Wrangling

What is data wrangling?

Data wrangling, also known as data cleaning or data munging, is the process of transforming and mapping raw data into a usable format for analysis. This includes cleaning, structuring, and enriching data to ensure its quality and suitability for downstream tasks. Data wrangling is a crucial step in the data lifecycle, bridging the gap between data collection and analysis.

Why is data wrangling important?

Data wrangling is essential because real-world data is often messy, incomplete, or inconsistent. Effective wrangling:

• Improves data quality and reliability
• Reduces errors and biases in analysis
• Ensures data is in a standardized, interoperable format
• Saves time and resources in later stages of the data lifecycle
• Supports compliance with FAIR principles by making data more findable, accessible, interoperable, and reusable

What should be considered for data wrangling?

To ensure best practices and adherence to FAIR principles during data wrangling, consider the following:

• Data Quality: Identify and address missing values, duplicates, outliers, and inconsistencies.
• Documentation: Record all transformations, cleaning steps, and decisions for transparency and reproducibility.
• Standardization: Use common formats, units, and controlled vocabularies to enhance interoperability.
• Automation: Where possible, automate wrangling processes to reduce manual errors and improve efficiency.
• Ethics and Privacy: Remove or anonymize sensitive information as required by regulations and ethical standards.
• Version Control: Track changes to data and scripts to enable rollback and collaboration.
• FAIR Principles: Ensure that cleaned and processed data, as well as wrangling scripts, are well-documented and shared in accessible repositories.
• Validation: Regularly validate data after wrangling to ensure accuracy and completeness.

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