This is the first in a three part series related to the four types of pills in Tableau. The four pill types are **discrete dimension**, **continuous dimension**, **discrete measure**, and **continuous measure**. Grasping these concepts is important to understanding Tableau and relational databases in general. This article will specifically focus on explaining the less intuitive concepts of **non-numeric measures** and **numeric dimensions**. For a more in-depth discussion of the basic properties of dimensions and measures, see the Tableau software online help.

## Measures

Measures are the result of a business process event. Aside from an exception listed later (*), a measure is numeric, quantitative information. Quantitative information, as opposed to qualitative, is information that can be measured in a traditional sense (thus measure). A key feature of measures is that they can be aggregated by summing, averaging, etc.

Looking at the **Superstore Subset Excel data** included in Tableau, we see numeric (indicated by the # icon to the left of the variable name) data in both the **Dimension** and **Measure** section of the data window.

Notice that the numeric measure variables such as **Order Quantity**, **Profit**, and **Unit Price** are all quantities that can be measured by observation. For example, **Profit = Revenue – Cost and Unit Price **is calculated by management based on labor costs, market research, etc.* *Additionally, these are quantities that we would be interested in aggregating; values like **Sum of Sales** and **Average Profit** would be of interest in analysis. These are attributes of measures.

## Dimensions

Dimensions are reference variables that give context to measures. A dimension is numeric or character information that is qualitative. Qualitative, as opposed to quantitative, is information that categorizes and cannot be measured in a traditional sense. Often, dimensions are categories such as Region, Department, or State, but they can also be numeric. We use dimensions to compare measures across different types, also called slicing. A key feature of dimensions is that they partition aggregate measures; e.g. **Sum of Profit by State** or **Average Order Quantity by Region**.

Notice now that numeric dimension variables such as **Customer ID** and **Order ID** are all values that cannot be measured by observation. Given a customer, we cannot calculate the **Customer ID** in a logical way (unless there is a non-random assignment of customer keys, which is poor practice and not within the scope of this article). Also, unlike measure variables, ID is not a number that would be interesting in aggregation. There is no real business application for the average or sum of **Order ID**. These are attributes of dimensions.

## Non-Numeric Measures

(*) The calculated field **Good Quantity** is a string measure. The field was calculated using this formula:

IF SUM([Order Quantity]) > 2000 THEN ‘Good’ ELSE ‘Bad’ END

This creates a string value that corresponds to a specific aggregate value, sum in this case. **Good Quantity** is a label for an aggregate measure that can be partitioned by dimensions such as **State** or **Region** to determine if their order Quantities are **Good** or **Bad**.

However, we could have used this formula instead:

IF [Order Quantity] > 50 THEN ‘Good’ ELSE ‘Bad’ END

Tableau would place this value in the **Dimension** section of the data window because it is a line calculation. Here, **Good** or **Bad** is a quality of a specific observation in the data set. While it is still measured, in a sense, it pertains only to one specific observation and cannot be partitioned by dimensions.