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Computes the rolling variance of values forward or backward of the current row within the specified column using the sample statistical method.
• 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 variance 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.

NOTE: This function applies to a sample of the entire population. More information is below.

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 VAR 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:

rollingvarsamp(myCol)

Output: Returns the rolling variance of all values in the `myCol` column from the first row of the dataset to the current one using the sample method of calculation.

Rows before example:

rollingvarsamp(myNumber, 100)

Output: Returns the rolling variance of the current row and the 100 previous row values in the `myNumber` column using the sample method of calculation.

Rows before and after example:

rollingvarsamp(myNumber, 3, 2)

Output: Returns the rolling variance of the three previous row values, the current row value, and the two rows after the current one in the `myNumber` column using the sample method of calculation.

## Syntax and Arguments

rollingvarsamp(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.

### col_ref

Name of the column whose values you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.

• 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 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 TypeExample Value
NoInteger`4`

## Examples

### Example - Rolling computations for racing splits

This example describes how to use the rolling computational functions:

• `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.
• `ROLLINGSTDEVSAMP` - computes a rolling standard deviation from a window of rows using the sample method of statistical calculation. See ROLLINGSTDEVSAMP Function.
• `ROLLINGVARSAMP` - computes a rolling variance from a window of rows using the sample method of statistical calculation. See ROLLINGVARSAMP 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.

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.

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