Visualize Function Call Dependencies in R Source Code.

funviewR

funviewR is an R package that analyzes R source code to detect function definitions and internal dependencies, then visualizes them as interactive network graphs.

Features

• 📊 Analyze R code dependencies - Automatically detect function definitions and calls
• 🔍 Multi-file & directory support - Analyze multiple R files and directories together
• 🤖 Auto-detection - Automatically detects whether paths are files or directories
• 📈 Interactive visualization - Create beautiful dependency graphs with visNetwork
• 🔧 Detailed tooltips - View function arguments, return values, and documentation
• 🎯 Smart layout - Hierarchical graph layout with most-connected nodes centered
• ⚠️ Duplicate detection - Identify functions defined in multiple files
• ⚡ One-line plotting - Quick visualization with plot_dependency_graph()

Installation

From GitHub

You can install the development version from GitHub:

# Install remotes if you don't have it
install.packages("remotes")

# Install funviewR from GitHub
remotes::install_github("deamonpog/funviewR")

Usage

Quick Start (One-Line Solution)

The easiest way to visualize dependencies:

library(funviewR)

# Analyze files or directories - auto-detects which is which!
plot_dependency_graph(c("R/", "analysis.R", "tests/"))

# Single directory
plot_dependency_graph("R/")

# Specific files
plot_dependency_graph(c("script1.R", "script2.R"))

# With options
plot_dependency_graph("src/", 
                      recursive = TRUE, 
                      include_disconnected = FALSE)

Advanced Usage (Two-Step Process)

For more control and access to analysis data:

library(funviewR)

# Step 1: Analyze files
file_paths <- c("path/to/your/script.R")
dep_info <- analyze_internal_dependencies_multi(file_paths)

# Step 2: Create visualization
plot_interactive_dependency_graph(dep_info)

Working with Directories

# Get all R files from a directory
files <- get_r_files("R/")

# Get R files recursively
files <- get_r_files("R/", recursive = TRUE)

# Then analyze
dep_info <- analyze_internal_dependencies_multi(files)
graph <- plot_interactive_dependency_graph(dep_info)

Analyzing Multiple Sources

# Mix files and directories - automatically detected!
plot_dependency_graph(c(
  "R/core/",
  "main.R",
  "R/utils/",
  "tests/",
  "helpers.R"
), recursive = TRUE)

Working with the Results

The analyze_internal_dependencies_multi() function returns a list containing:

• dependency_map: A list mapping each function to its dependencies
• env: Environment containing all defined functions
• all_code_lines: All code lines from analyzed files
• function_file_map: Maps function names to their source files
• duplicates: Lists functions defined in multiple files

# Access the dependency map
dep_info$dependency_map

# Check for duplicate function definitions
if (length(dep_info$duplicates) > 0) {
  print("Warning: Duplicate functions found:")
  print(dep_info$duplicates)
}

Example Output

The interactive graph provides:

• Blue boxes: Defined functions with full metadata
• Gray ellipses: External or undefined functions
• Arrows: Function call relationships
• Tooltips: Hover over nodes to see:
  • Function arguments
  • Return values
  • Source file
  • Documentation (if available)

Requirements

• R >= 3.5.0
• codetools
• visNetwork
• igraph
• htmltools
• magrittr

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the GNU General Public License v3.0

Author

Chathura Jayalath

• Email: [email protected]

Acknowledgments

• Built with visNetwork for interactive graph visualization
• Uses R's codetools for dependency analysis
• Leverages igraph for graph algorithms and distance calculations
• Uses htmltools for safe HTML rendering
• Powered by magrittr pipe operator.