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Excerpt

You can

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apply aggregate functions to groups of values in one or more columns to generate aggregated data. Depending on how you configure the Group By transformation, the output of these transformations is a new table or one or more columns in the current dataset.

Limitations

  • The Group By transformation does not support nested expressions. You cannot insert multiple nested expressions in your computed value.
  • The Group By transformation supports aggregation functions only. For more information, see Aggregate Functions. 

Example Data

The following table contains test score data from a set of students for four separate tests, spread over two days:

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You may wish to rename the newly generated column to something like average_TestScorePerStudent instead. See Rename Columns. 

The output data should look like the following:

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Tip

Tip: In this case, when you replace the existing table with a completely new table, data that is not included in the aggregation is lost. You can add columns to the list of values if you wish to bring forward untouched columns into the new table. You may also consider building aggregation tables in a recipe that is extended from the previous recipe, so that you can continue to work with the other columns in your dataset.

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label((label = "structuring_tasks") OR (label = "aggregation_functions") OR (label = "pivot"))