RandomWalker Wiki - Home • RandomWalker

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

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:

Installation - Install the package
Quick Start Guide - Learn the basics
Automatic Random Walks - Use rw30() for quick results
Continuous Distribution Generators - Explore different distributions
Visualization Guide - Create beautiful plots
Statistical Analysis Guide - Analyze your walks
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 [email protected]

🌟 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)
#> # A tibble: 6 × 3
#>   walk_number step_number     y
#>   <fct>             <int> <dbl>
#> 1 1                     1  0   
#> 2 1                     2 -1.40
#> 3 1                     3 -1.14
#> 4 1                     4 -3.58
#> 5 1                     5 -3.59
#> 6 1                     6 -2.97

# Visualize the walks
visualize_walks(walks)

Visualization of multiple random walks generated by rw30()

# Get summary statistics
walks |> 
  summarize_walks(.value = y) |>
  head()
#> Registered S3 method overwritten by 'quantmod':
#>   method            from
#>   as.zoo.data.frame zoo
#> Warning: There was 1 warning in `dplyr::summarize()`.
#> ℹ In argument: `geometric_mean = exp(mean(log(y)))`.
#> Caused by warning in `log()`:
#> ! NaNs produced
#> # A tibble: 1 × 16
#>   fns   fns_name dimensions mean_val median range quantile_lo quantile_hi
#>   <chr> <chr>         <dbl>    <dbl>  <dbl> <dbl>       <dbl>       <dbl>
#> 1 rw30  Rw30              1   -0.479 -0.677  43.4       -13.4        12.8
#> # ℹ 8 more variables: variance <dbl>, sd <dbl>, min_val <dbl>, max_val <dbl>,
#> #   harmonic_mean <dbl>, geometric_mean <dbl>, skewness <dbl>, kurtosis <dbl>

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

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