# ROLLINGSTDEV Function

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]

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 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 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 Type

Example Value

No

Integer

4

## Examples

Tip

### Example - Rolling computations for racing splits

This example describes how to use rolling statistical functions.

Functions:

Item

Description

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:

Item

Description

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.

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

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:

lap

quarter

splitId

time_sc

split_time_sc

rvar_samp

rstdev_samp

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

0.229

0.479

0.029

0.169

20.096

20.434

20.265

1

3

l1q3

61.031

20.501

0.154

0.392

0.031

0.177

20.096

20.501

20.344

2

0

l2q0

81.087

20.056

0.315

0.561

0.039

0.198

20.056

20.501

20.272

2

1

l2q1

101.383

20.296

0.142

0.376

0.029

0.17

20.056

20.501

20.322

2

2

l2q2

122.092

20.709

0.617

0.786

0.059

0.242

20.056

20.709

20.39

2

3

l2q3

141.886

19.794

0.621

0.788

0.113

0.337

19.794

20.709

20.214

3

0

l3q0

162.581

20.695

0.579

0.761

0.139

0.373

19.794

20.709

20.373

3

1

l3q1

183.018

20.437

0.443

0.666

0.138

0.371

19.794

20.709

20.409

3

2

l3q2

203.493

20.475

0.537

0.733

0.113

0.336

19.794

20.695

20.35

3

3

l3q3

222.893

19.4

0.520

0.721

0.252

0.502

19.4

20.695

20.252

You can reduce the number of steps by applying awindowtransform 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.