Question
Handling data loss when using the collapse command for aggregation
Neel Lane
0 reputation · 12 Sept 2024, 12:21 UTC
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0 reputation · 12 Sept 2024, 12:21 UTC
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?
Stata does not offer a native option to send collapse results to a separate memory buffer while keeping the original dataset active. collapse always replaces the in-memory dataset with the aggregated table. To run multiple different aggregations from the same source without repeatedly reloading the original file from disk, keep one copy of the raw data available and reset to it between aggregations using preserve/restore.
collapse aggregates observations by the grouping variables in by() and keeps only the statistics you request. Every other observation-level variable is dropped from memory. The operation is destructive in memory, so any identifier or raw variable not named in the varlist is lost unless you saved or preserved the data first.
statapreserve once before the first aggregation to snapshot the current state.collapse, save or analyze the summary, then restore.preserve
collapse (mean) price, by(group_var)
save summary_mean.dta, replace
restore
preserve
collapse (sum) volume, by(group_var)
save summary_sum.dta, replace
restoreIf the preserve snapshot itself is a concern, save the raw data once to a temporary file and reload that copy between runs. This still avoids re-reading or re-importing the original source.
save temp_original.dta, replace
collapse (mean) price, by(group_var)
save summary_mean.dta, replace
use temp_original.dta, clear
collapse (sum) volume, by(group_var)
save summary_sum.dta, replaceIf you need both the aggregated values and the raw rows in one dataset, collapse into a separate file and merge it back onto the original using the grouping variables as keys, rather than collapsing in place.
describe before and after collapse to compare variable lists and observation counts; a sudden drop signals unintended loss.count or isid group_var on the collapsed data to confirm one row per group.Confirmed: collapse overwrites memory and no new-buffer option exists. Assumption worth checking on your version: preserve snapshots the dataset efficiently, so restore is generally cheaper than re-importing the source, but exact behavior can vary by Stata version.
One diagnostic that would change the recommendation: do you need all summaries simultaneously in a single dataset, or only sequentially? If simultaneously, prefer the collapse-to-file-and-merge approach over repeated preserve/restore cycles.
Use comments to ask for clarification. Post a solution as an answer.
30,775 reputation · 12 Sept 2024, 21:40 UTC
One clarification worth adding: since Stata 16, frames provide exactly the "separate memory buffer" the question asks about, so preserve/restore is no longer the only option. You can copy the source data into a second frame, collapse there, and the original frame stays untouched:
frame copy default work
frame change work
collapse (mean) price, by(group_var)
frame change defaultYou can repeat this with a fresh frame copy per aggregation, or pull summary values back with frlink/frget if you need aggregated columns merged onto the raw rows.
Two cautions: frames still duplicate data in memory, so for genuinely large datasets the temp-file pattern from the answer may be lighter. And note that collapse's saving() option does not spare the in-memory dataset — the collapse is destructive regardless; saving() only writes the result to disk as well. Verify behavior on your version with help frames and a quick describe after switching back.