Project: Template Direct Air Capture Data Analytics Platform
Objective: To create an R Shiny app that provides a comprehensive view of various Direct Air Capture projects around the U.S.
App will include features to visualize real-time and historical data, predictive analytics, and data pre-processing capabilities - hoping to create a skeleton and return to this with larger datasets
Key Features:
-
Interactive Dashboard
-
A dynamic dashboard displaying key performance indicators (KPIs) such as total CO2 captured, energy efficiency, cost-effectiveness, and geographical coverage. Data Preprocessing Module
-
Tools for cleaning, normalizing, and integrating DAC data from multiple sources. Geospatial Analysis
-
Interactive maps showing the locations of various DAC installations and their performance metrics. Time Series Analysis
-
Graphs showing the performance of DAC projects over time, with the ability to filter by various parameters. Predictive Modeling
-
Implement machine learning models to forecast the efficiency, costs, and potential impact of different DAC technologies. Collaboration Tool
-
A feature to share insights or directly export graphs and data tables, fostering collaboration between carbon science, data science, and engineering teams. User Input and Customization
-
Allow users to input their data for quick analytics and visualization.
Data Requirements:
- Geospatial data for DAC installations.
- Time-series data for CO2 captured, energy used, and other relevant metrics.
- Additional environmental parameters (e.g., air temperature, humidity).
Technology Stack:
- R Shiny for the main application.
- R for data preprocessing and analytics.
- leaflet for map visualizations.
- ggplot2 and plotly for other visualizations.