gkagyen/R-Conference-2025

A presentation I gave at the Ghana R conference 2025 on the application of Machine Learning in Environmental monitoring using the R programming language # Ghana R Conference 2025 ## Machine Learning in Environmental Monitoring: An R Perspective This repository contains the materials and source code for my presentation at the **Ghana R User Community Conference**, under the theme: > *“Harnessing R for Sustainable Development: Innovations, Collaborations, and Health Impacts”* ## 📌 Overview This presentation introduces how **Machine Learning** can be used in **Environmental Monitoring** using R. The focus is on practical applications such as: - Forecasting rainfall using historical weather patterns (temperature and humidity) - Using `tidymodels` for reproducible ML workflows - Leveraging open-source R tools for data science and sustainability ## 📁 Repository Structure | Folder/File | Description | |---------------------------|---------------------------------------------| | `scripts/` | Contains all R scripts used in the live demo and data prep | | `data/` | Example environmental dataset (synthetic) | | `Conference Presentation.pdf` | Presentation slides (PowerPoint) | | `R Conference schedule.pdf` | Conference program schedule | | `README.md` | Project documentation (this file) | ## 🚀 How to Use ### Prerequisites Make sure you have R (≥ 4.1.0) and the following packages installed: \`\`\`r install.packages(c("tidymodels", "ggplot2", "lubridate", "dplyr", "readr")) ### Running the Live Demo Code 1. Open the project folder in RStudio. 2. Navigate to the script in `R/predict_rainfall_demo.R` (or equivalent). 3. Run the script to: - Load simulated weather data (monthly temperature, humidity, and rainfall) - Train a random forest model using `tidymodels` - Predict rainfall - Visualize predictions ## 💡 Live Demo Highlight The demo illustrates how **machine learning** (Random Forest) can be applied to **forecast future rainfall** based on previous months' temperature and humidity. This type of analysis is useful for: - Climate adaptation - Agricultural planning - Water …