From messy strings to analysis-ready data: Practical data cleaning with Stata string functions and regular expressions

Data cleaning is often the most time-consuming stage of empirical analysis, particularly when raw data contain inconsistencies in formatting, encoding, and structure. While Stata provides a range of built-in commands (for example, destring, split, date) for basic transformations, these tools are frequently insufficient for handling irregular or unstructured string variables encountered in practice. This presentation demonstrates how Stata’s string functions and regular expression capabilities can be used to efficiently transform messy, real-world datasets into analysis-ready formats. Drawing on examples from research subject and teaching experience, I illustrate common data issues, including nonnumeric values stored as strings, concatenated characters, and inconsistent delimiters. The session introduces a set of practical workflows that combine standard string commands with regex-based solutions to identify, parse, and restructure problematic variables. Emphasis is placed on reproducibility, efficiency as well as efficacy, and minimizing manual intervention. Attendees will gain hands-on strategies for diagnosing data irregularities, applying flexible string manipulation techniques, and integrating these methods into their empirical workflow. The presentation is intended for applied researchers, instructors, and students seeking to improve data preparation and conversion in Stata.