RandomWalker Wiki - Home

RandomWalker package logo featuring a hexagonal design with abstract paths representing random walks, symbolizing the package's functionality for generating and analyzing stochastic processes.

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

📚 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

🌟 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 0.319 
#> 3 1                     3 0.445 
#> 4 1                     4 0.189 
#> 5 1                     5 0.0557
#> 6 1                     6 0.120
# Visualize the walks
visualize_walks(walks)

Visualization of multiple random walks generated by rw30()

# Get summary statistics
walks |> 
  summarize_walks(.value = y) |>
  head()
#> 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   -0.714 -0.606  48.4       -14.6        12.2
#> # 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!