Jimmymugendi/eac-mortality-analysis

Geospatial and statistical analysis of under-five and neonatal mortality rates in Eastern African Community(EAC) using R # EAC Mortality Analysis This project presents a geospatial and temporal analysis of under-five and neonatal mortality rates across countries in the East African Community (EAC). The analysis leverages data visualization and mapping techniques in **R** and abit of **Python** to highlight regional disparities and trends over time. ## 📊 Project Objectives - Visualize and compare **Under-Five Mortality Rate** and **Neonatal Mortality Rate** across EAC countries. - Create **choropleth maps** using individual shapefiles to show the most recent data. - Analyze **trends over time** for each mortality indicator. - Identify countries with the **highest mortality rates** in the latest year. ## 🌍 Countries Included - Burundi - Democratic Republic of the Congo - Kenya - Rwanda - Somalia - South Sudan - Uganda - United Republic of Tanzania ## 🛠️ Tools Used - `R`, `ggplot2`, `sf`, `dplyr`, `viridis` - GADM country shapefiles (Level 0 boundaries) - Custom R functions for plotting and summarization ## 🔍 Insights - Temporal plots reveal a general **decline in mortality** across most EAC countries. - Choropleths highlight **geographic disparities**, with some countries persistently exhibiting higher rates. - Identified the countries with the **highest current mortality burdens**, informing where targeted health interventions may be needed most. ## 🚀 How to Run 1. Clone the repo: ```bash git clone github.com ``` 2. Open `jimmy_Mugendi.RMD` in RStudio. 3. Ensure all dependencies are installed: ```r install.packages(c("sf", "tidyverse", "ggplot2", "viridis")) ``` 4. Run the script to generate maps and plots. ## 🧠 Author **Jimmy Mugendi** _Data Scientist analysing about health analytics and spatial modeling._ --- ## 📄 License MIT License