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Optimizing Stata for Analysis of Large Data Sets


  • Joseph Canner

    () (Johns Hopkins University School of Medicine)

  • Eric Schneider

    (Johns Hopkins School of Medicine)


As with most programming languages, there can be multiple ways to do a task in Stata. Using modern CPUs with adequate memory, most Stata data processing commands run so quickly on small- or moderate-sized data sets that it is impossible to tell whether one command performs more efficiently than another. However, when analyzing large data sets such as the Nationwide Inpatient Sample (NIS), with about 8 million records per year (~3.5GB), this choice can make a substantial difference in performance. Using the Stata timer command, we performed standardized benchmarks of common programming tasks, such as searching the NIS for a list of ICD-9 codes, converting string data to numeric, and putting numeric variables in categories. For example, the inlist function can achieve significant performance gains compared to the equivalent “var==exp1 | var==exp2” notation (38% improvement) or using foreach loops (300% improvement). Using the real and subinstr functions to remove characters from strings and convert them to numbers is about 20 times faster than the destring command. The inlist, inrange, and recode functions also perform considerably better than the equivalent recode commands (13-70 times faster), especially for string variables, and are often easier to write and to read.

Suggested Citation

  • Joseph Canner & Eric Schneider, 2013. "Optimizing Stata for Analysis of Large Data Sets," 2013 Stata Conference 10, Stata Users Group.
  • Handle: RePEc:boc:norl13:10

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