R Data Analysis Framework

A modular R framework for data analysis, data manipulation, and visualization. This library provides functions for handling data, with emphasis on reproducibility and clean code practices.

Overview

This framework implements data analysis workflows focusing on:

• Geometric calculations and transformations
• Descriptive statistics and data summarization
• Statistical hypothesis testing
• Data preprocessing and cleaning
• Correlation analysis

Installation

# Clone repository
git clone https://github.com/username/r-statistical-analysis.git

# Install required R packages
install.packages(c("stats", "utils", "base"))

Project Structure

.
├── assignment1/
│   ├── assignment1.R      # Core statistical functions
│   └── chol.txt           # Sample cholesterol dataset
├── assignment2/
│   ├── assignment2.R      # Data preprocessing utilities  
│   └── Assignment2.RData  # Multi-dataset RData file
└── README.md

Core Features

Geometric Analysis Module

• Circle and sphere calculations (area, volume, circumference)
• Multi-dimensional geometric transformations
• Configurable precision and rounding

Statistical Analysis Tools

• Descriptive statistics generation
• Two-sample t-test automation
• Missing value handling
• Outlier detection and processing

Data Processing Utilities

• Data normalization
• Correlation analysis for metrics
• Categorical data imputation
• Flexible data frame operations

Usage Examples

Basic Circle Calculations

# Calculate circle area
calCircle('ac', 4)

# Multiple calculations
calCircle2(c('AC', 'VS'), seq(5,25,5))

Statistical Analysis

# Generate descriptive statistics
table1(patient_num, c("GLUC", "TGL", "HDL", "LDL"))

# Perform t-tests
myTtest(dat=chol, classVar="sex", numVar=c("age", "chol", "tg"))

Data Preprocessing

# Handle missing values
impute(dat=patient, varlist=c("LDL", "HRT", "MAMM"))

# Calculate correlations
myCorTest(chol, "bmi", c("sbp", "dbp", "vldl", "hdl", "ldl"))

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/enhancement)
3. Commit changes (git commit -am 'Add enhancement')
4. Push to branch (git push origin feature/enhancement)
5. Create Pull Request

License

This project is licensed under the MIT License - see LICENSE for details.

Citation

If you use this framework in your research, please cite:

@software{r_data_analysis,
  author = {Shadman, Nabil},
  title = {R Data Analysis Framework},
  year = {2020},
  url = {https://github.com/nabilshadman/r-data-analysis}
}