R Statistical Analysis Notebooks

Purpose

R notebooks process field data — soil carbon tracking, drone coverage analysis, water flow modeling, mycorrhizal mapping, and community economy analysis — all running locally.

Content

Purpose

R notebooks process field data — soil carbon tracking, drone coverage analysis, water flow modeling, mycorrhizal mapping, and community economy analysis — all running locally.

When to Use

• After collecting soil cores or water samples
• After drone NDVI flights
• Before and after major interventions for comparison
• When generating reports for peer nodes or allied organizations

Best First Action

Open soil_carbon_tracker notebook, import your core sample CSV, run the SOM (soil organic matter) regression to establish baseline before any amendment.

Best Practices

• Version control all notebooks with Git.
• Keep raw data separate from processed data.
• Generate before/after comparison plots for every intervention.
• Share notebook outputs as PDF bundles with CRK exports.
• Use local R installation — no cloud dependency.

Common Mistakes

• Running analysis without establishing baseline measurements first.
• Treating statistical significance as ecological significance — always ground-truth.
• Not sharing notebook source code with the community — reproducibility requires shared methods.

Next Steps

• Open Tasks for data collection missions.
• Open History to attach analysis outputs.
• Ask Bonsai for statistical methodology guidance.