Note: this article covers aggregations used when building a Workbook. If you are looking for how Datasets aggregate and display information from various Tables, see Dataset Aggregations.
When building a Metric, you will be required to select a Measure, which is simply a column of data from a selected Dataset. Most of the time, you don’t want the entire column of data to be displayed in your Workbook, instead, you want Numetric to summarize the data and display it visually.
For example, if you select a column that contains sales totals for all of your transactions, you may not want each of those values displayed, instead, you want Numetric to display the total of all sales, or possibly the average of all sales. This summarization of data is known as an aggregation.
You may also need to select an aggregation method when building a custom Tooltip in a Metric. Because Tooltips only display a single value, you will need to specify what aggregation method you would like to use in the selected measure.
Below is a list of the aggregations that can be carried out in Datasets, as well as a description of how that aggregation handles the aggregated values. It is important to note that the aggregation types available may change based on the Data Type of the selected Measure.
Total (sum)
Adds all of the values in the Measure together and displays as a number.
Total is the default aggregation method for numerical Data Types.
Only available with numeric Data Types.
Average (avg)
Averages all of the values in the Measure and displays as a number.
Only available with numeric Data Types.
Minimum (min)
Displays the minimum value in the Measure.
Only available with numeric Data Types.
Maximum (max)
Displays the maximum value in the Measure.
Only available with numeric Data Types.
Count (cnt)
Counts the number of values in the Measure. Note - this does not count unique values, duplicate values will be counted each time they are present.
Count is the default aggregation method for text Data Types.
Available for Text Data Types
Approximate Unique Count (auc)
Approximates the number of unique values in the Measure. In most cases, the approximate count is accurate enough for reliable reporting. It is important to remember that, while very accurate, this count is approximate. This approximation most apparent with high cardinality Datasets, where the count may be less than 100% accurate.
We do recognize that sometimes approximate isn’t close enough. If there is a situation where 100% accuracy is required for a unique count, please reach out to us through Chat (click the chat bubble in the bottom corner of the screen) and we can help you find an appropriate solution.
Available for all Data Types.