R Script Profiling Tool

A comprehensive R package for profiling and analyzing the performance of R scripts. This tool provides detailed metrics on execution time and memory usage for different steps of your R code, along with interactive HTML reports.

Features

• Step-by-step Profiling: Track execution time and memory usage for individual code blocks
• Interactive HTML Reports: Generate beautiful, interactive reports with visualizations
• Memory Usage Analysis: Monitor memory consumption for each step
• Easy Integration: Simple function calls to profile your existing R code
• Dependency Management: Automatically installs required packages
• Detailed Documentation: Comprehensive function reference and examples
• Demo Script: Ready-to-run example showing the tool in action

Installation

# Install from GitHub
if (!require("devtools")) install.packages("devtools")
devtools::install_github("username/profiling")

# Or use the script directly
source("profiling.R")

Quick Demo

Try out the profiling tool with the included demo script demo.R

This will:

1. Profile several example operations
2. Generate an interactive HTML report
3. Open the report in your default browser

Quick Start

# Initialize profiling
.init_profiling(script_name = "my_analysis.R")

# Profile a code block
profile_code("data_loading", {
  # Your data loading code here
  data <- read.csv("data.csv")
})

# Profile another step
profile_code("data_processing", {
  # Your processing code here
  processed_data <- transform(data, new_col = old_col * 2)
})

# Generate report
generate_profiling_report(show_report = TRUE)

Documentation

For complete documentation of all functions, parameters, and advanced usage, see DOCUMENTATION.md.

Main Functions

Core Functions

• .init_profiling(...) - Initialize the profiling environment
• profile_code(step_name, expr) - Profile a code block (recommended for most use cases)
• generate_profiling_report(...) - Generate an HTML report

Advanced Usage

• .start_profiling_step(step_name) - Manually start profiling a step
• .end_profiling_step(step_name) - Manually end profiling a step

For detailed parameter descriptions and examples, see the full documentation.

Example Report

The generated HTML report includes:

• Summary Statistics: Total time, average step time, peak memory usage
• Interactive Visualizations:
  • Execution time by step
  • Memory usage over time
  • Detailed metrics table
• Export Options: Save report as HTML or print to PDF
• Responsive Design: Works on desktop and mobile devices

Demo Script

A ready-to-run demo script is included (demo.R) that demonstrates:

• Basic profiling of code blocks
• Nested profiling operations
• Report generation and viewing

To run the demo:

source("demo.R")

Getting Help

For questions or issues, please open an issue.

Dependencies

The script will automatically install these packages if not already installed:

• pryr - Memory usage tracking
• tictoc - Precise timing
• knitr/kableExtra - Report generation
• ggplot2/plotly - Interactive visualizations
• DT - Interactive tables
• gridExtra - Layout management

Best Practices

1. Profile early and often to identify performance bottlenecks
2. Keep step names descriptive but concise
3. Focus on profiling the most time-consuming parts of your code
4. Compare profiles before and after optimization to measure improvements

License

MIT