EXAMPLE - Rolling 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 |
Transformation:
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:
Transformation Name |
|
|---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Order by | 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:
Transformation Name |
|
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | MONTH(Date) |
Parameter: New column name | 'Month' |
Deriving the quarter value:
Transformation Name |
|
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | (1 + FLOOR(((month-1)/3))) |
Parameter: New column name | 'QTR' |
Deriving the week-of-quarter value:
Transformation Name |
|
|---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Group by | QTR |
Parameter: Order by | Date |
Rename this column WOQ (week of quarter).
Deriving the week-of-month value:
Transformation Name |
|
|---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Group by | Month |
Parameter: Order by | Date |
Rename this column WOM (week of month).
Now, you perform your rolling computations. Compute the running total of sales using the following:
Transformation Name |
|
|---|---|
Parameter: Formulas | ROLLINGSUM(Sales, -1, 0) |
Parameter: Group by | QTR |
Parameter: Order by | Date |
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:
Transformation Name |
|
|---|---|
Parameter: Formulas | ROUND(ROLLINGAVERAGE(Sales, -1, 0)) |
Parameter: Group by | QTR |
Parameter: Order by | Date |
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 |