ROLLINGVARSAMP Function
Computes the rolling variance of values forward or backward of the current row within the specified column using the sample statistical method.
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 variance 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
and0
, which computes the rolling function from the current row back to the first row of the dataset.
This function works with the Window transform. See Window Transform.
注記
This function applies to a sample of the entire population. More information is below.
Terms...
Relevant terms:
Term | Description |
---|---|
Population | Population statistical functions are computed from all possible values. See https://en.wikipedia.org/wiki/Statistical_population. |
Sample | Sample-based statistical functions are computed from a subset or sample of all values. See https://en.wikipedia.org/wiki/Sampling_(statistics). These function names include 注記 Statistical sampling has no relationship to the samples taken within the product. When statistical functions are computed during job execution, they are applied across the entire dataset. Sample method calculations are computed at that time. |
For more information on a non-rolling version of this function, see VAR 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:
rollingvarsamp(myCol)
Output: Returns the rolling variance of all values in the myCol
column from the first row of the dataset to the current one using the sample method of calculation.
Rows before example:
<span>rollingvarsamp</span>(myNumber, 100)
Output: Returns the rolling variance of the current row and the 100 previous row values in the myNumber
column using the sample method of calculation.
Rows before and after example:
<span>rollingvarsamp</span>(myNumber, 3, 2)
Output: Returns the rolling variance of the three previous row values, the current row value, and the two rows after the current one in the myNumber
column using the sample method of calculation.
Syntax and Arguments
<span>rollingvarsamp</span>(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 you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.
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 function, 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
ヒント
For additional examples, see Common Tasks.
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.