Data handling refers to the process of collecting, organizing, transforming, and managing data so that it can be analyzed effectively. In the context of data analysis, data handling is crucial because raw data is rarely ready for immediate analysis. You often need to import data from various sources, clean and transform it, and then export results or share findings.

Typical data sources include:

• Spreadsheets;
• Databases;
• Text files;
• Web data;
• Data generated by experiments or surveys.

Being able to efficiently handle data is essential for accurate analysis, reproducible results, and meaningful insights.

R is a powerful tool for data handling, making it a popular choice among data analysts and researchers. With R, you can easily import data from different formats, manipulate and clean datasets, and export results in various forms. R's strengths in data handling include its ability to work with large datasets, its extensive suite of built-in functions for data manipulation, and its capability to connect to many data sources. Common use cases include:

• Reading data from CSV or Excel files;
• Cleaning and transforming messy datasets;
• Summarizing data;
• Preparing data for statistical analysis or visualization.

By mastering data handling in R, you set a strong foundation for all subsequent data analysis tasks.