ROLLINGAVERAGE Function
Computes the rolling average 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 average of previous values is the value in the first row.
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
and0
, which computes the rolling average from the current row back to the first row of the dataset.
This function works with the Window transform. See Window Transform.
For more information on a non-rolling version of this function, see AVERAGE 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:
rollingaverage(myCol)
Output: Returns the rolling average of all values in the myCol
column.
Rows before example:
rollingaverage(myNumber, 3)
Output: Returns the rolling average of the current row and the three previous row values in the myNumber
column.
Rows before and after example:
rollingaverage(myNumber, 3, 2)
Output: Returns the rolling average 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
rollingaverage(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.
For more information on syntax standards, see Language Documentation Syntax Notes.
col_ref
Name of the column whose values are used to compute the rolling average.
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 average, including the current row. For example, if the first value is 5
, the current row and the five rows before 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 value0
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
Dica
For additional examples, see Common Tasks.
Example - Compute prior quarter values
This example covers how to use the NEXT function to create windows of data from the current row and subsequent (next) rows in the dataset. You can then apply rolling computations across these windows of data.
Functions:
Item | Description |
---|---|
NEXT Function | Extracts the value from a column that is a specified number of rows after the current value. |
ROLLINGAVERAGE Function | Computes the rolling average of values forward or backward of the current row within the specified column. |
NUMFORMAT Function | Formats a numeric set of values according to the specified number formatting. Source values can be a literal numeric value, a function returning a numeric value, or reference to a column containing an Integer or Decimal values. |
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.
Date | Amount |
---|---|
12/31/15 | 118 |
11/30/15 | 6 |
10/31/15 | 443 |
9/30/15 | 785 |
8/31/15 | 77 |
7/31/15 | 606 |
6/30/15 | 421 |
5/31/15 | 763 |
4/30/15 | 305 |
3/31/15 | 824 |
2/28/15 | 135 |
1/31/15 | 523 |
Transformation:
Using the ROLLINGAVERAGE
function, you can generate a column containing the rolling average of the current month and the two previous months:
Transformation Name | |
---|---|
Parameter: Formulas | ROLLINGAVERAGE(Amount, 3, 0) |
Parameter: Order by | -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:
Transformation Name | |
---|---|
Parameter: Formulas | NEXT(window, 1) |
Parameter: Order by | -Date |
Note that the order parameter must be preserved. This new column, window1
, contains your prior quarter rolling average:
Transformation Name | |
---|---|
Parameter: Option | Manual rename |
Parameter: Column | window1 |
Parameter: New column name | 'Amount_PriorQtr' |
You can reformat this numeric value:
Transformation Name | |
---|---|
Parameter: Columns | Amount_PriorQtr |
Parameter: Formula | NUMFORMAT(Amount_PriorQtr, '###.00') |
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.
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | NUMFORMAT(((Amount - Amount_PriorQtr) / Amount_PriorQtr) * 100, '##.##') |
Parameter: New column name | 'NetChangePct_PriorQtr' |
Results:
Nota
You might notice that there are computed values for Amount_PriorQtr
for February and March. These values do not factor in a full three months because the data is not present. The January value does not exist since there is no data preceding it.
Date | Amount | Amount_PriorQtr | NetChangePct_PriorQtr |
---|---|---|---|
12/31/15 | 118 | 411.33 | -71.31 |
11/30/15 | 6 | 435.00 | -98.62 |
10/31/15 | 443 | 489.33 | -9.47 |
9/30/15 | 785 | 368.00 | 113.32 |
8/31/15 | 77 | 596.67 | -87.1 |
7/31/15 | 606 | 496.33 | 22.1 |
6/30/15 | 421 | 630.67 | -33.25 |
5/31/15 | 763 | 421.33 | 81.09 |
4/30/15 | 305 | 494.00 | -38.26 |
3/31/15 | 824 | 329.00 | 150.46 |
2/28/15 | 135 | 523.00 | -.74.19 |
1/31/15 | 523 |
Example - Rolling window functions
This example describes how to use rolling computational functions.
Functions:
Item | Description |
---|---|
ROLLINGSUM Function | Computes the rolling sum of values forward or backward of the current row within the specified column. |
ROLLINGAVERAGE Function | Computes the rolling average of values forward or backward of the current row within the specified column. |
ROWNUMBER Function | Generates a new column containing the row number as sorted by the |
Also:
Item | Description |
---|---|
MONTH Function | Derives the month integer value from a Datetime value. Source value can be a a reference to a column containing Datetime values or a literal. |
FLOOR Function | Computes the largest integer that is not more than the input value. Input can be an Integer, a Decimal, a column reference, or an expression. |
ROWNUMBER Function | Generates a new column containing the row number as sorted by the |
The following dataset contains sales data over the final quarter of the year.
Source:
Date | Sales |
---|---|
10/2/16 | 200 |
10/9/16 | 500 |
10/16/16 | 350 |
10/23/16 | 400 |
10/30/16 | 190 |
11/6/16 | 550 |
11/13/16 | 610 |
11/20/16 | 480 |
11/27/16 | 660 |
12/4/16 | 690 |
12/11/16 | 810 |
12/18/16 | 950 |
12/25/16 | 1020 |
1/1/17 | 680 |
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:
Transformation Name | |
---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Order by | 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:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | MONTH(Date) |
Parameter: New column name | 'Month' |
Deriving the quarter value:
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | (1 + FLOOR(((month-1)/3))) |
Parameter: New column name | 'QTR' |
Deriving the week-of-quarter value:
Transformation Name | |
---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Group by | QTR |
Parameter: Order by | Date |
Rename this column WOQ
(week of quarter).
Deriving the week-of-month value:
Transformation Name | |
---|---|
Parameter: Formulas | ROWNUMBER() |
Parameter: Group by | Month |
Parameter: Order by | Date |
Rename this column WOM
(week of month).
Now, you perform your rolling computations. Compute the running total of sales using the following:
Transformation Name | |
---|---|
Parameter: Formulas | ROLLINGSUM(Sales, -1, 0) |
Parameter: Group by | QTR |
Parameter: Order by | Date |
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:
Transformation Name | |
---|---|
Parameter: Formulas | ROUND(ROLLINGAVERAGE(Sales, -1, 0)) |
Parameter: Group by | QTR |
Parameter: Order by | Date |
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:
Date | WOQ | Sales | QTD | avgWeekByQuarter |
---|---|---|---|---|
10/2/16 | 1 | 200 | 200 | 200 |
10/9/16 | 2 | 500 | 700 | 350 |
10/16/16 | 3 | 350 | 1050 | 350 |
10/23/16 | 4 | 400 | 1450 | 363 |
10/30/16 | 5 | 190 | 1640 | 328 |
11/6/16 | 6 | 550 | 2190 | 365 |
11/13/16 | 7 | 610 | 2800 | 400 |
11/20/16 | 8 | 480 | 3280 | 410 |
11/27/16 | 9 | 660 | 3940 | 438 |
12/4/16 | 10 | 690 | 4630 | 463 |
12/11/16 | 11 | 810 | 5440 | 495 |
12/18/16 | 12 | 950 | 6390 | 533 |
12/25/16 | 13 | 1020 | 7410 | 570 |
1/1/17 | 1 | 680 | 680 | 680 |
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 | |
---|---|
Parameter: Columns | lap,quarter |
Parameter: New type | String |
Transformation Name | |
---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | MERGE(['l',lap,'q',quarter]) |
Parameter: New column name | 'splitId' |
Get split times: Use the following transform to break down the splits for each quarter of the race:
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROUND(time_sc - PREV(time_sc, 1), 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | '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 | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROLLINGAVERAGE(split_time_sc, 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | 'ravg' |
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROLLINGMAX(split_time_sc, 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | 'rmax' |
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROLLINGMIN(split_time_sc, 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | 'rmin' |
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROUND(ROLLINGSTDEV(split_time_sc, 3), 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | 'rstdev' |
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROUND(ROLLINGVAR(split_time_sc, 3), 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | '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 | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROUND(ROLLINGSTDEVSAMP(split_time_sc, 3), 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | 'rstdev_samp' |
Transformation Name | |
---|---|
Parameter: Formula type | Multiple row formula |
Parameter: Formula | ROUND(ROLLINGVARSAMP(split_time_sc, 3), 3) |
Parameter: Order rows by | splitId |
Parameter: New column name | '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 awindow
transform such as the following:
Transformation Name | |
---|---|
Parameter: Formula1 | lap |
Parameter: Formula2 | rollingaverage(split_time_sc, 0, 3) |
Parameter: Formula3 | rollingmax(split_time_sc, 0, 3) |
Parameter: Formula4 | rollingmin(split_time_sc, 0, 3) |
Parameter: Formula5 | round(rollingstdev(split_time_sc, 0, 3), 3) |
Parameter: Formula6 | round(rollingvar(split_time_sc, 0, 3), 3) |
Parameter: Formula7 | round(rollingstdevsamp(split_time_sc, 0, 3), 3) |
Parameter: Formula8 | round(rollingvarsamp(split_time_sc, 0, 3), 3) |
Parameter: Group by | lap |
Parameter: Order by | lap |
However, you must rename all of the generated windowX
columns.