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EXAMPLE - LISTIF Functions

This section provides simple examples for how to use the ANYIF and LISTIF functions. These functions include the following:

  • ANYIF - Identifies a single value from a group that meets a specific condition. See ANYIF Function.

  • LISTAIF - Lists all values within a group that meet a specified condition. See LISTIF Function.

Source:

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

EmployeeId

Date

Sales

S001

1/23/17

25

S002

1/23/17

40

S003

1/23/17

48

S001

1/24/17

81

S002

1/24/17

11

S003

1/24/17

25

S001

1/25/17

9

S002

1/25/17

40

S003

1/25/17

S001

1/26/17

77

S002

1/26/17

83

S003

1/26/17

S001

1/27/17

17

S002

1/27/17

71

S003

1/27/17

29

S001

1/28/17

S002

1/28/17

S003

1/28/17

14

S001

1/29/17

2

S002

1/29/17

7

S003

1/29/17

99

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:

EmployeeId

Date

listif_Sales

S001

1/23/17

25

S002

1/23/17

40

S003

1/23/17

48

S001

1/24/17

81

S002

1/24/17

11

S003

1/24/17

25

S001

1/25/17

40

S002

1/25/17

S003

1/25/17

66

S001

1/26/17

77

S002

1/26/17

83

S003

1/26/17

S001

1/27/17

17

S002

1/27/17

71

S003

1/27/17

29

S001

1/28/17

S002

1/28/17

S003

1/28/17

S001

1/29/17

S002

1/29/17

S003

1/29/17