lightbluetitan/african_countries_indicators

African Countries: A Curated Dataset on Africa Indicators for Education and Data Science # African Countries: A Curated Dataset on Africa Indicators for Education and Data Science ## Description This **curated dataset** provides a comprehensive geographic, demographic, and socioeconomic overview of all 54 sovereign nations on the African continent. Unlike raw data exports, this resource is the result of a meticulous process of **research and data selection**. It captures key indicators for every African country, including geographic identifiers, regional classification, spatial coordinates, land area, and population figures, making it suitable for educational analysis, data visualization, and applied data science projects. The purpose of this resource is to serve as a **foundational reference table** for educators, students, and researchers, providing a clean, verified, and technically robust starting point for any project involving African countries and their indicators. ## Language and Encoding To ensure maximum technical compatibility and international reach, this dataset follows these standards: 1. **Metadata in English:** All variable names (column headers) are in English (e.g., `country_name`, `region`, `population`), facilitating seamless integration with global data science libraries in R and Python. 2. **Data Integrity:** The records maintain their geographic and demographic accuracy, using international nomenclature for countries, regions, and ISO 3166-1 alpha-3 codes. 3. **Encoding:** The files were validated using `readr::guess_encoding()` in R, which detected **pure ASCII encoding (confidence = 1.0)**. Since ASCII is a strict subset of UTF-8, the dataset is fully compatible with UTF-8 environments across Windows, macOS, and Linux without risk of character corruption. 4. **Cross-Platform Compatibility (R and Python):** Both formats were successfully imported in R and Python without errors or warnings. In **R**, the `.xlsx` and `.csv` files were loaded using `readxl::read_excel()` and `readr::read_csv()` respectively, correctly parsing …