EXAMPLE - Settype Transform

This example illustrates how to clean up data that has been interpreted as numeric in nature, when it is actually String or a structured string type, such as Gender. This example uses:


The following example contains customer ID and Zip code information in two columns. When this data is loaded into the Transformer page, it is initially interpreted as numeric, since it contains all numerals.

The four-digit ZipCode values should have five digits, with a 0 in front.



CustId column: This column needs to be retyped as String values. You can set the column data type to String through the column drop-down, which is rendered as the following transform:

settype col:CustId type:'String'

While the column is now of String type, future transforms might cause it to be re-inferred as Integer values. To protect against this possibility, you might want to add a marker at the front of the string. This marker should be removed prior to execution.

The basic method is to create a new column containing the customer ID marker (C) and then merge this column and the existing CustId column together. It's useful to add such an indicator to the front in case the customer identifier is a numeric value that could be confused with other numeric values. Also, this merge step forces the value to be interpreted as a String value, which is more appropriate for an identifier.

merge col:'C', CustId

You can now drop the CustId columns and rename the new column as CustId.

ZipCode column: This column needs to be converted to valid Zip Code values. For ease of use, this column should be of type String:

settype col:ZipCode type:'Zipcode'

The transform below changes the value in the ZipCode column if the length of the value is four in any row. The new value is the original value prepended with the numeral 0:

set col: ZipCode value: if(len($col) == 4, merge(['0',$col]), $col)

This column might now be re-typed as Zipcode type.



Remember to remove the C marker from the CustId column. Select the C value in the CustId column and choose the replace transform. You might need to re-type the cleaned data as String data.

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