ROLLINGAVERAGE Function

Functions:

Source:

The following dataset contains order information for the preceding 12 months. You want to compare the current month's average against the preceding quarter. 

Transformation:

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

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:

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

You can reformat this numeric value:

You can use the following transformation 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.

Results:

Functions:

Also:

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

Source:

Transformation:

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:

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:

Deriving the quarter value:

Deriving the week-of-quarter value:

Rename this column WOQ (week of quarter).

Deriving the week-of-month value:

Rename this column WOM (week of month).

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

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:

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:

Functions:

Also:

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.

Transformation:

Primary key: Since the quarter information repeats every lap, there is no unique identifier for each row. The following steps create this identifier:

Get split times: Use the following transform to break down the splits for each quarter of the race:

Compute rolling computations: You can use the following types of computations to provide rolling metrics on the current and three previous splits:

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:

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:

However, you must rename all of the generated windowX columns.