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 | `Window` |
---|---|

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 | `New formula` |
---|---|

Parameter: Formula type | `Single row formula` |

Parameter: Formula | `MONTH(Date)` |

Parameter: New column name | `'Month'` |

Deriving the quarter value:

Transformation Name | `New formula` |
---|---|

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 | `Window` |
---|---|

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 | `Window` |
---|---|

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 | `Window` |
---|---|

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 | `Window` |
---|---|

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 |

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