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 you’re modeling stock prices, simulating particle movements, or exploring stochastic processes, RandomWalker provides a unified, tidyverse-compatible interface with extensive distribution support.
- 🎲 27+ Distribution Types: Generate random walks from Normal, Brownian Motion, Geometric Brownian Motion, Cauchy, Beta, Gamma, Poisson, and many more distributions
- 📐 Multi-Dimensional Support: Create walks in 1D, 2D, or 3D space
- 📊 Rich Visualizations: Built-in plotting functions with support for both static and interactive visualizations
- 📈 Statistical Analysis: Comprehensive summary statistics including cumulative functions, confidence intervals, and distance metrics
- 🔧 Tidyverse Compatible: Works seamlessly with dplyr, tidyr, and ggplot2
- ⚡ 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:
library(RandomWalker)
# Generate random walks
walks <- rw30()
head(walks, 10)
#> # A tibble: 10 × 3
#> walk_number step_number y
#> <fct> <int> <dbl>
#> 1 1 1 0
#> 2 1 2 0.952
#> 3 1 3 0.573
#> 4 1 4 0.292
#> 5 1 5 1.06
#> 6 1 6 1.39
#> 7 1 7 0.727
#> 8 1 8 0.186
#> 9 1 9 -0.305
#> 10 1 10 -0.310Create beautiful visualizations with a single function call:
rw30() |>
visualize_walks()
Get comprehensive statistical summaries of your random walks:
# Overall summary
rw30() |>
summarize_walks(.value = y)
#> # 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 -0.0134 0.302 43.1 -16.9 11.5
#> # 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 × 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 0.834 0.911 11.3 -4.14
#> 2 2 rw30 Rw30 1 -1.63 -1.22 9.43 -5.97
#> 3 3 rw30 Rw30 1 7.51 7.34 14.3 0.579
#> 4 4 rw30 Rw30 1 10.8 11.7 18.4 -0.147
#> 5 5 rw30 Rw30 1 -3.51 -3.96 12.8 -8.49
#> 6 6 rw30 Rw30 1 6.98 8.40 21.8 -1.90
#> 7 7 rw30 Rw30 1 0.392 0.458 11.1 -4.98
#> 8 8 rw30 Rw30 1 -0.0693 -0.495 9.60 -3.34
#> 9 9 rw30 Rw30 1 7.54 7.56 11.7 1.55
#> 10 10 rw30 Rw30 1 -9.22 -8.86 19.6 -18.5
#> # 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>Simulate continuous-time pendulum motion from randomized starting
angles. The solver (deSolve) and animation packages (gganimate,
gifski) are optional.
set.seed(287)
pendulum <- double_pendulum_walk(.num_walks = 2, .n = 101)
plot_double_pendulum(pendulum, .walk = 1)
animation <- animate_double_pendulum(pendulum, .walk = 1)
# Render explicitly with gganimate::animate(); construction saves no files.See vignette("double-pendulum", package = "RandomWalker") for units,
deterministic starts, and GIF rendering.
# 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()
# 2D random walk
random_normal_walk(.num_walks = 3, .n = 100, .dimensions = 2) |>
head(10)
#> # A tibble: 10 × 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 -0.0474 -0.117 -0.0474 0 -0.0474
#> 2 1 2 0.0274 -0.0564 -0.0201 0 -0.0474
#> 3 1 3 0.142 0.0586 0.122 0 -0.0474
#> 4 1 4 -0.103 0.276 0.0189 0 -0.103
#> 5 1 5 0.0206 -0.134 0.0395 0 -0.103
#> 6 1 6 -0.131 0.0759 -0.0919 0 -0.131
#> 7 1 7 -0.120 0.0155 -0.211 0 -0.131
#> 8 1 8 -0.0595 0.0214 -0.271 0 -0.131
#> 9 1 9 0.159 0.0155 -0.112 0 -0.131
#> 10 1 10 -0.233 0.178 -0.345 0 -0.233
#> # 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 × 20
#> walk_number step_number x y z cum_sum_x cum_prod_x
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -0.0119 -0.0963 0.154 -0.0119 0
#> 2 1 2 0.0345 -0.103 -0.278 0.0225 0
#> 3 1 3 0.139 -0.0586 -0.0603 0.162 0
#> 4 1 4 -0.0636 -0.0963 0.00208 0.0983 0
#> 5 1 5 -0.174 -0.170 0.0608 -0.0755 0
#> 6 1 6 0.124 -0.0995 0.112 0.0480 0
#> 7 1 7 -0.111 0.00989 -0.0555 -0.0633 0
#> 8 1 8 -0.0505 -0.159 0.00208 -0.114 0
#> 9 1 9 0.0366 -0.0686 0.00471 -0.0772 0
#> 10 1 10 0.00216 -0.131 -0.0555 -0.0750 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() - Student’s 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 ❤️ for the R community