Demo: Managing Missing Values (Self-Paced)

In this demonstration, you learn to address missing values in the MISSING_PVA data set. The data set has quite a few variables with missing values. However, for brevity, let's focus on the Rep_DemMedIncome variable. Use the Imputation node in a flow to impute synthetic data values.

Reminder: If you restarted your SAS session, or it has timed out due to inactivity, you must re-create the course libraries, LOCALLIB and VST. To do this, run the AssignLibrary flow.

1. In SAS Viya, launch SAS Studio by clicking the Applications menu button (the three-by-three grid in the upper left corner), expand ANALYTICS LIFE CYCLE, and click Develop Code and Flows.
2. In the navigation panel on the far left, click the Explorer button (second from top). Expand Files >Home > workshop > VST. Double-click AssignLibrary.flw to open it.
3. Click the Run button on the flow toolbar.

Demo Steps

1. Open the previously created flow Data Preprocessing.flw from the VST folder.
2. Click on the Steps icon on the left side of the Navigation Pane. Under SAS Steps, expand Prepare Data, then double click on the Imputation step.
3. Right click on the Imputation step and choose Add output port.
4. Next, move your mouse towards the edge of the MISSING_PVA node until the pointer becomes a hand icon. Click and drag an arrow from MISSING_PVA node to the box on the left side of the Imputation node (the input port). Now Imputation is connected to the MISSING_PVA data.
5. Add another Table step to the flow by double clicking Table under Data (Input and Output). For this node, under Table Properties, enter VST for Library and IMPUTED_PVA in the Table name field.
6. Connect the output port of the Imputation node to the IMPUTED_PVA node. Use the Arrange nodes button to visually organize the nodes in the flow.




7. Click on the Imputation node, then select the Data tab.
8. Under Interval Variables, click the plus sign corresponding to Replace missing values with the mean and select the Rep_DemMedIncome variable. Then click OK to close the Column Selection window.

Note: Users also have the option to impute the missing values in interval variables with the median or a random number. If required, users can choose different methods for different variables.

Note: The Imputation step also supports imputing missing values in nominal variables by mode.
9. Click the Output tab. Make sure Save imputed data and Replace existing output table are both selected.

Note: To expedite the demonstration in the class setup, a CAS table in the VST library has already been saved with the same name. Thus, the Replace option is used.

10. Next, to include all the variables from the input CAS table in the output CAS table, click the All variables radio button under Include variables from the input CAS table.
11. Click Run to execute the flow. Save the file Data Preprocessing.flw. When the run is complete, view the results in the work area. You clearly see that a new input, IM_Rep_DemMedIncome, is added to the data set, and it has missing values replaced by a synthetic value ($53513, the mean in this case) and nonmissing values copied from the original input.





12. Click the Output Data tab to view a snapshot of the output table. Scroll your cursor to the right, and you see the new column, IM_Rep_DemMedIncome, that contains the value 53513 for every missing value in the Rep_DemMedIncome column. Because both the versions of the income variables are available in the data set, going forward, you should use the IM_Rep_DemMedIncome variable for analysis.