knjiiru/Infant_Mortality_Kenya

A learning project exploring infant mortality trends in Kenya (1993–2014) using R. Includes data cleaning, visualization with ggplot2, and summary statistics to highlight public health improvements and disparities across wealth groups. # 📉 Infant Mortality Trends in Kenya (1993–2014) This project analyzes trends in **infant mortality rates** in Kenya between 1993 and 2014, using data filtered from a national health indicators dataset. It uses **R** for data cleaning, visualization, and statistical interpretation. --- ## 🎯 Objective To visualize and interpret how infant mortality has changed over time in Kenya and explore possible disparities across socioeconomic groups (e.g. wealth quintiles), using open health data. --- ## 📁 Dataset - **Source**: The Humanitarian Data Exchange (data.humdata.org) - **Indicator**: Infant mortality rate (deaths per 1,000 live births) - **Years Covered**: 1993, 1998, 2003, 2009, 2014 - **Additional Info**: Data includes breakdowns by wealth quintile (Q1–Q5) --- ## 🛠 Tools Used - `R` - `tidyverse` (for data wrangling and visualization) - `ggplot2` (for line plots and smooth trends) --- ## 📊 Steps in the Analysis 1. **Read and preview data** from a `.csv` file 2. **Clean and convert** data types (e.g. ensure `Year` is numeric) 3. **Filter and select** relevant columns 4. **Visualize** infant mortality rate over time using line and smooth plots 5. **Generate summary statistics** (min, max, average rates, and their years) 6. **Interpret** trends and disparities --- ## 🧠 Key Findings - Infant mortality in Kenya decreased from **96 to 38 deaths per 1,000 live births** between 1993 and 2014. - The **highest mortality** was observed in 1998 and 2003, both at 96. - The **lowest rate** was in 2014 for the wealthiest quintile. - The overall **average rate** during this period was 60.3. --- ## 🚀 How to Reproduce 1. Clone the repository 2. Open the `infant_mortality_analysis.R` script 3. Ensure the data file (`kenya_infant_mortality_trends.csv`) is in the same folder 4. Run the script in R or RStudio --- ## ✅ Future Work - Analyze trends by wealth quintile (Q1–Q5) more explicitly - Add comparison with maternal mortality or immunization coverage --- …