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MatchRegEx() compares the input string with one or more specified regular expression patterns, and returns the numeric location of the regular expression patterns that match. If no matches are found, the function returns 0. This function looks for exact matches only.
This function performs regex operations that are case-sensitive. You can alternatively use the variant MatchRegExI() to perform case-insensitive regex operations.
Syntax:
MatchRegEx
(text, regex1 [ , regex2,...regexN])
Return data type: integer
Arguments
Argument
Description
text
The input string text to which you want to match one or more regular expressions.
regex
The regular expression to try to match to the input string. You can specify more than one regular expression in a single expression, separated as subsequent arguments.
Returns 3 .The third regex pattern is the first to match the data.
MatchRegEx('ABC','[abc]+','[123]')
Returns 0, because MatchRegEx() is case-sensitive.
MatchRegExI('ABC','[abc]+','[123]')
Returns 1 .The case-insensitive variant of the function, MatchRegExI(), is used.
When to use it
Use cases for this function include:
Validating text to meet formatting and compliance standards, and to identify errors in data.
Identifying differences in how text is formatted. For example, you can identify how many phone numbers use format A, format B, and so on.
Example 1 – load script to analyze phone number formats
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains:
A table BusinessContactInfo containing contact phone numbers for various businesses. The phone numbers are 10-digit NANP phone numbers.
A calculated field, PhoneNumberFormat, to assess whether each phone number matches one of a specific set of phone number formats.
There are three different formats that we want to check for: (###) ###-####, ###-###-####, and ##########.
Load script
BusinessContactInfo:
Load MatchRegEx(ContactInfo,'\({0,1}[0-9]{3}\) [0-9]{3}-[0-9]{4}', '[0-9]{3}-[0-9]{3}-[0-9]{4}', '[0-9]{10}') as PhoneNumberFormat,
* Inline `
ID CompanyName ContactInfo
1 Company A (123) 456-7890
2 Company B 0123456790
3 Company C 234-567-8901
4 Company D (024) 680-2456
5 Company E (135) 791-3579
6 Company F 8901234567
7 Company G 235-235-2352
8 Company H (555) (555) (5555)
9 Company I 2342342343
10 Company J 444-444-4444
` (delimiter is '\t');
Results
Load the data and open a sheet. Create a new table and add these fields as dimensions:
CompanyName
ContactInfo
PhoneNumberFormat
Results table
CompanyName
ContactInfo
PhoneNumberFormat
Company A
(123) 456-7890
1
Company B
0123456790
3
Company C
234-567-8901
2
Company D
(024) 680-2456
1
Company E
(135) 791-3579
1
Company F
8901234567
3
Company G
235-235-2352
2
Company H
(555) (555) (5555)
0
Company I
2342342343
3
Company J
444-444-4444
2
From here, for example, you could create KPIs to calculate the total count of each phone number format, as well as the number of phone numbers that do not have a recognized format:
Examples of aggregation expressions to count occurrences of each format
Example 2 – chart expression to identify ISBN format
Overview
Open the Data load editor and add the load script below to a new tab.
The load script contains:
A table Books listing ISBN codes for various books.
From this data, we want to identify whether each ISBN code is a valid 10-digit or 13-digit code, based on syntax specified in a regex pattern.
Load script
Books:
Load * Inline [
Book
ISBN 123-3-1234-1234-0
ISBN 012-2-0123-0123-4
ISBN 000-1-0123-0123-2
ISBN 0-111-23456-7
ISBN 555-2-5555-5555-3
ISBN 222-4-2222-2222-2
ISBN 1-901-23456-8
ISBN 333-3-3333-3333-3
ISBN 555-1-5151-5151-3
ISBN 232-1-2323-2323-1
ISBN 2-444-44444-4
ISBN 888-0-9999-0000-0
];
Results
Load the data and open a sheet. Create a new table and add this field as a dimension:
Book
Add this calculated dimension, and give it the label ISBN Format:
