbers can take on any value. In opposition to the counting numbers mentioned before, there are an infinite number of real numbers between zero and ten. Continuous variables behave in a similar way in that they can take on any value. These are usually variables such as Unit Price, Profit, or Order Quantity. In the real-world, there are limits to Order Quantity such as production limits. In theory, there is no limit to the amount a customer can purchase. This is in opposition to a discrete value like Product Category where no matter how many products are purchased, they will each fall into specific categories. Each order in your transaction database could potentially have a different quantity, but there will be a finite number of product categories.

The second common Tableau pill type I mentioned was continuous measure. Continuous measures are common because measures are generally numeric measurements such as Gross Profit, Shipping Cost or Inventory. These are continuous values because they do not fall into distinct categories. A Gross Profit variable could be any monetary value, positive or negative, rather than a few distinct classes. Also notice that continuous variables are not usually binned.

Icons and Pills Excerpt from 'Tableau Your Data!'

Above: An excerpt from ‘Tableau Your Data!’ concerning Tableau Pills

An Example Using Date

Date is a variable that can be both continuous and discrete. Let’s say we have a database of transactional data. We could examine this data by looking at aggregate sales in separate quarters, months or days of the week using date as a discrete variable. If we were looking at quarters, sales in Q1 of 2010 would be grouped with Q1 of 2011. Each sale would be binned into its appropriate category and the chart would default to a bar chart. We could also use date as a continuous variable. In this case each year, quarter, month, day, hour, etc. is a distinct value in a long continuous timeline. Dates from Q1 of 2010 are not grouped with dates from Q1 2011 because these two values are distinct. Each date value is continuous down to the shortest measurement of time available, and the chart would default to a line graph.

I’ve only just touched on Tableau Pills. If you want to learn more, you should check out ‘Tableau Your Data!’ by InterWorks’ own, Dan Murray.

More About the Author

Michael Treadwell

Data Lead
Introducing the Snowflake Data Cloud: Data Science When you think of data science (for the purposes of this blog, this will encompass all machine learning and AI activities), you may ...
The Migratory Patterns of the Common Alteryx Workflow Prior to Alteryx Server version 2018.4, migrating workflows was a three-step process: Deny the problem exists Procrastinate Acquiesce ...

See more from this author →

InterWorks uses cookies to allow us to better understand how the site is used. By continuing to use this site, you consent to this policy. Review Policy OK


Interworks GmbH
Ratinger Straße 9
40213 Düsseldorf
Geschäftsführer: Mel Stephenson

Telefon: +49 (0)211 5408 5301

Amtsgericht Düsseldorf HRB 79752
UstldNr: DE 313 353 072


Love our blog? You should see our emails. Sign up for our newsletter!