IFMISMATCHED Function

Name of the column, string literal, or function to be tested for data type matches.

Usage Notes:

myColumn

Literal value or string array that identifies data type to which to validate the source column or string.

Usage Notes:

'Integer'

Valid data type strings:

When referencing a data type within a transform, you can use the following strings to identify each type:

'String'

'Integer'

'Float'

'Bool'

'SSN'

'Phone'

'Emailaddress'

'Creditcard'

'Gender'

'Map'

'Array'

'Ipaddress'

'Url'

'Httpcodes'

'Zipcode'

'State'

'Datetime'

The output value to write if the tested value is mismatched for the specified data type.

Usage Notes:

'Data type mismatch'

This example illustrates how to use the IF* functions for data type validation.

Functions:

Source:

The following simple table lists zip codes by customer identifier:

Transformation:

When the above is imported into the Transformer page, you notice the following:

First, you test for valid values in the custZip column. Using the IFVALID function, you can validate against any data type:

Single row formula

IFVALID(custZip, 'Zipcode', 'ok')

'status'

Fix four-digit zips: In the status column are instances of ok for the top four rows. You notice that the bottom two rows contain four-digit codes.

Since the custZip values were originally imported as Integer, any leading 0 values are deleted. In this case, you can add back the leading zero. Before the previous step, change the data type of zip to String and insert the following:

Single row formula

IF(LEN(custZip)==4,'0','')

'FourDigitZip'

Single row formula

MERGE([FourDigitZip,custZip])

'custZip2'

zip

custZip2

FourDigitZip,custZip2

Delete selected columns

Now, when you click the last recipe step, you should see that two more rows in status are listed as Ok.

For the zip code with the three-digit extension, you can simply remove that extension to make it valid. Click the step above the last one. In the data grid, highlight the value. Click the Replace suggestion card. Select the option that uses the following for the matching pattern:

'-{digit}{3}{end}'

The above means that all three-digit extensions are deleted from the zip. You can do the same for any two- and one-digit extensions, although there are none in this sample.

Missing and null values: Now, you need to address how to handle missing and null values. The IFMISSING tests for both missing and null values, while the IFNULL tests just for null values. In this example, you want to delete null values, which could mean that the data for that row is malformed and to write a status of missing for missing values.

Click above the last line in the recipe to insert the following:

custZip

IFNULL(custZip, 'xxxxx')

custZip

IFMISSING(custZip, '00000')

Now, when you click the last line of the recipe, only the null value is listed as having a status other than ok. You can use the following to remove this row and all like it:

Custom formula

Custom single

(status == 'xxxxx')

Delete matching rows

Results:

As an exercise, you might repeat the above steps starting with the IFMISMATCHED function determining the value in the status column:

Single row formula

IFMISMATCHED(custZip, 'Zipcode', 'mismatched')

'status'