Catalogue des procédures
1 Clessnverse
1.1 Overview
The Datagotchi Meta Package is a comprehensive R ecosystem designed to streamline data analysis, visualization, and reporting workflows. It combines multiple specialized packages into a cohesive framework, providing tools for everything from data processing to advanced visualizations with a consistent, user-friendly interface.
1.2 Core Philosophy
The package follows several key principles:
- Consistency: All sub-packages follow similar design patterns and naming conventions
- Accessibility: Tools are designed to be usable by both beginners and advanced users
- Reproducibility: Emphasis on creating reproducible analyses and reports
- Aesthetics: Custom themes and visualization tools that maintain a consistent, professional look
1.3 Package Ecosystem
1.3.1 Data Processing & Analysis
- Advanced data frame inspection and summarization tools
- Survey data processing utilities
- Statistical analysis functions
- Factor analysis capabilities
- Robust CSV handling and data merging utilities
1.3.2 Visualization
- Custom ggplot2 themes (light and dark variants)
- Interactive plotting tools
- Specialized visualization functions:
- Bar plots with custom styling
- Cartograms for geographic data
- Decision trees
- Custom overlay capabilities
- Built-in color schemes for political data visualization
1.3.3 Reporting
- Report generation templates
- Interactive graph creation utilities
- Custom fonts and styling options
- Logo and branding integration
1.4 Key Features
1.4.1 Consistent Theming
The package provides a unified visual language through: - Custom fonts (PixelOperatorSC and WebFont) - Predefined color schemes - Standardized plotting themes - Brand-consistent visualization options
1.4.2 Interactive Tools
- Interactive graph creation with
make_graph()
- Decision tree visualization
- Custom barplot creation with
datagotchi_barplot()
1.4.3 Geographic Visualization
- Built-in US state coordinates for cartograms
- Political data visualization tools
- State-level analysis capabilities
1.4.4 Data Processing
- Robust data import capabilities
- Survey data handling
- Missing data analysis
- Statistical processing utilities
1.5 Getting Started
1.5.1 Installation
# Install the meta package from GitHub
::install_github("clessn/datagotchi")
devtools
# Load the package
library(datagotchi)
1.5.2 Basic Usage
# Load required packages
library(datagotchi)
# Create a basic visualization with the Datagotchi theme
ggplot(your_data) +
geom_point() +
theme_datagotchi_light()
# Use the interactive graph maker
make_graph(your_data)
1.6 Design Philosophy
The Datagotchi Meta Package is built around several core principles:
- Modularity: Each component is designed to work both independently and as part of the larger ecosystem
- Consistency: All components share common design patterns and naming conventions
- Flexibility: Tools can be customized to meet specific needs while maintaining consistency
- User Experience: Focus on intuitive interfaces and clear documentation
1.7 Future Development
The package is actively maintained and developed, with plans for: - Additional visualization tools - Enhanced reporting capabilities - Expanded geographic analysis tools - More interactive features - Improved documentation and vignettes
1.8 Contributing
We welcome contributions from the community. Areas where you can contribute include: - Bug reports and fixes - New features and enhancements - Documentation improvements - Example use cases and tutorials
1.9 Support and Resources
- GitHub Repository: clessn/datagotchi
- Bug Reports: Please use the GitHub issues page
- Documentation: Available through package documentation and vignettes
1.10 Citation
When using the Datagotchi Meta Package in your research, please cite:
@software{datagotchi,
title = {Datagotchi: A Comprehensive R Package for Data Analysis and Visualization},
author = {Your Team},
year = {2024},
url = {https://github.com/clessn/datagotchi}
}
1.11 License
[Add your license information here]