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Release 5.0.1

Contents:

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:

DateSales
10/2/16200
10/9/16500
10/16/16350
10/23/16400
10/30/16190
11/6/16550
11/13/16610
11/20/16480
11/27/16660
12/4/16690
12/11/16810
12/18/16950
12/25/161020
1/1/17680

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:

DateWOQSalesQTDavgWeekByQuarter
10/2/161200200200
10/9/162500700350
10/16/1633501050350
10/23/1644001450363
10/30/1651901640328
11/6/1665502190365
11/13/1676102800400
11/20/1684803280410
11/27/1696603940438
12/4/16106904630463
12/11/16118105440495
12/18/16129506390533
12/25/161310207410570
1/1/171680680680