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 following transforms:

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

Basic Usage

Column example:

derive type:single value:ROLLINGSTDEV(myCol)

Output: Generates a new column containing the rolling standard deviation of all values in the myCol column from the first row of the dataset to the current one.

Rows before example:

window value:ROLLINGSTDEV(myNumber, 100)

Output: Generates the new column, which contains the rolling standard deviation of the current row and the 100 previous row values in the myNumber column.

Rows before and after example:

window value:ROLLINGSTDEV(myNumber, 3, 2)

Output: Generates the new column, which contains the rolling standard deviation of the two previous row values, the current row value, and the two rows after the current one in the myNumber column.

Syntax

window value: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 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 TypeExample Value
NoInteger4

Examples

Example - Rolling computations for racing splits

This example describes how to use the rolling computational functions:

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

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:

lapquartersplitIdtime_scsplit_time_scrvarrstdevrminrmaxravg
10l1q00
11l1q120.09620.0960020.09620.09620.096
12l1q240.5320.4340.0290.16920.09620.43420.265
13l1q361.03120.5010.0310.17720.09620.50120.344
20l2q081.08720.0560.0390.19820.05620.50120.272
21l2q1101.38320.2960.0290.1720.05620.50120.322
22l2q2122.09220.7090.0590.24220.05620.70920.39
23l2q3141.88619.7940.1130.33719.79420.70920.214
30l3q0162.58120.6950.1390.37319.79420.70920.373
31l3q1183.01820.4370.1380.37119.79420.70920.409
32l3q2203.49320.4750.1130.33619.79420.69520.35
33l3q3222.89319.40.2520.50219.420.69520.252

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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