Generates a new column containing the row number as sorted by t=
he `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:**

=20
window ROWNUMBER() order:Date

**Output:** Generates the new column, which contains the ro=
w number of each row as ordered by the values in the `Date`

colu=
mn.

**Example with grouping:**

=20
window ROWNUMBER() order:Date group:QTR

**Output:** Generates the new column , which contains the row number of each row as ordered by the valu=
es in the `Date`

column grouped by the ```
QTR<=
/code> values. For each quarter value, the row number counter resets.&=
nbsp;
```

` `

```
&nbs=
p;
```

```
```

## Sy=
ntax and Arguments

=20
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 Note=
s.

## Examples

**Tip:** For additional examples, see Common Tasks.

### Example - Rolli=
ng 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 deter=
mined by the ordering column. See ROWNUMBER Function.

The following dataset contains sales data over the final quarter of the =
year.

**Source:**

=20
=20
=20
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. Si=
nce data is ordered already by the `Date`

column, you can use th=
e following:

window value:ROWNUMBER() order:Date

Ren=
ame 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:=20
derive type:single value:(1 + FLOOR(=
((month-1)/3))) as:'QTR'

Deriving the week-of-quarter value:=20
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: Dat=
e group:QTR

The=20
`-1`

parameter is used in the above computation to gather t=
he rolling sum of all rows of data from the current one to the first one. N=
ote that the use of the
`QTR`

column for grouping, which moves the value for the&nb=
sp;
`01/01/2017`

into its own computational bucket. This may or may =
not be preferred.
Rename this column `QTD`

(quarter to-date). Now, generat=
e a similar column to compute the rolling average of weekly sales for the q=
uarter:

window value: ROUND(ROLLINGAVERAGE(Sales, -1, 0))=
order: Date group:QTR

Since the=20
`ROLLINGAVERAGE`

function can compute fractional values, it is w=
rapped in the=20
`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

=20

```
------=_Part_53993_447333307.1635071053750--
```