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 all 54 rows and 10 columns — 5 character (country_name, iso_alpha3, capital, region, landlocked) and 5 numeric (id, latitude, longitude, area_km2, population). In Python, both files were imported via pandas.read_csv() and pandas.read_excel(), yielding identical DataFrames. The dataset is ready for immediate use in standard data science workflows across Windows, macOS, and Linux.
5. Geospatial Validation (R and Python): The latitude and longitude variables were validated beyond simple import testing. In R, interactive maps were successfully generated using the leaflet package, correctly plotting all 54 African countries. In Python, equivalent maps were produced using the folium library, yielding identical geospatial results. Both libraries confirmed the accuracy and integrity of the coordinate data for mapping workflows.

Data Structure

The dataset is provided in .csv and .xlsx formats, containing the following variables:

Variable	Type	Description
id	Integer	Unique numeric identifier for each country (1 to 54).
country_name	String	Full official name of the African country.
iso_alpha3	String	ISO 3166-1 alpha-3 three-letter country code (e.g., DZA, AGO, KEN).
capital	String	Name of the country's capital city.
region	String	African sub-region classification (e.g., Northern Africa, Western Africa).
latitude	Float	Geographic latitude coordinate of the country's centroid (decimal degrees).
longitude	Float	Geographic longitude coordinate of the country's centroid (decimal degrees).
landlocked	Boolean	Indicates whether the country is landlocked (TRUE) or has coastal access (FALSE).
area_km2	Integer	Total land area of the country in square kilometers.
population	Integer	Estimated total population of the country.

Data Curation

1. Data Source: Information validated against official records from the following sources:

   • World Bank Group – Surface area (sq. km)
   • World Bank Group – Population, total
   • World Atlas – Landlocked Countries in Africa
   • Google Developers – Latitude and Longitude
   • Countries of the World – List of African Capitals
   • Countries of the World – List of Countries in Africa
   • World Atlas – Regions of Africa
   • ISO Online Browsing Platform (OBP) – Country Codes (iso_alpha3)

2. Normalization: Column names use snake_case for easy calling in Python, R, and SQL functions.

3. Interoperability: Both .csv and .xlsx formats were tested in R and Python without errors. See Language and Encoding, item 4 for full details.

4. Purpose: This dataset was created to provide a valuable digital asset for end users of R, Python, and SQL, offering a clean and ready-to-use resource that can be immediately integrated into data analysis, visualization, and educational workflows.

5. Data Currency: The variables area_km2 and population reflect the most recent data available from the World Bank Group, with area_km2 updated to 2023 and population updated to 2024, ensuring the dataset represents current figures for all 54 African countries.

License

This dataset is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to share and adapt the material as long as appropriate credit is given.

Author

Renzo Caceres Rossi

• ORCID: 0009-0005-0744-854X
• GitHub: lightbluetitan