RandomWalker Wiki - Home"

In RandomWalker: Generate Random Walks Compatible with the 'tidyverse'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(RandomWalker)

#| fig.alt: >
#|   RandomWalker package logo featuring a hexagonal design with abstract paths
#|   representing random walks, symbolizing the package's functionality for generating
#|   and analyzing stochastic processes.
knitr::include_graphics("../man/figures/logo.png")

Welcome to the RandomWalker Wiki! This comprehensive guide will help you master the RandomWalker R package for generating, visualizing, and analyzing random walks.

📖 What is RandomWalker?

RandomWalker is a comprehensive R package that provides a unified, tidyverse-compatible interface for generating random walks of various types. Whether you're modeling stock prices, simulating particle movements, or exploring stochastic processes, RandomWalker makes it easy to:

• Generate random walks from 27+ different probability distributions
• Create walks in 1D, 2D, or 3D space
• Visualize walks with beautiful, interactive plots
• Compute comprehensive statistical summaries
• Work seamlessly with tidyverse tools

🚀 Quick Navigation

Getting Started

• Installation - How to install the package
• Quick Start Guide - Get up and running in minutes
• Basic Concepts - Understanding random walks

Function Guides

• Automatic Random Walks - Using rw30() for instant results
• Continuous Distributions - Normal, Brownian, Gamma, Beta, and more
• Discrete Distributions - Binomial, Poisson, Geometric, and more
• Multi-Dimensional Walks - Working in 2D and 3D space

Advanced Topics

• Visualization Guide - Creating beautiful plots
• Statistical Analysis Guide - Computing summary statistics
• Use Cases and Examples - Real-world applications

Reference

• API Reference - Complete function documentation
• FAQ - Frequently Asked Questions
• Troubleshooting - Common issues and solutions

Contributing

• Contributing Guide - How to contribute to the project

💡 Key Features

🎲 27+ Distribution Types

Generate random walks from a wide variety of probability distributions including:

• Continuous: Normal, Brownian Motion, Geometric Brownian Motion, Beta, Cauchy, Chi-Squared, Exponential, F, Gamma, Log-Normal, Logistic, Student's t, Uniform, Weibull
• Discrete: Binomial, Discrete, Geometric, Hypergeometric, Multinomial, Negative Binomial, Poisson
• Custom: Define your own displacement functions

📐 Multi-Dimensional Support

• 1D random walks for time series analysis
• 2D random walks for spatial modeling
• 3D random walks for particle physics simulations

📊 Rich Visualizations

• Static plots with ggplot2
• Interactive visualizations with ggiraph
• Support for multiple walk comparison
• Customizable aesthetics

📈 Statistical Analysis

• Comprehensive summary statistics
• Cumulative functions (sum, product, min, max, mean)
• Confidence intervals
• Running quantiles
• Euclidean distance calculations
• Harmonic and geometric means
• Skewness and kurtosis

🔧 Tidyverse Compatible

Works seamlessly with:

• dplyr for data manipulation
• tidyr for data reshaping
• ggplot2 for custom visualizations
• Pipe operators (|> and %>%)

📦 Package Information

• Current Version: 1.0.0.9000 (development)
• CRAN Release: 1.0.0
• License: MIT
• Authors: Steven P. Sanderson II, MPH & Antti Rask
• R Version Required: >= 4.1.0

🔗 External Links

• Package Website: https://www.spsanderson.com/RandomWalker/
• GitHub Repository: https://github.com/spsanderson/RandomWalker
• Issue Tracker: https://github.com/spsanderson/RandomWalker/issues
• CRAN Page: https://cran.r-project.org/package=RandomWalker

📚 Learning Path

If you're new to RandomWalker, we recommend following this learning path:

1. Installation - Install the package
2. Quick Start Guide - Learn the basics
3. Automatic Random Walks - Use rw30() for quick results
4. Continuous Distribution Generators - Explore different distributions
5. Visualization Guide - Create beautiful plots
6. Statistical Analysis Guide - Analyze your walks
7. Use Cases and Examples - See real-world applications

🎯 Common Use Cases

• Finance: Model stock price movements with Geometric Brownian Motion
• Physics: Simulate particle diffusion with Brownian Motion
• Biology: Model organism movement patterns
• Computer Science: Generate test data for algorithms
• Education: Teach probability and stochastic processes
• Research: Explore theoretical properties of random walks

🤝 Getting Help

• Documentation: Read the vignettes with vignette("getting-started") or vignette("home")
• Issues: Report bugs at the GitHub Issues page
• Discussions: Ask questions in GitHub Discussions
• Email: Contact the maintainer at spsanderson\@gmail.com

🌟 Citation

If you use RandomWalker in your research, please cite it:

citation("RandomWalker")

Example: Quick Start

Here's a quick example to get you started with RandomWalker:

# Generate 30 random walks
walks <- rw30()

# View the first few rows
head(walks)

#| fig.alt: >
#|   Visualization of multiple random walks generated by rw30()
# Visualize the walks
visualize_walks(walks)

# Get summary statistics
walks |> 
  summarize_walks(.value = y) |>
  head()

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Ready to get started? Explore the package documentation and other vignettes to begin your journey with RandomWalker!

RandomWalker documentation built on Sept. 8, 2026, 1:06 a.m.