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LeafContourEFD Analysis

This repository contains analysis code and reproducibility materials for the LeafContourEFD manuscript. The software itself is available at: leaf-contour-efd

Quickstart

Follow these steps to confirm the environment and run a first example notebook.

1. Get the data (see (data?) section below).

2. Set PROJECT_DATA_DIR in .Renviron (project root):

.Renviron

PROJECT_DATA_DIR=/path/to/your/data

3. Restore the R package environment:

renv::restore()

4. Execute analysis notebooks in notebooks/. (.qmd files; Quarto is required)

Requirements

To reproduce the analysis, you need:

• R (recommended version: 4.3.1 or higher)
• Quarto
• the R packages required by this project

If you install renv, you can install the required R packages by executing renv::restore() (see below). All analysis code is written in R version 4.3.1 (R 2026).

Optional tools

• Air: R code formatter.
• Positron: IDE used for all analyses in this project, but not required to run the code.

R environment setup

In this repository, I use renv to manage R package dependencies. Please execute command below after installing renv.

# install.packages("renv") # if not installed
renv::restore()

Data and outputs

Data

Raw data are available on Dryad (DOI: in preparation).

Tracked-vs-external data policy is documented in data/README.md.

This project may require setting a local data directory via environment variable. Create a .Renviron file in the project root and add:

.Renviron

PROJECT_DATA_DIR=/path/to/your/data

For example:

• macOS: /Users/yourname/Dropbox/project_data
• Windows: C:/Users/yourname/Dropbox/project_data

Output directories

Generated artifacts should be written to deterministic, repository-relative paths:

• outputs/figures/: rendered figures/images (e.g., SVGs from notebooks)
• outputs/tables/: derived tabular outputs
• outputs/cache/: temporary caches/intermediate artifacts

Notebooks in notebooks/ follow these conventions and avoid user-specific absolute output paths.

Repository layout

Path	Purpose
notebooks/	Quarto notebooks used for analysis, figures, and report outputs.
scripts/ or R/	Reusable R scripts/functions shared across notebooks and workflows.
renv/	Project-local R environment metadata and lockfile support.
PROJECT_DATA_DIR location	External data directory expected by analysis code (set via .Renviron).

Common pitfalls

• PROJECT_DATA_DIR is not set or points to the wrong location
  Symptoms: file-not-found errors when running notebooks or scripts.
  Fix: confirm .Renviron exists in project root and contains the correct absolute path.

• renv::restore() fails
  Symptoms: package installation errors, version conflicts, or missing system libraries.
  Fix: update R to a compatible version (4.3.1+ recommended), then retry renv::restore() and install missing system dependencies if prompted.

• Quarto is missing or not on PATH
  Symptoms: quarto: command not found or render failures.
  Fix: install Quarto from https://quarto.org/ and verify with quarto --version.

About README

This README.md is generated from README.qmd using Quarto by using the command:

quarto render README.qmd

Use of AI and LLMs

ChatGPT was used to improve the documentation and code. Codex was used to refactor the code in this repository. GitHub Copilot was used for coding assistance.

References

R: A Language and Environment for Statistical Computing. 2026. Vienna, Austria. https://www.R-project.org/.