ROLLINGAVERAGE Function

Source:

Transform:

Using the ROLLINGAVERAGE function, you can generate a column containing the rolling average of the current month and the two previous months:

window value: ROLLINGAVERAGE(Amount, 3, 0) order: -Date

Note the sign of the second parameter and the order parameter. The sort is in the reverse order of the Date parameter, which preserves the current sort order. As a result, the second parameter, which identifies the number of rows to use in the calculation, must be positive to capture the previous months.

Technically, this computation does not capture the prior quarter, since it includes the current quarter as part of the computation. You can use the following column to capture the rolling average of the preceding month, which then becomes the true rolling average for the prior quarter. The window column refers to the name of the column generated from the previous step:

window value: NEXT(window, 1) order: -Date

Note that the order parameter must be preserved. This new column, window1, contains your prior quarter rolling average:

rename col:window1 to:'Amount_PriorQtr'

You can reformat this numeric value:

set col:Amount_PriorQtr value:NUMFORMAT(Amount_PriorQtr, '###.00')

You can use the following transform to calculate the net change. This formula computes the change as a percentage of the prior quarter and then formats it as a two-digit percentage.

derive type:single value:NUMFORMAT(((Amount - Amount_PriorQtr) / Amount_PriorQtr) * 100, '##.##') as:'NetChangePct_PriorQtr' 

Results:

• ROLLINGSUM - computes a rolling sum from a window of rows before and after the current row. See ROLLINGSUM Function.
• ROLLINGAVERAGE - computes a rolling average from a window of rows before and after the current row. See ROLLINGAVERAGE Function.
• ROWNUMBER - computes the row number for each row, as determined by the ordering column. See ROWNUMBER Function.

The following dataset contains sales data over the final quarter of the year. 

Source:

Transform:

First, you want to maintain the row information as a separate column. Since data is ordered already by the Date column, you can use the following:

window value:ROWNUMBER() order:Date

Rename this column to rowId for week of quarter.

Now, you want to extract month and week information from the Date values. Deriving the month value:

derive type:single value:MONTH(Date) as:'Month'

Deriving the quarter value:

derive type:single value:(1 + FLOOR(((month-1)/3))) as:'QTR'

Deriving the week-of-quarter value:

window value:ROWNUMBER() order:Date group:QTR

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

window value:ROWNUMBER() group:Month order:Date

Rename this column WOM (week of month).

Now, you perform your rolling computations. Compute the running total of sales using the following:

window value: ROLLINGSUM(Sales, -1, 0) order: Date group:QTR

The -1 parameter is used in the above computation to gather the rolling sum of all rows of data from the current one to the first one. Note that the use of the QTR column for grouping, which moves the value for the 01/01/2017 into its own computational bucket. This may or may not be preferred.

Rename this column QTD (quarter to-date). Now, generate a similar column to compute the rolling average of weekly sales for the quarter:

window value: ROUND(ROLLINGAVERAGE(Sales, -1, 0)) order: Date group:QTR

Since the ROLLINGAVERAGE function can compute fractional values, it is wrapped in the ROUND function for neatness. Rename this column avgWeekByQuarter.

Results:

When the unnecessary columns are dropped and some reordering is applied, your dataset should look like the following:

• 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.

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.

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:

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.