Stata Data Cleaning
- 316 installs
- 590 repo stars
- Updated June 23, 2026
- meleantonio/awesome-econ-ai-stuff
stata-data-cleaning is a data agent skill that cleans survey and administrative economic datasets in Stata with reproducible transforms so developers and researchers can run estimation and visualization on consistent inp
About
stata-data-cleaning is a skill from meleantonio/awesome-econ-ai-stuff aimed at economists, data engineers, and quantitative developers who work in Stata on survey and administrative economic data. The skill guides reproducible cleaning steps—variable recodes, missing-value handling, merge validation, label consistency, and sample restrictions—before regression, panel estimation, or chart generation. Developers reach for it when raw extracts arrive with inconsistent codes, duplicate keys, or dirty categorical fields that would break `.do` scripts or publishable tables. It emphasizes repeatable transforms documented in Stata logs rather than one-off GUI edits. The skill suits agent-assisted econometrics repos where AI helps author或 review `.do` files, `egen` chains, and `merge` diagnostics while keeping pipelines auditable for research replication.
- Stata do-file oriented cleaning
- Variable harmonization patterns
- Missing-value and outlier handling
- Reproducible transform steps
- Econometrics-ready datasets
Stata Data Cleaning by the numbers
- 316 all-time installs (skills.sh)
- +12 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #575 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 316 |
|---|---|
| repo stars | ★ 590 |
| Last updated | June 23, 2026 |
| Repository | meleantonio/awesome-econ-ai-stuff ↗ |
How do you clean economic survey data reproducibly in Stata?
Clean survey and administrative economic datasets in Stata with reproducible transforms before estimation and visualization.
Who is it for?
Quantitative developers and economists who prepare Stata datasets for estimation and need reproducible cleaning workflows.
Skip if: Python pandas ETL pipelines, real-time streaming analytics, or teams with no Stata tooling in their stack.
When should I use this skill?
Raw survey or administrative economic data needs reproducible Stata cleaning before models, tables, or visualizations.
What you get
Cleaned `.dta` datasets, documented `.do` cleaning scripts, and merge or recode validation logs.
- cleaned .dta dataset
- documented .do cleaning script
Files
Stata Data Cleaning
Purpose
This skill helps economists clean, transform, and prepare datasets for analysis in Stata. It emphasizes reproducibility, proper documentation, and handling common data quality issues found in economic research.
When to Use
- Cleaning raw survey or administrative data
- Merging multiple data sources
- Handling missing values, duplicates, and outliers
- Creating analysis-ready panel datasets
- Documenting data transformations for replication
Instructions
Step 1: Understand the Data
Before generating code, ask the user: 1. What is the data source? (survey, administrative, API, etc.) 2. What is the unit of observation? 3. What are the key variables needed for analysis? 4. Are there known data quality issues to address?
Step 2: Generate Cleaning Pipeline
Create a Stata do-file that:
1. Has a clear header with project info and date 2. Sets up the environment (clear all, set memory, log) 3. Loads and inspects raw data 4. Documents each transformation with comments 5. Creates a codebook for the final dataset
Step 3: Follow Best Practices
- Use
assertstatements to verify data integrity - Create labeled variables with
label variable - Use value labels for categorical variables
- Generate a log file for reproducibility
- Save intermediate files when appropriate
Example Output
/*==============================================================================
Project: Economic Analysis Data Cleaning
Author: [Your Name]
Date: [Date]
Purpose: Clean raw survey data for regression analysis
Input: raw_survey_data.dta
Output: cleaned_analysis_data.dta
==============================================================================*/
* ============================================
* 1. SETUP
* ============================================
clear all
set more off
cap log close
log using "logs/data_cleaning_`c(current_date)'.log", replace
* Set working directory
cd "/path/to/project"
* Define globals for paths
global raw_data "data/raw"
global clean_data "data/clean"
global output "output"
* ============================================
* 2. LOAD AND INSPECT RAW DATA
* ============================================
use "${raw_data}/raw_survey_data.dta", clear
* Basic inspection
describe
summarize
codebook, compact
* Check for duplicates
duplicates report id_var
duplicates list id_var if _dup > 0
* ============================================
* 3. VARIABLE CLEANING
* ============================================
* --- Rename variables for clarity ---
rename q1 age
rename q2 income_reported
rename q3 education_level
* --- Clean numeric variables ---
* Replace missing value codes with .
mvdecode age income_reported, mv(-99 -88 -77)
* Cap outliers at 99th percentile
qui sum income_reported, detail
replace income_reported = r(p99) if income_reported > r(p99) & !mi(income_reported)
* --- Clean string variables ---
* Standardize state names
replace state = upper(trim(state))
replace state = "NEW YORK" if inlist(state, "NY", "N.Y.", "N Y")
* --- Create categorical variables ---
gen education_cat = .
replace education_cat = 1 if education_level < 12
replace education_cat = 2 if education_level == 12
replace education_cat = 3 if education_level > 12 & education_level <= 16
replace education_cat = 4 if education_level > 16 & !mi(education_level)
label define edu_lbl 1 "Less than HS" 2 "High School" 3 "College" 4 "Graduate"
label values education_cat edu_lbl
* ============================================
* 4. HANDLE MISSING DATA
* ============================================
* Create missing indicator variables
gen mi_income = mi(income_reported)
* Document missingness
tab mi_income
* Count complete cases
egen complete_case = rownonmiss(age income_reported education_cat)
tab complete_case
* ============================================
* 5. CREATE DERIVED VARIABLES
* ============================================
* Age groups
gen age_group = .
replace age_group = 1 if age >= 18 & age < 30
replace age_group = 2 if age >= 30 & age < 50
replace age_group = 3 if age >= 50 & age < 65
replace age_group = 4 if age >= 65 & !mi(age)
label define age_lbl 1 "18-29" 2 "30-49" 3 "50-64" 4 "65+"
label values age_group age_lbl
* Log income
gen log_income = ln(income_reported + 1)
* ============================================
* 6. DATA VALIDATION
* ============================================
* Assert expected ranges
assert age >= 18 & age <= 120 if !mi(age)
assert income_reported >= 0 if !mi(income_reported)
* Check variable types
assert !mi(id_var)
isid id_var // Verify unique identifier
* ============================================
* 7. LABEL VARIABLES
* ============================================
label variable age "Age in years"
label variable income_reported "Annual income (USD)"
label variable education_cat "Education category"
label variable log_income "Log of annual income"
label variable mi_income "Missing income indicator"
* ============================================
* 8. FINAL CHECKS AND SAVE
* ============================================
* Keep relevant variables
keep id_var age age_group income_reported log_income ///
education_cat mi_income state year
* Order variables logically
order id_var year state age age_group income_reported ///
log_income education_cat mi_income
* Compress to minimize file size
compress
* Save cleaned data
save "${clean_data}/cleaned_analysis_data.dta", replace
* Create codebook
codebook, compact
* Close log
log close
* ============================================
* END OF FILE
* ============================================Requirements
Software
- Stata 15+ (some commands require newer versions)
Recommended User-Written Commands
ssc install unique // For unique value checking
ssc install mdesc // For missing data patterns
ssc install labutil // For label manipulationBest Practices
1. Always start with `clear all` to ensure clean environment 2. Use log files to document all transformations 3. Comment extensively - explain WHY, not just WHAT 4. Use `assert` statements to catch data errors early 5. Create a data dictionary alongside your cleaned data 6. Version your do-files and datasets
Common Pitfalls
- ❌ Not checking for duplicates before merging
- ❌ Forgetting to handle missing value codes (-99, -88, etc.)
- ❌ Not labeling variables and values
- ❌ Overwriting raw data files
- ❌ Not documenting data transformations
References
- Stata Data Management Manual
- Gentzkow & Shapiro (2014) Code and Data for the Social Sciences
- DIME Analytics Data Handbook
Changelog
v1.0.0
- Initial release with comprehensive cleaning template
Stata Data Cleaning
Purpose
This skill helps economists clean, transform, and prepare datasets for analysis in Stata. It emphasizes reproducibility, proper documentation, and handling common data quality issues found in economic research.
When to Use
- Cleaning raw survey or administrative data
- Merging multiple data sources
- Handling missing values, duplicates, and outliers
- Creating analysis-ready panel datasets
- Documenting data transformations for replication
Instructions
Step 1: Understand the Data
Before generating code, ask the user: 1. What is the data source? (survey, administrative, API, etc.) 2. What is the unit of observation? 3. What are the key variables needed for analysis? 4. Are there known data quality issues to address?
Step 2: Generate Cleaning Pipeline
Create a Stata do-file that:
1. Has a clear header with project info and date 2. Sets up the environment (clear all, set memory, log) 3. Loads and inspects raw data 4. Documents each transformation with comments 5. Creates a codebook for the final dataset
Step 3: Follow Best Practices
- Use
assertstatements to verify data integrity - Create labeled variables with
label variable - Use value labels for categorical variables
- Generate a log file for reproducibility
- Save intermediate files when appropriate
Example Output
/*==============================================================================
Project: Economic Analysis Data Cleaning
Author: [Your Name]
Date: [Date]
Purpose: Clean raw survey data for regression analysis
Input: raw_survey_data.dta
Output: cleaned_analysis_data.dta
==============================================================================*/
* ============================================
* 1. SETUP
* ============================================
clear all
set more off
cap log close
log using "logs/data_cleaning_`c(current_date)'.log", replace
* Set working directory
cd "/path/to/project"
* Define globals for paths
global raw_data "data/raw"
global clean_data "data/clean"
global output "output"
* ============================================
* 2. LOAD AND INSPECT RAW DATA
* ============================================
use "${raw_data}/raw_survey_data.dta", clear
* Basic inspection
describe
summarize
codebook, compact
* Check for duplicates
duplicates report id_var
duplicates list id_var if _dup > 0
* ============================================
* 3. VARIABLE CLEANING
* ============================================
* --- Rename variables for clarity ---
rename q1 age
rename q2 income_reported
rename q3 education_level
* --- Clean numeric variables ---
* Replace missing value codes with .
mvdecode age income_reported, mv(-99 -88 -77)
* Cap outliers at 99th percentile
qui sum income_reported, detail
replace income_reported = r(p99) if income_reported > r(p99) & !mi(income_reported)
* --- Clean string variables ---
* Standardize state names
replace state = upper(trim(state))
replace state = "NEW YORK" if inlist(state, "NY", "N.Y.", "N Y")
* --- Create categorical variables ---
gen education_cat = .
replace education_cat = 1 if education_level < 12
replace education_cat = 2 if education_level == 12
replace education_cat = 3 if education_level > 12 & education_level <= 16
replace education_cat = 4 if education_level > 16 & !mi(education_level)
label define edu_lbl 1 "Less than HS" 2 "High School" 3 "College" 4 "Graduate"
label values education_cat edu_lbl
* ============================================
* 4. HANDLE MISSING DATA
* ============================================
* Create missing indicator variables
gen mi_income = mi(income_reported)
* Document missingness
tab mi_income
* Count complete cases
egen complete_case = rownonmiss(age income_reported education_cat)
tab complete_case
* ============================================
* 5. CREATE DERIVED VARIABLES
* ============================================
* Age groups
gen age_group = .
replace age_group = 1 if age >= 18 & age < 30
replace age_group = 2 if age >= 30 & age < 50
replace age_group = 3 if age >= 50 & age < 65
replace age_group = 4 if age >= 65 & !mi(age)
label define age_lbl 1 "18-29" 2 "30-49" 3 "50-64" 4 "65+"
label values age_group age_lbl
* Log income
gen log_income = ln(income_reported + 1)
* ============================================
* 6. DATA VALIDATION
* ============================================
* Assert expected ranges
assert age >= 18 & age <= 120 if !mi(age)
assert income_reported >= 0 if !mi(income_reported)
* Check variable types
assert !mi(id_var)
isid id_var // Verify unique identifier
* ============================================
* 7. LABEL VARIABLES
* ============================================
label variable age "Age in years"
label variable income_reported "Annual income (USD)"
label variable education_cat "Education category"
label variable log_income "Log of annual income"
label variable mi_income "Missing income indicator"
* ============================================
* 8. FINAL CHECKS AND SAVE
* ============================================
* Keep relevant variables
keep id_var age age_group income_reported log_income ///
education_cat mi_income state year
* Order variables logically
order id_var year state age age_group income_reported ///
log_income education_cat mi_income
* Compress to minimize file size
compress
* Save cleaned data
save "${clean_data}/cleaned_analysis_data.dta", replace
* Create codebook
codebook, compact
* Close log
log close
* ============================================
* END OF FILE
* ============================================Requirements
Software
- Stata 15+ (some commands require newer versions)
Recommended User-Written Commands
ssc install unique // For unique value checking
ssc install mdesc // For missing data patterns
ssc install labutil // For label manipulationBest Practices
1. Always start with `clear all` to ensure clean environment 2. Use log files to document all transformations 3. Comment extensively - explain WHY, not just WHAT 4. Use `assert` statements to catch data errors early 5. Create a data dictionary alongside your cleaned data 6. Version your do-files and datasets
Common Pitfalls
- ❌ Not checking for duplicates before merging
- ❌ Forgetting to handle missing value codes (-99, -88, etc.)
- ❌ Not labeling variables and values
- ❌ Overwriting raw data files
- ❌ Not documenting data transformations
References
- Stata Data Management Manual
- Gentzkow & Shapiro (2014) Code and Data for the Social Sciences
- DIME Analytics Data Handbook
Changelog
v1.0.0
- Initial release with comprehensive cleaning template
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What data does stata-data-cleaning handle?
stata-data-cleaning targets survey and administrative economic datasets in Stata. The skill focuses on reproducible transforms—recodes, merges, missing values, and labels—before estimation and visualization steps.
Does stata-data-cleaning run outside Stata?
stata-data-cleaning is Stata-centric and expects `.do` workflows and `.dta` datasets. It is not a pandas or SQL replacement; teams should use it when Stata is the analysis environment.