R Statistical Analysis Notebooks

What is R Statistical Analysis Notebooks?

Local data science for soil, water, biodiversity, and economy. This guide covers when to use it, the best first action, best practices, common mistakes, and concrete next steps.

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.