Example: Run an aggregate function on a window

You want to compare the salary of each employee with the average salary for the corresponding department.

The following table lists the department names, the employee identification number, and the employee salary:

Department	Employee	Salary
Development	11	5200
Development	7	4200
Development	9	4500
Development	8	6000
Development	10	5200
Personnel	5	3500
Personnel	2	3900
Sales	3	4800
Sales	1	5000
Sales	4	4800

You set an unbounded frame to include all employees in the calculation, and you define an aggregate function to calculate the difference between the salary of each employee and the average salary in the department.

Window Properties

Property	Value	Description
Start offset	All Preceding Rows	Describes the number of rows that appear before the current input row.
End offset	All Following Rows	Describes the number of rows that appear after the current input row.
Order key	Salary Ascending	Arranges the data by increasing salary.
Partition key	Department	Groups the rows according to department.

When you select All Preceding Rows and All Following Rows, the function includes all partition rows. For example, suppose the current row is the third row. The third row is in the "Development" partition, so the frame includes the third row in addition to all rows before and after the third row in the "Development" partition.

Window Function

Because you configure window properties in the Expression transformation, you can use an aggregate function as a window function.

You define the following aggregate function to calculate the difference between the salary of each employee and the average salary in the corresponding department:

Output

Department	Employee	Salary	Salary_Diff
Development	11	5200	-820
Development	7	4200	-520
Development	9	4500	180
Development	8	6000	180
Development	10	5200	980
Personnel	5	3500	200
Personnel	2	3900	200
Sales	3	4800	-66
Sales	1	5000	-66
Sales	4	4800	134

You can identify which employees are making less or more than the average salary within the same department. Based on this information, you can add other transformations to learn more about the data. For example, you might add a Rank transformation to produce a numerical rank for each employee within the same department.