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EXAMPLE - Change data type transformation

This example illustrates how to clean up data by changing its data type to String, manipulating it using String functions, and then retyping the data to its proper data type.

Functions:

Item

Description

IF Function

TheIFfunction allows you to build if/then/else conditional logic within your transforms.

LEN Function

Returns the number of characters in a specified string. String value can be a column reference or string literal.

MERGE Function

Merges two or more columns of String type to generate output of String type. Optionally, you can insert a delimiter between the merged values.

Source:

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

ZipCode

4020123

1234

2012121

94105

3212012

94101

1301212

2020

Transformation:

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 transformation:

Transformation Name

Change column data type

Parameter: Columns

CustId

Parameter: New type

String

While the column is now of String type, future transformations 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.

Transformation Name

Merge columns

Parameter: Columns

'C',CustId

You can now delete 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:

Transformation Name

Change column data type

Parameter: Columns

ZipCode

Parameter: New type

Zipcode

The transformation 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:

Transformation Name

Edit column with formula

Parameter: Columns

ZipCode

Parameter: Formula

if(len($col) == 4, merge(['0',$col]), $col)

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

Results:

CustId

ZipCode

C4020123

01234

C2012121

94105

C3212012

94101

C1301212

02020

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.