What is the main difference between using the SET and MERGE statements in a DATA step?

The distinction between the SET and MERGE statements in a DATA step fundamentally revolves around how they process and combine datasets. When utilizing the SET statement, SAS reads data from one or more datasets sequentially and stacks them on top of each other. This allows all observations from the specified datasets to be appended into a single output dataset, where each observation retains its original structure and attributes.

In contrast, the MERGE statement is designed to combine datasets based on shared BY variable values, effectively merging them horizontally. This method aligns observations with the same values of the BY variables, creating a new dataset where the columns from the merged datasets are combined according to these keys.

The nuances of using SET and MERGE are crucial for manipulating datasets effectively in SAS. While the stacking behavior of SET allows accumulation of data from multiple sources, the merging capability of MERGE facilitates a more relational-like integration based on specific criteria.

Other potential answers reference specifics around performance and data requirements. For instance, while MERGE does require the datasets to be sorted by the BY variables before execution, this does not fundamentally define the operational difference between SET and MERGE. The performance speed is context-dependent and can vary based on many factors beyond just the nature of the statement used. The requirement for BY