Handling data loss when using the collapse command for aggregation

Dataset Reduction via Collapse

The collapse command in Stata is used to aggregate data into summary statistics, effectively transforming the dataset in memory into a summary table based on specified grouping variables.

Because this operation replaces the active dataset with the aggregated results, the original observation-level data is removed from memory. This behavior creates a design conflict when a workflow requires both the summarized results and the original raw data for subsequent analysis or validation.

While preserve and restore are available for temporary state management, there is uncertainty regarding the most efficient way to maintain both data structures without excessive disk I/O or memory overhead in large datasets.

Which strategy is recommended to perform multiple different aggregations on the same source data without repeatedly reloading the dataset from disk? Does Stata offer a native way to output collapse results to a new memory buffer rather than overwriting the current one?