# ROLLINGSUM Function

Computes the rolling sum of values forward or backward of the current row within the specified column.

• If an input value is missing or null, it is not factored in the computation. For example, for the first row in the dataset, the rolling sum of previous values is the value in the first row.

• The row from which to extract a value is determined by the order in which the rows are organized based on the order parameter.

• If you are working on a randomly generated sample of your dataset, the values that you see for this function might not correspond to the values that are generated on the full dataset during job execution.

• The function takes a column name and two optional integer parameters that determine the window backward and forward of the current row.

• The default integer parameter values are -1 and 0, which computes the rolling average from the current row back to the first row of the dataset.

• This function works with the Window transform. See Window Transform.

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

Column example:

rollingsum(myCol)

Output: Returns the rolling sum of all values in the myCol column.

Rows before example:

rollingsum(myNumber, 3)

Output: Returns the rolling sum of the current row and the three previous row values in the myNumber column.

Rows before and after example:

rollingsum(myNumber, 3, 2)

Output: Returns the rolling sum of the three previous row values, the current row value, and the two rows after the current one in the myNumber column.

## Syntax and Arguments

rollingsum(col_ref, rowsBefore_integer, rowsAfter_integer) order: order_col [group: group_col]

Argument

Required?

Data Type

Description

col_ref

Y

string

Name of column whose values are applied to the function

rowsBefore_integer

N

integer

Number of rows before the current one to include in the computation.

rowsAfter_integer

N

integer

Number of rows after the current one to include in the computation

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

### col_ref

Name of the column whose values are used to compute the rolling sum.

• Multiple columns and wildcards are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

String (column reference to Integer or Decimal values)

myColumn

### rowsBefore_integer, rowsAfter_integer

Integers representing the number of rows before or after the current one from which to compute the rolling sum, including the current row. For example, if the first value is 5, the current row and the five rows before it are used in the computation. Negative values for rowsAfter_integer compute the rolling function from rows preceding the current one.

• rowBefore=0 generates the current row value only.

• rowBefore=-1 uses all rows preceding the current one.

• If rowsAfter is not specified, then the value 0 is applied.

• If a group parameter is applied, then these parameter values should be no more than the maximum number of rows in the groups.

Usage Notes:

Required?

Data Type

Example Value

No

Integer

4

## Examples

Suggerimento

### Example - Rolling window functions

This example describes how to use rolling computational functions.

Functions:

Item

Description

ROLLINGSUM Function

Computes the rolling sum of values forward or backward of the current row within the specified column.

ROLLINGAVERAGE Function

Computes the rolling average of values forward or backward of the current row within the specified column.

ROWNUMBER Function

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

Also:

Item

Description

MONTH Function

Derives the month integer value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal.

FLOOR Function

Computes the largest integer that is not more than the input value. Input can be an Integer, a Decimal, a column reference, or an expression.

ROWNUMBER Function

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

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 ROWNUMBER() 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 Single row formula MONTH(Date) 'Month'

Deriving the quarter value:

 Transformation Name New formula Single row formula (1 + FLOOR(((month-1)/3))) 'QTR'

Deriving the week-of-quarter value:

 Transformation Name Window ROWNUMBER() QTR Date

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

 Transformation Name Window ROWNUMBER() Month 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 ROLLINGSUM(Sales, -1, 0) QTR 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 ROUND(ROLLINGAVERAGE(Sales, -1, 0)) QTR 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