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Release 5.0.1




The IFMISSING function writes out a specified value if the source value is a null or missing value. Otherwise, it writes the source value. Input can be a literal, a column reference, or a function.

  • The ISMISSING function simply tests if a value is missing. See ISMISSING Function.
  • Missing values are different from null values. To test for null values, see IFNULL Function.

Tip: Since this function captures both missing and null values, you may first wish to address the rows with null values using the IFNULL or ISNULL functions. Any remaining rows that are matched based on this function are exclusively missing values.


Basic Usage

derive type:single value:IFMISSING(my_score,'0') as:'final_score'

Output: Generates a new column called, final_score, which contains the value 0 if the value in my_score is a null or missing value. 

Syntax and Arguments

derive type:single value:IFMISSING(column_string, computed_value)

ArgumentRequired?Data TypeDescription
source_valueYstringName of column, string literal or function to be tested
output_valueystringString literal value to write

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


Name of the column, string literal, or function to be tested for missing values.

  • Missing literals or column values generate missing string results.
  • Multiple columns and wildcards are not supported.

Usage Notes:

Required?Data TypeExample Value
YesString literal, column reference, or functionmyColumn


The output value to write if the tested value returns a null or missing value.

Usage Notes:

Required?Data TypeExample Value
YesString or numeric literal'Missing input'


Tip: For additional examples, see Common Tasks.

Example - IF* functions for data type validation

This section provides simple examples for how to use the IF* functions for data type validation. These functions include the following:
  • IFNULL - For an input expression or value, this function returns the specified value if the input is a null value. See IFNULL Function.
  • IFMISSING - Returns the specified value if the input value or expression is a missing value. See IFMISSING Function.
  • IFMISMATCHED - Returns the specified value if the input value or expression is mismatched against the column's data type. See IFMISMATCHED Function.
  • IFVALID - Returns the specified value if the input value or expression is valid against the column's data type. See IFVALID Function


The following simple table lists zip codes by customer identifier:



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

  • The custZip column is typed as Integer.
  • There are two missing and two mismatched values in the custZip column.

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

derive type:single value:IFVALID(custZip, 'Zipcode', 'ok') as:'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 dropped. 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:

derive type:single value:IF(LEN(custZip)==4,'0','') as:'FourDigitZip'

derive type:single value: merge([FourDigitZip,custZip]) as:'custZip2'

set col:zip value:custZip2

drop col:FourDigitZip,custZip2

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:


The above means that all three-digit extensions are dropped 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:

set col:custZip value:IFNULL(custZip, 'xxxxx')

set col:custZip value: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:

delete row:(status == 'xxxxx')


C006 00000ok

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

derive type:single value:IFMISMATCHED(custZip, 'Zipcode', 'mismatched') as:'status'



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