NEXT Function
Extracts the value from a column that is a specified number of rows after the current value.
The row from which to extract a value is determined by the order in which the rows are organized at the time that the function is executed.
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.
If the next value is missing or null, this function generates a missing value.
You can use the
groupandorderparameters to define the groups of records and the order of those records to which this function is applied.This function works with the following transforms:
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
next(myNumber, 1) order:Date
Output: Returns the value in the row in the myNumber column immediately after the current row when the dataset is ordered by Date.
Syntax and Arguments
next(col_ref, k_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 |
k_integer | Y | integer (positive) | Number of rows after the current one from which to extract the value |
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 extract the value that is k-integer values after the current one.
Multiple columns and wildcards are not supported.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | String (column reference) | myColumn |
k_integer
Integer representing the number of rows after the current one from which to extract the value.
Value must be a positive integer. For negative values, see PREV Function.
k=1represents the immediately following row value.If k is greater than or equal to the number of values in the column, all values in the generated column are missing. If a
groupparameter is applied, then this parameter should be no more than the maximum number of rows in the groups.If the range provided to the function exceeds the limits of the dataset, then the function generates a null value.
If the range of the function is valid but includes missing values, the function generates a missing, non-null value.
Usage Notes:
Required? | Data Type | Example Value |
|---|---|---|
Yes | Integer | 4 |
Examples
Tip
For additional examples, see Common Tasks.
Example - Examine prior order history
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:
Note
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 |