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Generates a new column containing the row number as sorted by the order parameter and optionally grouped by the group parameter.

Tip: To generate row identifiers by the original order in the source data, use the $sourcerownumber reference. See Source Metadata References.

This function works with the following transforms:

 

Basic Usage

Example:

Output: Returns the row number of each row.

Example with grouping:

Output: Returns the row number of each row as ordered by the values in the Date column grouped by the QTR values. For each quarter value, the row number counter resets.       

Syntax

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

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

Examples

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:

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


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:

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


 

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