Stata

Stata Data Cleaning and Analysis Skill

Contents

• Core Principles
• Project Configuration
• Coding Standards Quick Reference
• Data Cleaning Workflow
• Missing Values
• Common Operations
• Quality Checks
• Troubleshooting
• References

Core Principles

Four-Stage Data Flow

1. Import - Combine data into Stata format, apply corrections, remove duplicates
2. Deidentify - Remove PII as early as possible
3. Clean - Standardize formats, verify consistency
4. Construct - Build analysis variables through merging/appending

Project Configuration

Do-File Header

* ==============================================================================
* Project: [Project Name]
* Purpose: [Brief description]
* Author: [Name]
* Created: [Date]
* ==============================================================================

clear all
set more off
version 17.0
set maxvar 5000  // Increase only if genuinely needed

Path Setup

* Define paths in master do-file (use forward slashes)
global data   "$root/data"
global output "$root/output"

* Usage - always use globals, never cd
use "$data/raw/survey.dta", clear
save "$data/clean/survey_clean.dta", replace

Coding Standards Quick Reference

Variable Naming

Command Abbreviations

Conditionals

* Good - explicit and clear
replace status = 1 if (employed == 1) & !missing(income)
drop if missing(respondent_id)

* Bad - implicit or unclear
replace status = 1 if employed & income
drop if respondent_id >= .

Line Breaking

regress income ///
    age i.education i.region ///
    if (sample == 1), ///
    vce(cluster village_id)

Data Cleaning Workflow

1. Import and Inspect

import delimited "$data/raw/survey.csv", clear varnames(1)
describe
codebook, compact

2. Verify Identifiers

duplicates report respondent_id
duplicates tag respondent_id, gen(dup_flag)
* Investigate and resolve duplicates
isid respondent_id  // Assert uniqueness

3. Clean Variables

* Rename to convention
rename (q1 q2 q3) (resp_age resp_gender resp_education)

* Validate ranges
assert inrange(age, 0, 120) if !missing(age)

* Clean strings
replace name = strtrim(strproper(name))

4. Document

label var resp_age "Respondent age in years"
label define gender_lbl 1 "Male" 2 "Female"
label values resp_gender gender_lbl
notes _dta: "Cleaned on `c(current_date)'"

5. Save and Verify

compress
save "$data/clean/survey_clean.dta", replace

Missing Values

IPA Extended Missing Conventions

Recoding

* Using mvdecode (efficient)
mvdecode _all, mv(-99=.d \ -98=.r \ -97=.n \ -96=.s)

* Check missing patterns
misstable summarize

Common Operations

Merging

use "$data/clean/household.dta", clear
count
local pre_merge = r(N)

merge 1:1 hhid using "$data/admin/treatment.dta"
tab _merge
assert _merge != 2  // No unmatched using expected
keep if _merge == 3
drop _merge

Appending

use "$data/clean/baseline.dta", clear
gen wave = 1
append using "$data/clean/endline.dta"
replace wave = 2 if missing(wave)

Reshaping

* Wide to long
reshape long income_, i(hhid) j(year)
rename income_ income

* Long to wide
reshape wide income, i(hhid) j(year)

Quality Checks

* Summary statistics
summarize, detail
tabstat income expenditure, stats(n mean sd min max)

* Outlier detection
egen income_std = std(income)
list hhid income if abs(income_std) > 3

* Cross-tabulation consistency
tab gender pregnant, missing
assert pregnant == . | pregnant == 0 if gender == 1

Troubleshooting

Assert Failures

1. Examine failing observations: list if !(condition)
2. Check for unexpected missing values
3. Verify data source and transformations
4. Document exceptions if valid

Merge Issues

1. Check _merge distribution with tab _merge
2. Investigate unmatched: list if _merge == 1 or _merge == 2
3. Verify key variable types match (string vs numeric)
4. Check for leading/trailing spaces in string keys

Performance

1. Load only needed variables: use var1 var2 using "data.dta"
2. Reshape to long format before loops
3. Use quietly to suppress output in loops
4. Increase maxvar only when necessary

References

Project References

• Coding Standards - Complete DIME Analytics Stata standards
• Data Cleaning Checklist - Step-by-step checklist
• Missing Values Guide - IPA missing value conventions

External Resources

• IPA Data Cleaning Guide
• DIME Analytics Handbook
• Stata Linter

Linting

just lint-stata                      # Lint all do-files
just lint-stata-file scripts/01.do   # Lint specific file

Common Packages

ssc install ietoolkit    // DIME tools
ssc install estout       // Tables
ssc install fre          // Frequencies

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