EmmanuelKinyanjui525/conflict-analysis-kenya.R

Exploratory data analysis, visualization, and statistical modeling of conflict events in Kenya using R (tidyverse, ggplot2, dplyr, time series, and regression analysis). Includes mapping of conflict zones, fatality analysis, and forecasting future trends. # Kenya Conflict Data Analysis This repository contains an in-depth **exploratory data analysis, visualization, and statistical modeling of conflict events in Kenya** using R. The project demonstrates data cleaning, analysis, mapping, and forecasting of conflict trends over time. ## Tools and Packages Used - **R** programming language - **tidyverse** (dplyr, ggplot2, etc.) for data manipulation and visualization - **dplyr** for data wrangling - **ggplot2** for plotting - **GGally** for correlation analysis - **tseries**, **forecast**, **astsa** for time series analysis - **caret**, **e1071** for prediction and machine learning ## Dataset The dataset contains records of conflict events in Kenya, including: - Event date, type, and sub-type - Actors involved - Location (county, sub-county, latitude, longitude) - Civilian targeting - Number of fatalities > **Note:** Make sure the CSV file `kenya_conflict_data.csv` is in the same folder as the R script to run the code. ## Features / Analyses - Data cleaning and formatting - Frequency tables and bar charts for event types, sub-event types, and actors - Mapping conflict zones in Kenya by type of event and disorder - Statistical analysis of fatalities (per year, per event type, per disorder type) - Linear regression modeling to understand factors affecting fatalities - Time series analysis and forecasting of conflict fatalities - Correlation analysis between variables ## How to Run 1. Clone or download this repository. 2. Ensure the dataset CSV file is in the same directory. 3. Open `kenya-conflict-analysis.R` in R or RStudio. 4. Run the script line by line, or source the file to execute the full analysis. ```r # Example: source("kenya-conflict-analysis.R")