TEMPLATE: Cleanup of NEW HerbVar Dataset

This is the structure of the template. After setting it up, replace the text in the README with informartion specific to your dfata cleanup project. A sample README can be found at https://github.com/BrunaLab/HeliconiaSurveys

new_dataset_template

|- code/                  # Folder for R code   
|    +- data_cleaner.R    # R script for loading, cleaning, and organizing dataset 
|   
|- data/                  #   
|    +- data_clean/       # folder for the clean data files in open formats (.txt, .csv).
|    +- data_raw/         # folder for raw data not changed once created    
|        +- my_data.csv   # data files in open formats (.txt, .csv). Do not modify these directly.   
|   
|- docs/                  # Folder for metadata
|    +- docs.md           # file describing content & structure of clean data files 
|  
|- CODE_OF_CONDUCT.md     # code of conduct for collaborators and contributors  
|- CONTRIBUTING.md        # information on how to suggest improvements to the code  
|- LICENSE.md             # specifies the conditions of use and reuse of the code, data & text  
|- NEWS.md                # details updates, version status, and other milestones  
|- README.md              # top-level description of content and guide to users  
|- makefile.R             # file to execute data cleaning & organizing using scripts in `code` folder  

How to use

• Create a new repository for your project using this as a template
• Open the README and edit it the title.

Key Concepts

This template structure is based on Noble 2009. Fundamental to this approach is:

• Keep the raw data unaltered in their own folder. Any derived data (intermediate or final) is kept in a different folder.
• Code is kept separate from data.
• Manuscript production is in a different respository.
• The README describes the purpose of the repository and all the contents.
• There is a top-level makefile that documents the computational study in executable form. Those files may call out to other .R scripts in the code folder.
• There is a metadata file describing all input and output data files.

TO ADD:

• Github actions:

  • data validation with pointblank
  • automatic versioning after corrections & saving txt file of clean_data with version no.

• More advanced, likely overkill:

  • Renv for package versions
  • targets to manage project workflow.

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Inspired by R packages starters, rrrpkg, template, and rr-init.