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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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