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 SOURCEROWNUMBER. See SOURCEROWNUMBER Function.
This function works with the following transforms:
Basic Usage
Example:
window ROWNUMBER() order:Date
Output: Generates the new column, which contains the row number of each row as ordered by the values in the Date
column.
Example with grouping:
window ROWNUMBER() order:Date group:QTR
Output: Generates the new column , which contains 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 and Arguments
window value:ROWNUMBER() order: order_col [group: group_col]
For more information on the order
and group
parameters, see Window Transform.
For more information on syntax standards, see Language Documentation Syntax Notes.
Tip: For additional examples, see Common Tasks.
Examples
Example - Rolling window 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 |
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