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Release 9.2


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This example illustrates you to identify and list all values within a group that meet a specified condition.

Functions:

ItemDescription
ANYIF Function Selects a single non-null value from rows in each group that meet a specific condition.
LISTIF Function Returns list of all values in a column for rows that match a specified condition.
WEEKDAY Function Derives the numeric value for the day of the week (1, 2, etc.). Input must be a reference to a column containing Datetime values.

Source:

The following data identifies sales figures by salespeople for a week:

EmployeeIdDateSales
S0011/23/1725
S0021/23/1740
S0031/23/1748
S0011/24/1781
S0021/24/1711
S0031/24/1725
S0011/25/179
S0021/25/1740
S0031/25/17 
S0011/26/1777
S0021/26/1783
S0031/26/17 
S0011/27/1717
S0021/27/1771
S0031/27/1729
S0011/28/17 
S0021/28/17 
S0031/28/1714
S0011/29/172
S0021/29/177
S0031/29/1799

 

Transformation:

In this example, you are interested in the high performers. A good day in sales is one in which an individual sells more than 80 units. First, you want to identify the day of week:

Transformation Name New formula
Parameter: Formula type Single row formula
Parameter: Formula WEEKDAY(Date)
Parameter: New column name 'DayOfWeek'

Values greater than 5 in DayOfWeek are weekend dates. You can use the following to identify if anyone reached this highwater marker during the workweek (non-weekend):

Transformation Name Pivot columns
Parameter: Rows labels EmployeeId,Date
Parameter: Values ANYIF(Sales, (Sales > 80 && DayOfWeek < 6))
Parameter: Max number of columns to create 1

Before adding the step to the recipe, you take note of the individuals who reached this mark in the anyif_Sales column for special recognition.

Now, you want to find out sales for individuals during the week. You can use the following to filter the data to show only for weekdays:

Transformation Name Pivot columns
Parameter: Rows labels EmployeeId,Date
Parameter: Values LISTIF(Sales, 1000, (DayOfWeek < 6))
Parameter: Max number of columns to create 1

To clean up, you might select and replace the following values in the listif_Sales column with empty strings:

["
"]
[]

Results:

EmployeeIdDatelistif_Sales
S0011/23/1725
S0021/23/1740
S0031/23/1748
S0011/24/1781
S0021/24/1711
S0031/24/1725
S0011/25/1740
S0021/25/17 
S0031/25/1766 
S0011/26/1777
S0021/26/1783
S0031/26/17 
S0011/27/1717
S0021/27/1771
S0031/27/1729
S0011/28/17 
S0021/28/17 
S0031/28/17 
S0011/29/17 
S0021/29/17 
S0031/29/17 

 

See Also for EXAMPLE - LISTIF Functions:

 

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