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giddy-mpungu/eastafrica_global_indicators_dashboard

Domaine:

socioeconomic

Type de record:

project
Créateur:
gid
Hôte:
Data-driven dashboard providing insights into key global indicators for East African countries. # Global Indicators Dashboard for East Africa ## Project Overview The **Global Indicators Dashboard for East Africa** is an interactive data visualization tool designed to provide actionable insights into key global indicators for East African countries. By integrating data from reliable sources and utilizing advanced analytics, the dashboard enables comparative analysis, trend exploration, and correlation studies across economic, social, environmental, and technological domains. ## Features - **Interactive Visualizations**: Provides dynamic charts, maps, and tables for exploring data by country, region, and demographic attributes. - **Comparative Analysis**: Allows users to compare performance across countries and regions, focusing on East African nations. - **Time-Series Exploration**: Tracks changes in indicators over time to identify trends and patterns. - **User-Friendly Interface**: Employs filters, slicers, and dynamic descriptions to make data accessible to non-technical audiences. - **Case Study**: Includes a dedicated analysis of Uganda and its East African neighbors for in-depth insights. ## Data Sources The dashboard uses data from the following reputable sources: - **Gapminder**: A rich repository of global development indicators. - **World Bank Open Data** - **United Nations Development Programme (UNDP)** - **United Nations Statistics Division (UNStats)** - **OECD Data Library** ## Technologies Used - **Languages**: R and Python for data analysis and preprocessing. - **Libraries**: Highcharter, Leaflet, ggplot2 for advanced visualizations. - **Frameworks**: Shiny for building the dashboard interface. - **Deployment**: Hosted on ShinyApps.io with GitHub integration for version control. ## Methodology 1. **Data Acquisition and Cleaning**: - Collected datasets from Gapminder and complementary sources. - Addressed missing values, outliers, and inconsistencies. 2. **Exploratory Data Analysis (EDA)**: - Conducted descriptive statistics and visualization …

Visit

github.com

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