From Raw Data to Published Paper: The 7-Step Stata Pipeline
A step-by-step pipeline from raw .csv to published table: import, clean, construct, analyze, visualize, export, validate. With code at every step.
Sytra Team
Research Engineering Team, Sytra AI
Every empirical paper follows the same pipeline, whether the author knows it or not: import raw data, clean it, construct variables, run the analysis, produce output. Most researchers do this in an ad-hoc way — a single 2,000-line .do file that does everything. Here’s a structured 7-step pipeline that keeps your work clean, documented, and reproducible.
The import step converts raw files (.csv, .xlsx, .txt) to Stata’s native format. Run describe and codebook to verify that variables imported correctly. Check for string/numeric conversions and encoding issues.
Step 2: Clean
* 02_clean.do
use "$inter/survey_raw.dta", clear
* Rename variables to standard names
rename q1_income income
rename q2_age age
* Recode missing values
mvdecode income age, mv(-99 = . \ -88 = .a)
* Remove duplicates
duplicates report id
duplicates drop id, force
* Label variables
label variable income "Annual household income (USD)"
label variable age "Age at interview (years)"
save "$inter/survey_clean.dta", replace
Step 3: Construct
* 03_construct.do
use "$inter/survey_clean.dta", clear
* Treatment variable
gen treatment = (year >= reform_year & state_treated == 1)
esttab main sub_* using "$tables/table2.tex", replace ///
se star(* 0.10 ** 0.05 *** 0.01) ///
booktabs label nomtitle ///
stats(N r2, labels("N" "\$R^2\$"))
Step 7: Validate
* Validation checks
assert _N > 1000
assert r(r2) > 0.01
count if missing(treatment)
assert r(N) == 0
Add assertions throughout your pipeline. They catch data corruption, dropped observations, and logical errors before they propagate to your results. If an assertion fails, the .do file stops — which is what you want.
The Pipeline as a Mindset
The 7-step pipeline isn’t just organization. It’s a commitment to separating concerns: each step does one thing. The import step never runs regressions. The analysis step never renames variables. If you need to change how a variable is constructed, you change Step 3 and everything downstream updates automatically.
This is software engineering applied to research. And it works.