us-energy-analysis-R

📊 Project Overview This project provides a data-driven analysis of the United States' shift toward renewable energy. By leveraging historical net generation data, I developed a cleaning pipeline and a predictive model to visualize the velocity of the energy transition and forecast future capacity.

🚀 Key Features

• End-to-End Data Pipeline: A robust R-based workflow that cleans and transforms raw energy sector data into a tidy format for analysis.
• Growth Visualization: Created high-fidelity exponential growth plots comparing renewable sources against traditional sectors.
• Predictive Modeling: Developed a Linear Regression model to forecast energy trends.
• High Model Accuracy: Achieved an R^2 of 0.93, indicating that the model explains 93% of the variance in the renewable energy growth data.

🛠 Tech Stack

• Language: R
• Libraries: tidyverse (data manipulation), ggplot2 (visualization), stats (linear modeling).
• Data Source: US Energy Information Administration (EIA)/Net Generation Sectors.

📈 Key Insights

• Renewable energy adoption is currently following an exponential curve rather than a linear one.
• The high R^2 value suggests that historical trends are highly reliable predictors for near-term transition milestones.