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Comment: Published by Scroll Versions from space DEV and version r089

D toc

Excerpt

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

D s lang vs sql

D s
snippetBasic

Column reference example:

D lang syntax
RawWrangletrue
Typeref
showNotetrue
WrangleTextderive type:single value:right(MyString,3)

right(MyString,3)

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

String literal example:

D lang syntax
RawWrangletrue
Typeref
showNotetrue
WrangleTextderive type:single value:right('Hello, World',5)

right('Hello, World',5)

Output: Returns the string: World.

D s
snippetSyntax

D lang syntax
RawWrangletrue
Typesyntax
showNotetrue
WrangleTextderive type:single value:right(column_string,end_count)

right(column_string,end_count)


ArgumentRequired?Data TypeDescription
column_stringYstringName of the column or string literal to be applied to the function
end_countYinteger (positive)Count of characters from the end of the source string to apply to the match

D s lang 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.

D s
snippetusage

Required?Data TypeExample Value
YesString literal or column referencemyColumn

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.

D s
snippetusage

Required?Data TypeExample Value
YesInteger (non-negative)5

D s
snippetExamples

Example - Parse segments of social security numbers

Social security numbers follow a regular format:

Code Block
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:

Info

NOTE: 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.

ParticipantIdSocialNum
1001805-88-2013
1002845221914
1003865 22 9291
1004892-732213

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:

D trans
RawWrangletrue
p03Value''
Typestep
WrangleTextreplace col: SocialNum on: '-' with: '' global: true
p01NameColumn
p01ValueSocialNum
p02NameFind
p02Value'-'
p03NameReplace with
p04Valuetrue
p04NameMatch all occurrences
SearchTermReplace text or pattern

D trans
RawWrangletrue
p03Value''
Typestep
WrangleTextreplace col: SocialNum on: ' ' with: '' global: true
p01NameColumn
p01ValueSocialNum
p02NameFind
p02Value' '
p03NameReplace with
p04Valuetrue
p04NameMatch all occurrences
SearchTermReplace text or pattern

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

ParticipantIdSocialNum
1001805882013
1002845221914
1003865229291
1004892732213

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

D trans
RawWrangletrue
p03Value'SSN_area'
Typestep
WrangleTextderive type:single value: left(SocialNum, 3) as: 'SSN_area'
p01NameFormula type
p01ValueSingle row formula
p02NameFormula
p02Valueleft(SocialNum, 3)
p03NameNew column name
SearchTermNew formula

D trans
RawWrangletrue
p03Value'SSN_group'
Typestep
WrangleTextderive type:single value: substring(SocialNum, 3,5) as: 'SSN_group'
p01NameFormula type
p01ValueSingle row formula
p02NameFormula
p02Valuesubstring(SocialNum, 3,5)
p03NameNew column name
SearchTermNew formula

D trans
RawWrangletrue
p03Value'SSN_serial'
Typestep
WrangleTextderive type:single value: right(SocialNum, 4) as: 'SSN_serial'
p01NameFormula type
p01ValueSingle row formula
p02NameFormula
p02Valueright(SocialNum, 4)
p03NameNew column name
SearchTermNew formula

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

D trans
RawWrangletrue
p03ValueSSN_area
Typestep
WrangleTextmove col: SSN_serial after: SSN_area
p01NameColumn(s)
p01ValueSSN_serial
p02NameOption
p02ValueAfter
p03NameColumn
SearchTermMove columns

D trans
RawWrangletrue
p03ValueSSN_area
Typestep
WrangleTextmove col: SSN_group after: SSN_area
p01NameColumn(s)
p01ValueSSN_group
p02NameOption
p02ValueAfter
p03NameColumn
SearchTermMove columns

D trans
RawWrangletrue
Typestep
WrangleTextdrop col:SocialNum
p01NameColumns
p01ValueSocialNum
p02NameAction
p02ValueDelete selected columns
SearchTermDelete columns

Results:

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

ParticipantIdSSN_areaSSN_groupSSN_serial
1001805882013
1002845221914
1003865229291
1004892732213

D s also
labelstring