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RIGHT Function

Matches the right set of characters in a string, as specified by parameter. The string can be specified as a column reference or a string literal.

  • Since theRIGHTfunction matches based on fixed numeric values, changes to the length or structure of a data field can cause your recipe to fail to properly execute.

  • TheRIGHTfunction requires an integer value for the number of characters to match. If you need to match strings using patterns, you should use theENDSWITHtransform instead. See ENDSWITH Function.

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

Column reference example:

right(MyString,3)

Output: Returns the rightmost (last) three letters of the MyName column value.

String literal example:

right('Hello, World',5)

Output: Returns the string: World.

Syntax and Arguments

right(column_string,end_count)

Argument

Required?

Data Type

Description

column_string

Y

string

Name of the column or string literal to be applied to the function

end_count

Y

integer (positive)

Count of characters from the end of the source string to apply to the match

For more information on syntax standards, see Language Documentation Syntax Notes.

column_string

Name of the column or string constant to be searched.

  • Missing string or column values generate missing string results.

  • String constants must be quoted ('Hello, World').

  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

String literal or column reference

myColumn

end_count

Count of characters from the right end of the string to include in the match.

  • Value must a non-negative integer. If the value is 0, then the match fails for all strings.

  • If this value is greater than the length of the string, then the match is the entire string.

  • References to columns of integer data type are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

Integer (non-negative)

5

Examples

Suggerimento

For additional examples, see Common Tasks.

Example - Parse segments of social security numbers

Social security numbers follow a regular format:

XXX-XX-XXXX

Each of the separate numeric groups corresponds to a specific meaning:

  • XXX - Area value that corresponds to a geographic location that surrounds the SSN applicant's address

  • XX - Group number identifies the order in which the numbers are assigned within an area

  • XXX - Serial number of the individual within the area and group groupings.

  • For more information, see http://www.usrecordsearch.com/ssn.htm.

Source:

You want to analyze some social security numbers for area, group, and serial information. However, your social security number data is messy:

Nota

The following sample contains invalid social security numbers for privacy reasons. If you use this data in the application, it fails validation for the SSN data type.

ParticipantId

SocialNum

1001

805-88-2013

1002

845221914

1003

865 22 9291

1004

892-732213

Transformation:

When the above data is imported, the SocialNum column might or might not be inferred as SSN data type. Either way, you should clean up your data, using the following transforms:

Transformation Name

Replace text or pattern

Parameter: Column

SocialNum

Parameter: Find

'-'

Parameter: Replace with

''

Parameter: Match all occurrences

true

Transformation Name

Replace text or pattern

Parameter: Column

SocialNum

Parameter: Find

' '

Parameter: Replace with

''

Parameter: Match all occurrences

true

At this point, your SocialNum data should be inferred as SSN type and consistently formatted as a set of digits:

ParticipantId

SocialNum

1001

805882013

1002

845221914

1003

865229291

1004

892732213

From this more consistent data, you can now break out the area, group, and serial values from the column:

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

left(SocialNum, 3)

Parameter: New column name

'SSN_area'

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

substring(SocialNum, 3,5)

Parameter: New column name

'SSN_group'

Transformation Name

New formula

Parameter: Formula type

Single row formula

Parameter: Formula

right(SocialNum, 4)

Parameter: New column name

'SSN_serial'

If desired, you can re-order the three new columns and delete the source column:

Transformation Name

Move columns

Parameter: Column(s)

SSN_serial

Parameter: Option

After

Parameter: Column

SSN_area

Transformation Name

Move columns

Parameter: Column(s)

SSN_group

Parameter: Option

After

Parameter: Column

SSN_area

Transformation Name

Delete columns

Parameter: Columns

SocialNum

Parameter: Action

Delete selected columns

Results:

If you complete the previous transform steps, your data should look like the following:

ParticipantId

SSN_area

SSN_group

SSN_serial

1001

805

88

2013

1002

845

22

1914

1003

865

22

9291

1004

892

73

2213