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Computes the rolling standard deviation 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 standard deviation 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.
• This function works with the Window transform. See Window Transform.

Terms...

Relevant terms:

Term Description
Population Population statistical functions are computed from all possible values. See https://en.wikipedia.org/wiki/Statistical_population.
Sample

Sample-based statistical functions are computed from a subset or sample of all values. See https://en.wikipedia.org/wiki/Sampling_(statistics).

These function names include `SAMP` in their name.

NOTE: Statistical sampling has no relationship to the samples taken within the product. When statistical functions are computed during job execution, they are applied across the entire dataset. Sample method calculations are computed at that time.

For more information on a non-rolling version of this function, see STDEV 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:

rollingstdev(myCol)

Output: Returns the rolling standard deviation of all values in the `myCol` column.

Rows before example:

rollingstdev(myNumber, 100)

Output: Returns the rolling standard deviation of the current row and the 100 previous row values in the `myNumber` column.

Rows before and after example:

rollingstdev(myNumber, 3, 2)

Output: Returns the rolling standard deviation 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

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

ArgumentRequired?Data TypeDescription
col_refYstringName of column whose values are applied to the function
rowsBefore_integerNintegerNumber of rows before the current one to include in the computation
rowsAfter_integerNintegerNumber 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 TypeExample Value
YesString (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 five before after 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 TypeExample Value
NoInteger`4`

## Examples

Tip: For additional examples, see Common Tasks.

### Example - Rolling computations for racing splits

This example describes how to use rolling statistical functions.

Functions:

ItemDescription
ROLLINGAVERAGE Function Computes the rolling average of values forward or backward of the current row within the specified column.
ROLLINGMAX Function Computes the rolling maximum of values forward or backward of the current row within the specified column. Inputs can be Integer, Decimal, or Datetime.
ROLLINGSTDEV Function Computes the rolling standard deviation of values forward or backward of the current row within the specified column.
ROLLINGVAR Function Computes the rolling variance of values forward or backward of the current row within the specified column.
ROLLINGSTDEVSAMP Function Computes the rolling standard deviation of values forward or backward of the current row within the specified column using the sample statistical method.
ROLLINGVARSAMP Function Computes the rolling variance of values forward or backward of the current row within the specified column using the sample statistical method.

Also:

ItemDescription
MERGE Function Merges two or more columns of String type to generate output of String type. Optionally, you can insert a delimiter between the merged values.
ROUND Function Rounds input value to the nearest integer. Input can be an Integer, a Decimal, a column reference, or an expression. Optional second argument can be used to specify the number of digits to which to round.

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.

lapquartertime_sc
100.000
1119.554
1239.785
1360.021
2080.950
21101.785
22121.005
23141.185
30162.008
31181.887
32200.945
33220.856

Transformation:

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

Transformation Name `Change column data type` `lap,quarter` `String`

Transformation Name `New formula` `Single row formula` `MERGE(['l',lap,'q',quarter])` `'splitId'`

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

Transformation Name `New formula` `Multiple row formula` `ROUND(time_sc - PREV(time_sc, 1), 3)` `splitId` `'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:

Transformation Name `New formula` `Multiple row formula` `ROLLINGAVERAGE(split_time_sc, 3)` `splitId` `'ravg'`

Transformation Name `New formula` `Multiple row formula` `ROLLINGMAX(split_time_sc, 3)` `splitId` `'rmax'`

Transformation Name `New formula` `Multiple row formula` `ROLLINGMIN(split_time_sc, 3)` `splitId` `'rmin'`

Transformation Name `New formula` `Multiple row formula` `ROUND(ROLLINGSTDEV(split_time_sc, 3), 3)` `splitId` `'rstdev'`

Transformation Name `New formula` `Multiple row formula` `ROUND(ROLLINGVAR(split_time_sc, 3), 3)` `splitId` `'rvar'`

Compute rolling computations using sample method: These metrics compute the rolling STDEV and VAR on the current and three previous splits using the sample method:

Transformation Name `New formula` `Multiple row formula` `ROUND(ROLLINGSTDEVSAMP(split_time_sc, 3), 3)` `splitId` `'rstdev_samp'`

Transformation Name `New formula` `Multiple row formula` `ROUND(ROLLINGVARSAMP(split_time_sc, 3), 3)` `splitId` `'rvar_samp'`

Results:

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

lapquartersplitIdtime_scsplit_time_scrvar_samprstdev_samprvarrstdevrminrmaxravg
10l1q00

11l1q120.09620.096

0020.09620.09620.096
12l1q240.5320.4340.2290.4790.0290.16920.09620.43420.265
13l1q361.03120.5010.1540.3920.0310.17720.09620.50120.344
20l2q081.08720.0560.3150.5610.0390.19820.05620.50120.272
21l2q1101.38320.2960.1420.3760.0290.1720.05620.50120.322
22l2q2122.09220.7090.6170.7860.0590.24220.05620.70920.39
23l2q3141.88619.7940.6210.7880.1130.33719.79420.70920.214
30l3q0162.58120.6950.5790.7610.1390.37319.79420.70920.373
31l3q1183.01820.4370.4430.6660.1380.37119.79420.70920.409
32l3q2203.49320.4750.5370.7330.1130.33619.79420.69520.35
33l3q3222.89319.40.5200.7210.2520.50219.420.69520.252

You can reduce the number of steps by applying a `window` transform such as the following:

Transformation Name `Window` `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)` `round(rollingstdevsamp(split_time_sc, 0, 3), 3)` `round(rollingvarsamp(split_time_sc, 0, 3), 3)` `lap` `lap`

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

See Also for EXAMPLE - Rolling Functions 2:

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