ROLLINGSUM Function

Release 5.0.1

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Computes the rolling sum of values forward or backward of the current row within the specified column.

If an input value is missing or null, it is not factored in the computation. For example, for the first row in the dataset, the rolling sum of previous values is the value in the first row.
The row from which to extract a value is determined by the order in which the rows are organized based on the order parameter. If you are working on a randomly generated sample of your dataset, the values that you see for this function might not correspond to the values that are generated on the full dataset during job execution.
The function takes a column name and two optional integer parameters that determine the window backward and forward of the current row.
- The default integer parameter values are -1 and 0, which computes the rolling average from the current row back to the first row of the dataset.
This function works with the following transforms:

Basic Usage

Column example:

derive type:single value:ROLLINGSUM(myCol)

Output: Generates a new column containing the rolling sum of all values in the myCol column from the first row of the dataset to the current one.

Rows before example:

window value:ROLLINGSUM(myNumber, 3)

Output: Generates the new column, which contains the rolling sum of the current row and the two previous row values in the myNumber column.

Rows before and after example:

window value:ROLLINGSUM(myNumber, 3, 2)

Output: Generates the new column , which contains the rolling sum of the two previous row values, the current row value, and the two rows after the current one in the myNumber column.

Syntax and Arguments

window value:ROLLINGSUM(col_ref, rowsBefore_integer, rowsAfter_integer) order: order_col [group: group_col]

Argument	Required?	Data Type	Description
col_ref	Y	string	Name of column whose values are applied to the function
rowsBefore_integer	N	integer	Number of rows before the current one to include in the computation
rowsAfter_integer	N	integer	Number of rows after the current one to include in the computation

For more information on the order and group parameters, see Window Transform.

For more information on syntax standards, see Language Documentation Syntax Notes.

col_ref

Name of the column whose values are used to compute the rolling sum.

Multiple columns and wildcards are not supported.

Usage Notes:

Required?	Data Type	Example Value
Yes	String (column reference to Integer or Decimal values)	`myColumn`

rowsBefore_integer, rowsAfter_integer

Integers representing the number of rows before or after the current one from which to compute the rolling sum, including the current row. For example, if the first value is 5, the current row and the four rows after it are used in the computation. Negative values for k compute the rolling average from rows preceding the current one.

rowBefore=1 generates the current row value only.
rowBefore=-1 uses all rows preceding the current one.
If rowsAfter is not specified, then the value 0 is applied.
If a group parameter is applied, then these parameter values should be no more than the maximum number of rows in the groups.

Usage Notes:

Required?	Data Type	Example Value
No	Integer	`4`

Examples

Tip: For additional examples, see Common Tasks.

Example - Rolling window functions

This example describes how to use the rolling computational functions:

ROLLINGSUM - computes a rolling sum from a window of rows before and after the current row. See ROLLINGSUM Function.
ROLLINGAVERAGE - computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.
ROWNUMBER - computes the row number for each row, as determined by the ordering column. See ROWNUMBER Function.

The following dataset contains sales data over the final quarter of the year.

Source:

Date	Sales
10/2/16	200
10/9/16	500
10/16/16	350
10/23/16	400
10/30/16	190
11/6/16	550
11/13/16	610
11/20/16	480
11/27/16	660
12/4/16	690
12/11/16	810
12/18/16	950
12/25/16	1020
1/1/17	680

Transform:

First, you want to maintain the row information as a separate column. Since data is ordered already by the Date column, you can use the following:

window value:ROWNUMBER() order:Date

Rename this column to rowId for week of quarter.

Now, you want to extract month and week information from the Date values. Deriving the month value:

derive type:single value:MONTH(Date) as:'Month'

Deriving the quarter value:

derive type:single value:(1 + FLOOR(((month-1)/3))) as:'QTR'

Deriving the week-of-quarter value:

window value:ROWNUMBER() order:Date group:QTR

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

window value:ROWNUMBER() group:Month order:Date

Rename this column WOM (week of month).

Now, you perform your rolling computations. Compute the running total of sales using the following:

window value: ROLLINGSUM(Sales, -1, 0) order: Date group:QTR

The -1 parameter is used in the above computation to gather the rolling sum of all rows of data from the current one to the first one. Note that the use of the QTR column for grouping, which moves the value for the 01/01/2017 into its own computational bucket. This may or may not be preferred.

Rename this column QTD (quarter to-date). Now, generate a similar column to compute the rolling average of weekly sales for the quarter:

window value: ROUND(ROLLINGAVERAGE(Sales, -1, 0)) order: Date group:QTR

Since the ROLLINGAVERAGE function can compute fractional values, it is wrapped in the ROUND function for neatness. Rename this column avgWeekByQuarter.

Results:

When the unnecessary columns are dropped and some reordering is applied, your dataset should look like the following:

Date	WOQ	Sales	QTD	avgWeekByQuarter
10/2/16	1	200	200	200
10/9/16	2	500	700	350
10/16/16	3	350	1050	350
10/23/16	4	400	1450	363
10/30/16	5	190	1640	328
11/6/16	6	550	2190	365
11/13/16	7	610	2800	400
11/20/16	8	480	3280	410
11/27/16	9	660	3940	438
12/4/16	10	690	4630	463
12/11/16	11	810	5440	495
12/18/16	12	950	6390	533
12/25/16	13	1020	7410	570
1/1/17	1	680	680	680

See Also for ROLLINGSUM Function:

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