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:

**Wrangle vs. SQL:** This function is part of Wrangle
, a proprietary data transformation language. Wrangle
is not SQL. For more information, see Wrangle Language.

## Basic Usage

**Example:**

rownumber()

**Output:** Returns the row number of each row.

**Example with grouping:**

rownumber() order:Date group:QTR

**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 and Arguments

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.

## Examples

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

### 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 |

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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