ROLLINGMAXDATE Function
Computes the rolling maximum of date values forward or backward of the current row within the specified column. Inputs must be of Datetime type.
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
orderparameter.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
-1and0, which computes the rolling function from the current row back to the first row of the dataset.
This function works with the Window transform. See Window Transform.
For more information on a non-rolling version of this function, see MAXDATE Function.
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:
rollingmaxdate(<span>myDate</span>)
Output: Returns the rolling maximum of all values in the myDate column.
Rows before example:
rollingmaxdate(<span>myDate</span>, 3)
Output: Returns the rolling maximum of the current row and the three previous row values in the myDate column.
Rows before and after example:
rollingmaxdate(myDate, 3, 2)
Output: Returns the rolling maximum of the three previous row values, the current row value, and the two rows after the current one in the myDate column.
Syntax and Arguments
rollingmaxdate(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. Inputs must be Datetime values.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String (column reference to Datetime values) | transactionDate |
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 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=0generates the current row value only.rowBefore=-1uses all rows preceding the current one.If
rowsAfteris not specified, then the value0is applied.If a
groupparameter 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
Astuce
For additional examples, see Common Tasks.
Example - Rolling date functions
This example describes how to use rolling functions for Datetime values.
Functions:
Item | Description |
|---|---|
ROLLINGMINDATE Function | Computes the rolling minimum of Date values forward or backward of the current row within the specified column. Inputs must be of Datetime type. |
ROLLINGMAXDATE Function | Computes the rolling maximum of date values forward or backward of the current row within the specified column. Inputs must be of Datetime type. |
ROLLINGMODEDATE Function | Computes the rolling mode (most common value) forward or backward of the current row within the specified column. Input values must be of Datetime data type. |
Source:
The following table contains an unordered list of orders:
myDate | prodId | orderDollars |
|---|---|---|
2020-03-13 | p001 | 1445 |
2020-03-06 | p002 | 712 |
2020-03-16 | p003 | 1374 |
2020-03-23 | p001 | 1675 |
2020-04-09 | p002 | 1005 |
2020-08-09 | p003 | 984 |
2020-05-02 | p001 | 1395 |
2020-06-14 | p002 | 1866 |
2020-07-16 | p003 | 824 |
2020-09-02 | p001 | 1785 |
2020-08-31 | p002 | 697 |
2020-10-22 | p003 | 1513 |
2020-03-17 | p001 | 768 |
2020-03-21 | p002 | 1893 |
2020-03-23 | p003 | 1122 |
2020-04-06 | p001 | 805 |
2020-05-09 | p002 | 1752 |
2021-01-09 | p003 | 616 |
2020-08-18 | p001 | 1563 |
2020-09-12 | p002 | 730 |
2020-10-04 | p003 | 587 |
2021-02-15 | p001 | 1979 |
2021-02-22 | p002 | 134 |
2021-03-14 | p003 | 938 |
Transformation:
You can use the following Window transformation to calculate the rolling minimum, maximum, and mode dates for the last five orders for each product identifier:
Transformation Name | |
|---|---|
Parameter: Formula1 | ROLLINGMINDATE(orderDate, 4, 0) |
Parameter: Formula2 | ROLLINGMAXDATE(orderDate, 4, 0) |
Parameter: Formula3 | ROLLINGMODEDATE(orderDate, 4, 0) |
Parameter: Group by | prodId |
Parameter: Order by | prodId |
You can use the following transformation to rename the generated window columns:
Transformation Name | |
|---|---|
Parameter: Option | Manual rename |
Parameter: Column | window1 |
Parameter: New column name | rollingMinDate |
Parameter: Parameter: Column | window2 |
Parameter: New column name | rollingMaxDate |
Parameter: Parameter: Column | window3 |
Parameter: New column name | rollingModeDate |
Results:
orderDate | prodId | orderDollars | rollingMinDate | rollingMaxDate | rollingModeDate |
|---|---|---|---|---|---|
3/16/20 | p003 | 1374 | 3/16/20 | 3/16/20 | 3/16/20 |
8/9/20 | p003 | 984 | 3/16/20 | 8/9/20 | 3/16/20 |
7/16/20 | p003 | 824 | 3/16/20 | 8/9/20 | 3/16/20 |
10/22/20 | p003 | 1513 | 3/16/20 | 10/22/20 | 3/16/20 |
3/23/20 | p003 | 1122 | 3/16/20 | 10/22/20 | 3/16/20 |
1/9/21 | p003 | 616 | 3/23/20 | 1/9/21 | 3/23/20 |
10/4/20 | p003 | 587 | 3/23/20 | 1/9/21 | 3/23/20 |
3/14/21 | p003 | 938 | 3/23/20 | 3/14/21 | 3/23/20 |
3/13/20 | p001 | 1445 | 3/13/20 | 3/13/20 | 3/13/20 |
3/23/20 | p001 | 1675 | 3/13/20 | 3/23/20 | 3/13/20 |
5/2/20 | p001 | 1395 | 3/13/20 | 5/2/20 | 3/13/20 |
9/2/20 | p001 | 1785 | 3/13/20 | 9/2/20 | 3/13/20 |
3/17/20 | p001 | 768 | 3/13/20 | 9/2/20 | 3/13/20 |
4/6/20 | p001 | 805 | 3/17/20 | 9/2/20 | 3/17/20 |
8/18/20 | p001 | 1563 | 3/17/20 | 9/2/20 | 3/17/20 |
2/15/21 | p001 | 1979 | 3/17/20 | 2/15/21 | 3/17/20 |
3/6/20 | p002 | 712 | 3/6/20 | 3/6/20 | 3/6/20 |
4/9/20 | p002 | 1005 | 3/6/20 | 4/9/20 | 3/6/20 |
6/14/20 | p002 | 1866 | 3/6/20 | 6/14/20 | 3/6/20 |
8/31/20 | p002 | 697 | 3/6/20 | 8/31/20 | 3/6/20 |
3/21/20 | p002 | 1893 | 3/6/20 | 8/31/20 | 3/6/20 |
5/9/20 | p002 | 1752 | 3/21/20 | 8/31/20 | 3/21/20 |
9/12/20 | p002 | 730 | 3/21/20 | 9/12/20 | 3/21/20 |
2/22/21 | p002 | 134 | 3/21/20 | 2/22/21 | 3/21/20 |