Stata Data Cleaning
- 1 installs
- 3.2k repo stars
- Updated August 4, 2026
- brycewang-stanford/awesome-agent-skills-for-empirical-research
stata-data-cleaning is a Claude skill that generates reproducible Stata do-files to clean, transform, and validate messy economic datasets for analysis.
About
A skill that helps economists clean, transform, and prepare datasets for analysis in Stata with reproducible workflows. Developers use it when cleaning raw survey or administrative data, merging multiple sources, handling missing values or duplicates, and building analysis-ready panel datasets. It generates documented do-files with logging, assert checks, and codebook output for replication.
- Generates reproducible Stata cleaning pipelines with logging and validation
- Handles missing values, duplicates, outliers, and panel dataset construction
- Emphasizes assert-based data-integrity checks and documented transformations
Stata Data Cleaning by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
stata-data-cleaning capabilities & compatibility
- Capabilities
- data cleaning · data validation
- Use cases
- data analysis
What stata-data-cleaning says it does
Clean and transform messy data in Stata with reproducible workflows
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
Creating analysis-ready panel datasets
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| Installs | 1 |
|---|---|
| repo stars | ★ 3.2k |
| Last updated | August 4, 2026 |
| Repository | brycewang-stanford/awesome-agent-skills-for-empirical-research ↗ |
What it does
Generate reproducible Stata do-files that clean, merge, and validate messy economic datasets into analysis-ready form.
Who is it for?
Building reproducible, documented Stata data-cleaning pipelines for economic research.
Skip if: Non-Stata data cleaning or downstream econometric estimation.
When should I use this skill?
You need to clean, merge, or validate messy data in Stata for analysis.
What you get
A documented, logged Stata do-file that outputs an analysis-ready dataset with a codebook.
- Reproducible Stata cleaning do-file
- cleaned analysis-ready dataset
- codebook
By the numbers
- 8-section cleaning pipeline template
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 does the cleaning pipeline include?
A clear header, environment setup with logging, raw-data inspection, documented transformations, assert-based validation, variable labeling, and a codebook for the final dataset.
Who is it for?
Economists cleaning raw survey or administrative data, merging sources, and preparing analysis-ready panel datasets.