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Generate random walks of various types with tidyverse compatibility
To view the full wiki, click here: Full RandomWalker Wiki
RandomWalker is a comprehensive R package that makes it easy to generate, visualize, and analyze random walks. Whether youβre modeling stock prices, simulating particle movements, or exploring stochastic processes, RandomWalker provides a unified, tidyverse-compatible interface with extensive distribution support.
- π² 27+ Distribution Types: Generate random walks from Normal, Brownian Motion, Geometric Brownian Motion, Cauchy, Beta, Gamma, Poisson, and many more distributions
- π Multi-Dimensional Support: Create walks in 1D, 2D, or 3D space
- π Rich Visualizations: Built-in plotting functions with support for both static and interactive visualizations
- π Statistical Analysis: Comprehensive summary statistics including cumulative functions, confidence intervals, and distance metrics
- π§ Tidyverse Compatible: Works seamlessly with dplyr, tidyr, and ggplot2
- β‘ Easy to Use: Sensible defaults with extensive customization options
Install the stable version from CRAN:
install.packages("RandomWalker")Or get the development version from GitHub for the latest features and bug fixes:
# install.packages("devtools") devtools::install_github("spsanderson/RandomWalker")
The rw30() function provides a quick way to generate 30 random walks
with 100 steps each:
Γγ°γ€ 3 #> walk_number step_number y #> <fct> <int> <dbl> #> 1 1 1 0 #> 2 1 2 -0.315 #> 3 1 3 0.413 #> 4 1 4 -0.351 #> 5 1 5 -1.53 #> 6 1 6 -2.44 #> 7 1 7 -3.19 #> 8 1 8 -3.58 #> 9 1 9 -4.95 #> 10 1 10 -3.75">
library(RandomWalker) # Generate random walks walks <- rw30() head(walks, 10) #> # A tibble: 10 Γγ°γ€ 3 #> walk_number step_number y #> <fct> <int> <dbl> #> 1 1 1 0 #> 2 1 2 -0.315 #> 3 1 3 0.413 #> 4 1 4 -0.351 #> 5 1 5 -1.53 #> 6 1 6 -2.44 #> 7 1 7 -3.19 #> 8 1 8 -3.58 #> 9 1 9 -4.95 #> 10 1 10 -3.75
Create beautiful visualizations with a single function call:
rw30() |> visualize_walks()
Line plot showing 30 different random walk paths over time with varying trajectories
Get comprehensive statistical summaries of your random walks:
Γγ°γ€ 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 -0.624 -0.900 49.1 -13.9 18.7 #> # i 8 more variables: variance <dbl>, sd <dbl>, min_val <dbl>, max_val <dbl>, #> # harmonic_mean <dbl>, geometric_mean <dbl>, skewness <dbl>, kurtosis <dbl> # Summary by walk rw30() |> summarize_walks(.value = y, .group_var = walk_number) |> head(10) #> # A tibble: 10 Γγ°γ€ 17 #> walk_number fns fns_name dimensions mean_val median range quantile_lo #> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 rw30 Rw30 1 3.88 3.90 10.6 -0.636 #> 2 2 rw30 Rw30 1 0.848 0.922 10.4 -3.85 #> 3 3 rw30 Rw30 1 3.32 3.09 10.2 -0.536 #> 4 4 rw30 Rw30 1 -3.40 -3.30 10.2 -7.30 #> 5 5 rw30 Rw30 1 7.28 7.04 13.7 1.10 #> 6 6 rw30 Rw30 1 -8.04 -9.73 19.2 -18.0 #> 7 7 rw30 Rw30 1 -2.50 -2.56 11.3 -6.96 #> 8 8 rw30 Rw30 1 -0.348 -0.426 7.21 -3.41 #> 9 9 rw30 Rw30 1 7.19 5.56 16.2 1.44 #> 10 10 rw30 Rw30 1 1.42 1.44 12.8 -4.34 #> # i 9 more variables: quantile_hi <dbl>, variance <dbl>, sd <dbl>, #> # min_val <dbl>, max_val <dbl>, harmonic_mean <dbl>, geometric_mean <dbl>, #> # skewness <dbl>, kurtosis <dbl>">
# Overall summary rw30() |> summarize_walks(.value = y) #> # A tibble: 1 Γγ°γ€ 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 -0.624 -0.900 49.1 -13.9 18.7 #> # i 8 more variables: variance <dbl>, sd <dbl>, min_val <dbl>, max_val <dbl>, #> # harmonic_mean <dbl>, geometric_mean <dbl>, skewness <dbl>, kurtosis <dbl> # Summary by walk rw30() |> summarize_walks(.value = y, .group_var = walk_number) |> head(10) #> # A tibble: 10 Γγ°γ€ 17 #> walk_number fns fns_name dimensions mean_val median range quantile_lo #> <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 rw30 Rw30 1 3.88 3.90 10.6 -0.636 #> 2 2 rw30 Rw30 1 0.848 0.922 10.4 -3.85 #> 3 3 rw30 Rw30 1 3.32 3.09 10.2 -0.536 #> 4 4 rw30 Rw30 1 -3.40 -3.30 10.2 -7.30 #> 5 5 rw30 Rw30 1 7.28 7.04 13.7 1.10 #> 6 6 rw30 Rw30 1 -8.04 -9.73 19.2 -18.0 #> 7 7 rw30 Rw30 1 -2.50 -2.56 11.3 -6.96 #> 8 8 rw30 Rw30 1 -0.348 -0.426 7.21 -3.41 #> 9 9 rw30 Rw30 1 7.19 5.56 16.2 1.44 #> 10 10 rw30 Rw30 1 1.42 1.44 12.8 -4.34 #> # i 9 more variables: quantile_hi <dbl>, variance <dbl>, sd <dbl>, #> # min_val <dbl>, max_val <dbl>, harmonic_mean <dbl>, geometric_mean <dbl>, #> # skewness <dbl>, kurtosis <dbl>
# Normal walk with custom parameters random_normal_walk( .num_walks = 5, .n = 50, .mu = 0, .sd = 0.1, .initial_value = 100 ) |> visualize_walks()
# Geometric Brownian Motion (great for stock prices!) geometric_brownian_motion( .num_walks = 10, .n = 100, .mu = 0.05, .sigma = 0.2, .initial_value = 100 ) |> visualize_walks()
Γγ°γ€ 14 #> walk_number step_number x y cum_sum_x cum_prod_x cum_min_x #> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0.0268 -0.0711 0.0268 0 0.0268 #> 2 1 2 0.0268 0.0582 0.0536 0 0.0268 #> 3 1 3 -0.195 -0.0251 -0.141 0 -0.195 #> 4 1 4 -0.0504 -0.148 -0.192 0 -0.195 #> 5 1 5 0.00382 0.0865 -0.188 0 -0.195 #> 6 1 6 0.131 0.139 -0.0567 0 -0.195 #> 7 1 7 0.0420 0.0549 -0.0148 0 -0.195 #> 8 1 8 0.0384 -0.0772 0.0236 0 -0.195 #> 9 1 9 -0.258 0.149 -0.234 0 -0.258 #> 10 1 10 -0.195 0.144 -0.429 0 -0.258 #> # i 7 more variables: cum_max_x <dbl>, cum_mean_x <dbl>, cum_sum_y <dbl>, #> # cum_prod_y <dbl>, cum_min_y <dbl>, cum_max_y <dbl>, cum_mean_y <dbl> # 3D random walk random_normal_walk(.num_walks = 2, .n = 50, .dimensions = 3) |> head(10) #> # A tibble: 10 Γγ°γ€ 20 #> walk_number step_number x y z cum_sum_x cum_prod_x #> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0.190 0.147 0.0425 0.190 0 #> 2 1 2 -0.0333 -0.0159 0.0287 0.157 0 #> 3 1 3 0.124 -0.0312 0.0523 0.281 0 #> 4 1 4 -0.0133 -0.00990 -0.0491 0.267 0 #> 5 1 5 0.0863 -0.0759 -0.106 0.354 0 #> 6 1 6 -0.0173 -0.0564 0.0971 0.336 0 #> 7 1 7 0.0345 -0.0564 -0.0301 0.371 0 #> 8 1 8 0.0345 0.222 0.108 0.405 0 #> 9 1 9 -0.0566 -0.0325 0.0971 0.349 0 #> 10 1 10 0.104 0.199 0.123 0.453 0 #> # i 13 more variables: cum_min_x <dbl>, cum_max_x <dbl>, cum_mean_x <dbl>, #> # cum_sum_y <dbl>, cum_prod_y <dbl>, cum_min_y <dbl>, cum_max_y <dbl>, #> # cum_mean_y <dbl>, cum_sum_z <dbl>, cum_prod_z <dbl>, cum_min_z <dbl>, #> # cum_max_z <dbl>, cum_mean_z <dbl>">
# 2D random walk random_normal_walk(.num_walks = 3, .n = 100, .dimensions = 2) |> head(10) #> # A tibble: 10 Γγ°γ€ 14 #> walk_number step_number x y cum_sum_x cum_prod_x cum_min_x #> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0.0268 -0.0711 0.0268 0 0.0268 #> 2 1 2 0.0268 0.0582 0.0536 0 0.0268 #> 3 1 3 -0.195 -0.0251 -0.141 0 -0.195 #> 4 1 4 -0.0504 -0.148 -0.192 0 -0.195 #> 5 1 5 0.00382 0.0865 -0.188 0 -0.195 #> 6 1 6 0.131 0.139 -0.0567 0 -0.195 #> 7 1 7 0.0420 0.0549 -0.0148 0 -0.195 #> 8 1 8 0.0384 -0.0772 0.0236 0 -0.195 #> 9 1 9 -0.258 0.149 -0.234 0 -0.258 #> 10 1 10 -0.195 0.144 -0.429 0 -0.258 #> # i 7 more variables: cum_max_x <dbl>, cum_mean_x <dbl>, cum_sum_y <dbl>, #> # cum_prod_y <dbl>, cum_min_y <dbl>, cum_max_y <dbl>, cum_mean_y <dbl> # 3D random walk random_normal_walk(.num_walks = 2, .n = 50, .dimensions = 3) |> head(10) #> # A tibble: 10 Γγ°γ€ 20 #> walk_number step_number x y z cum_sum_x cum_prod_x #> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 1 0.190 0.147 0.0425 0.190 0 #> 2 1 2 -0.0333 -0.0159 0.0287 0.157 0 #> 3 1 3 0.124 -0.0312 0.0523 0.281 0 #> 4 1 4 -0.0133 -0.00990 -0.0491 0.267 0 #> 5 1 5 0.0863 -0.0759 -0.106 0.354 0 #> 6 1 6 -0.0173 -0.0564 0.0971 0.336 0 #> 7 1 7 0.0345 -0.0564 -0.0301 0.371 0 #> 8 1 8 0.0345 0.222 0.108 0.405 0 #> 9 1 9 -0.0566 -0.0325 0.0971 0.349 0 #> 10 1 10 0.104 0.199 0.123 0.453 0 #> # i 13 more variables: cum_min_x <dbl>, cum_max_x <dbl>, cum_mean_x <dbl>, #> # cum_sum_y <dbl>, cum_prod_y <dbl>, cum_min_y <dbl>, cum_max_y <dbl>, #> # cum_mean_y <dbl>, cum_sum_z <dbl>, cum_prod_z <dbl>, cum_min_z <dbl>, #> # cum_max_z <dbl>, cum_mean_z <dbl>
# Discrete walk with upper/lower bounds discrete_walk( .num_walks = 5, .n = 100, .upper_bound = 1, .lower_bound = -1, .upper_probability = 0.55, .initial_value = 0 ) |> visualize_walks()
RandomWalker supports a wide variety of random walk types:
- Normal:
random_normal_walk(),random_normal_drift_walk() - Brownian Motion:
brownian_motion(),geometric_brownian_motion() - Beta:
random_beta_walk() - Cauchy:
random_cauchy_walk() - Chi-Squared:
random_chisquared_walk() - Exponential:
random_exponential_walk() - F-Distribution:
random_f_walk() - Gamma:
random_gamma_walk() - Log-Normal:
random_lognormal_walk() - Logistic:
random_logistic_walk() - Studentβs t:
random_t_walk() - Uniform:
random_uniform_walk() - Weibull:
random_weibull_walk() - And more!
- Binomial:
random_binomial_walk() - Discrete:
discrete_walk() - Geometric:
random_geometric_walk() - Hypergeometric:
random_hypergeometric_walk() - Multinomial:
random_multinomial_walk() - Negative Binomial:
random_negbinomial_walk() - Poisson:
random_poisson_walk()
- Custom Displacement:
custom_walk(),random_displacement_walk()
| Function | Description |
|---|---|
rw30() |
Quickly generate 30 random walks |
visualize_walks() |
Create visualizations (static or interactive) |
summarize_walks() |
Generate comprehensive statistics |
subset_walks() |
Subset walks by max/min values |
euclidean_distance() |
Calculate distances in multi-dimensional walks |
confidence_interval() |
Compute confidence intervals |
running_quantile() |
Calculate running quantiles |
- Getting Started Guide: See
vignette("getting-started") - Function Reference: Online Documentation
- Package Website: RandomWalker
- News and Updates: Check NEWS.md for latest changes
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
This package is licensed under the MIT License. See LICENSE.md for details.
- Steven P. Sanderson II, MPH - Author, Creator, Maintainer - GitHub
- Antti Rask - Contributor - Visualization functions
- Bug Reports: GitHub Issues
- Questions: Use GitHub Discussions or Stack Overflow with the
randomwalkertag - Website: https://www.spsanderson.com/RandomWalker/
If you use RandomWalker in your research, please cite:
citation("RandomWalker")Made with β€οΈ for the R community