Example: Partitioning the SASHELP.CLASSFIT Data Set

1. In the Tasks section, expand the StandardPrepare DataTransform Data folders, and then double-click Partition Data. The user interface for the Partition Data task opens.
2. On the Data tab, select SASHELP.CLASSFIT as the input data set.
   TipIf the data source is not available from the drop-down list, click . In the Choose a Table window, expand the library that contains the data source that you want to use. Select the data source for the example and click OK. The selected data source now appears in the drop-down list.
3. In the Number of partitions box, enter 2.
4. In the Proportion of cases for partition 1 box, enter .5, which specifies 50% of the values should be in partition 1.
5. In the Proportion of cases for partition 2 box, enter .3, which specifies 30% of the values should be in partition 2.
6. From the Partition data sets drop-down list, select All partitions in one data set.
7. In the ID value for partition 1 data box, enter Test.
8. In the ID value for partition 2 data box, enter Train.
9. Under the Output Data heading, select Print output data to view the output data set in the results.
10. To run the task, click Run .

Here is a subset of the results:

The new _Partition_ variable in the output data set specifies the partition (either Train or Test) for the observation. For example, the data for Joyce is in the Test partition. The data for Janet is in the Test partition. This example does not specify a random seed. As a result, the task randomly assigns 50% of the observations to the Test partition and 30% of the observations to the Train partition. If you run this example again, you might see slightly different results.