Contents:
The cloudbased version of Trifacta Wrangler is now available! Read all about it, and register for your free account.
Contents:
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 transform 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
andorder
parameters to define the groups of records and the order of those records to which this transform is applied.  This function works with the following transforms:
Basic Usage
Output: Returns the value in the row in the myNumber
column immediately preceding the current row, when ordered by Date
.
Syntax
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 kinteger
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, nonnull value.
Usage Notes:
Required?  Data Type  Example Value 

Yes  Integer  4 
Examples
Example  Examine prior order history
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  Window 

Parameter: Formulas  PREV(OrderValue, 1) 
Parameter: Group by  CustId 
Parameter: Order by  Date 
Transformation Name  Window 

Parameter: Formulas  PREV(OrderValue, 2) 
Parameter: Group by  CustId 
Parameter: Order by  Date 
Transformation Name  Rename columns 

Parameter: Option  Manual rename 
Parameter: Column  window 
Parameter: New column name  'OrderValue_1' 
Transformation Name  Rename columns 

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  New formula 

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  New formula 

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. Note that since the transforms 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 
This page has no comments.