# PREV Function

Extracts the value from a column that is a specified number of rows before 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 previous value is missing or null, this function generates a missing value.

You can use the

`group`

and`order`

parameters 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

prev(myNumber, 1) order:Date

**Output:** Returns the value in the row in the `myNumber`

column immediately preceding the current row, when ordered by `Date`

.

## Syntax and Arguments

prev(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 before 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 before 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 before the current one from which to extract the value.

Value must be a positive integer. For negative values, see NEXT Function.

`k=1`

represents the immediately preceding 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

`group`

parameter 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

**Astuce**

For additional examples, see Common Tasks.

### Example - Examine prior order history

This example describes how you can use the PREV function to analyze data that is available in a window in rows before the current one.

**Functions:**

Item | Description |
---|---|

PREV Function | Extracts the value from a column that is a specified number of rows before the current value. |

IF Function | The |

The following dataset contains orders for multiple customers over a period of a few days, listed in no particular order. You want to assess how order size has changed for each customer over time and to provide offers to your customers based on changes in order volume.

**Source:**

Date | CustId | OrderId | OrderValue |
---|---|---|---|

1/4/16 | C001 | Ord002 | 500 |

1/11/16 | C003 | Ord005 | 200 |

1/20/16 | C002 | Ord007 | 300 |

1/21/16 | C003 | Ord008 | 400 |

1/4/16 | C001 | Ord001 | 100 |

1/7/16 | C002 | Ord003 | 600 |

1/8/16 | C003 | Ord004 | 700 |

1/21/16 | C002 | Ord009 | 200 |

1/15/16 | C001 | Ord006 | 900 |

**Transformation:**

When the data is loaded into the Transformer page, you can use the `PREV`

function to gather the order values for the previous two orders into a new column. The trick is to order the `window`

transform by the date and group it by customer:

Transformation Name | |
---|---|

Parameter: Formulas | PREV(OrderValue, 1) |

Parameter: Group by | CustId |

Parameter: Order by | Date |

Transformation Name | |
---|---|

Parameter: Formulas | PREV(OrderValue, 2) |

Parameter: Group by | CustId |

Parameter: Order by | Date |

Transformation Name | |
---|---|

Parameter: Option | Manual rename |

Parameter: Column | window |

Parameter: New column name | 'OrderValue_1' |

Transformation Name | |
---|---|

Parameter: Option | Manual rename |

Parameter: Column | window1 |

Parameter: New column name | 'OrderValue_2' |

You should now have the following columns in your dataset: `Date`

, `CustId`

, `OrderId`

, `OrderValue`

, `OrderValue_1`

, `OrderValue_2`

.

The two new columns represent the previous order and the order before that, respectively. Now, each row contains the current order (`OrderValue`

) as well as the previous orders. Now, you want to take the following customer actions:

If the current order is more than 20% greater than the sum of the two previous orders, send a rebate.

If the current order is less than 90% of the sum of the two previous orders, send a coupon.

Otherwise, send a holiday card.

To address the first one, you might add the following, which uses the `IF`

function to test the value of the current order compared to the previous ones:

Transformation Name | |
---|---|

Parameter: Formula type | Single row formula |

Parameter: Formula | IF(OrderValue >= (1.2 * (OrderValue_1 + OrderValue_2)), 'send rebate', 'no action') |

Parameter: New column name | 'CustomerAction' |

You can now see which customers are due a rebate. Now, edit the above and replace it with the following, which addresses the second condition. If neither condition is valid, then the result is `send holiday card`

.

Transformation Name | |
---|---|

Parameter: Formula type | Single row formula |

Parameter: Formula | IF(OrderValue >= (1.2 * (OrderValue_1 + OrderValue_2)), 'send rebate', IF(OrderValue <= (1.2 * (OrderValue_1 + OrderValue_2)), 'send coupon', 'send holiday card')) |

Parameter: New column name | 'CustomerAction' |

**Results:**

After you delete the `OrderValue_1`

and `OrderValue_2`

columns, your dataset should look like the following. Since the transformations with `PREV`

functions grouped by `CustId`

, the order of records has changed.

Date | CustId | OrderId | OrderValue | CustomerAction |
---|---|---|---|---|

1/4/16 | C001 | Ord001 | 100 | send rebate |

1/7/16 | C001 | Ord002 | 500 | send rebate |

1/15/16 | C001 | Ord006 | 900 | send rebate |

1/8/16 | C003 | Ord004 | 700 | send rebate |

1/11/16 | C003 | Ord005 | 200 | send rebate |

1/21/16 | C003 | Ord008 | 400 | send coupon |

1/7/16 | C002 | Ord003 | 600 | send rebate |

1/20/16 | C002 | Ord007 | 300 | send rebate |

1/21/16 | C002 | Ord009 | 200 | send coupon |