**Contents:**

- 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 maximum of previous values is undefined.
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 function from the current row back to the first row of the dataset.

- The default integer parameter values are
- This function works with the following transforms:

For more information on a non-rolling version of this function, see MAX Function.

## Basic Usage

**Column example:**

derive type:single value:ROLLINGMAX(myCol)

**Output: **Generates a new column containing the rolling maximum of all values in the `myCol`

column from the first row of the dataset to the current one.

**Rows before example:**

window value:ROLLINGMAX(myNumber, 3)

**Output:** Generates the new column, which contains the rolling maximum of the current row and the two previous row values in the `myNumber`

column.

**Rows before and after example:**

window value:ROLLINGMAX(myNumber, 3, 2)

**Output:** Generates the new column, which contains the rolling maximum of the two previous row values, the current row value, and the two rows after the current one in the `myNumber`

column.

## Syntax and Arguments

window value:ROLLINGMAX(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.

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

### col_ref

Name of the column whose values are used to compute the function.

- 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 function, including the current row. For example, if the first value is `5`

, the current row and the four rows after it are used in the computation. Negative values for `k`

compute the rolling average from rows preceding the current one.

`rowBefore=1`

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

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

### Example - Rolling computations for racing splits

`ROLLINGAVERAGE`

- computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.`ROLLINGMIN`

- computes a rolling minimum from a window of rows. See ROLLINGMIN Function.`ROLLINGMAX`

- computes a rolling maximum from a window of rows. See ROLLINGMAX Function.`ROLLINGSTDEV`

- computes a rolling standard deviation from a window of rows. See ROLLINGSTDEV Function.`ROLLINGVAR`

- computes a rolling variance from a window of rows. See ROLLINGVAR Function.

**Source:**

In this example, the following data comes from times recorded at regular intervals during a three-lap race around a track. The source data is in cumulative time in seconds (`time_sc`

). You can use ROLLING and other windowing functions to break down the data into more meaningful metrics.

lap | quarter | time_sc |
---|---|---|

1 | 0 | 0.000 |

1 | 1 | 19.554 |

1 | 2 | 39.785 |

1 | 3 | 60.021 |

2 | 0 | 80.950 |

2 | 1 | 101.785 |

2 | 2 | 121.005 |

2 | 3 | 141.185 |

3 | 0 | 162.008 |

3 | 1 | 181.887 |

3 | 2 | 200.945 |

3 | 3 | 220.856 |

**Transform:**

**Primary key: **Since the quarter information repeats every lap, there is no unique identifier for each row. The following steps create this identifer:

`settype col: lap,quarter type: 'String'`

`derive type:single value: MERGE(['l',lap,'q',quarter]) as: 'splitId'`

**Get split times:** Use the following transform to break down the splits for each quarter of the race:

`derive type:single value: ROUND(time_sc - PREV(time_sc, 1), 3) order: splitId as: 'split_time_sc'`

**Compute rolling computations: **You can use the following types of computations to provide rolling metrics on the current and three previous splits:

`derive type:single value: ROLLINGAVERAGE(split_time_sc, 3) order: splitId as: 'ravg'`

`derive type:single value: ROLLINGMAX(split_time_sc, 3) order: splitId as: 'rmax'`

`derive type:single value: ROLLINGMIN(split_time_sc, 3) order: splitId as: 'rmin'`

`derive type:single value: ROUND(ROLLINGSTDEV(split_time_sc, 3), 3) order: splitId as: 'rstdev'`

`derive type:single value: ROUND(ROLLINGVAR(split_time_sc, 3), 3) order: splitId as: 'rvar'`

**Results:**

When the above transforms have been completed, the results look like the following:

lap | quarter | splitId | time_sc | split_time_sc | rvar | rstdev | rmin | rmax | ravg |
---|---|---|---|---|---|---|---|---|---|

1 | 0 | l1q0 | 0 | ||||||

1 | 1 | l1q1 | 20.096 | 20.096 | 0 | 0 | 20.096 | 20.096 | 20.096 |

1 | 2 | l1q2 | 40.53 | 20.434 | 0.029 | 0.169 | 20.096 | 20.434 | 20.265 |

1 | 3 | l1q3 | 61.031 | 20.501 | 0.031 | 0.177 | 20.096 | 20.501 | 20.344 |

2 | 0 | l2q0 | 81.087 | 20.056 | 0.039 | 0.198 | 20.056 | 20.501 | 20.272 |

2 | 1 | l2q1 | 101.383 | 20.296 | 0.029 | 0.17 | 20.056 | 20.501 | 20.322 |

2 | 2 | l2q2 | 122.092 | 20.709 | 0.059 | 0.242 | 20.056 | 20.709 | 20.39 |

2 | 3 | l2q3 | 141.886 | 19.794 | 0.113 | 0.337 | 19.794 | 20.709 | 20.214 |

3 | 0 | l3q0 | 162.581 | 20.695 | 0.139 | 0.373 | 19.794 | 20.709 | 20.373 |

3 | 1 | l3q1 | 183.018 | 20.437 | 0.138 | 0.371 | 19.794 | 20.709 | 20.409 |

3 | 2 | l3q2 | 203.493 | 20.475 | 0.113 | 0.336 | 19.794 | 20.695 | 20.35 |

3 | 3 | l3q3 | 222.893 | 19.4 | 0.252 | 0.502 | 19.4 | 20.695 | 20.252 |

You can reduce the number of steps by applying a `window`

transform such as the following:

`window value: window1 = lap,rollingaverage(split_time_sc, 0, 3), rollingmax(split_time_sc, 0, 3),rollingmin(split_time_sc, 0, 3),round(rollingstdev(split_time_sc, 0, 3), 3),round(rollingvar(split_time_sc, 0, 3), 3) group: lap order: lap`

However, you must rename all of the generated `windowX`

columns.

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