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 the
RIGHTfunction 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
RIGHTfunction 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
Sugerencia
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 | |
|---|---|
Parameter: Column | SocialNum |
Parameter: Find | '-' |
Parameter: Replace with | '' |
Parameter: Match all occurrences | true |
Transformation Name | |
|---|---|
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 | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | left(SocialNum, 3) |
Parameter: New column name | 'SSN_area' |
Transformation Name | |
|---|---|
Parameter: Formula type | Single row formula |
Parameter: Formula | substring(SocialNum, 3,5) |
Parameter: New column name | 'SSN_group' |
Transformation Name | |
|---|---|
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 | |
|---|---|
Parameter: Column(s) | SSN_serial |
Parameter: Option | After |
Parameter: Column | SSN_area |
Transformation Name | |
|---|---|
Parameter: Column(s) | SSN_group |
Parameter: Option | After |
Parameter: Column | SSN_area |
Transformation Name | |
|---|---|
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 |