## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  fig.alt = "Random walk visualization examples"
)

## ----setup, echo=FALSE, message=FALSE-----------------------------------------
library(RandomWalker)
library(dplyr)
library(ggplot2)

## ----install_example, eval=FALSE----------------------------------------------
# # From CRAN (stable)
# install.packages("RandomWalker")
# 
# # From GitHub (development)
# devtools::install_github("spsanderson/RandomWalker")

## ----dependencies_example, eval=FALSE-----------------------------------------
# install.packages(c("dplyr", "tidyr", "purrr", "rlang", "patchwork", "NNS", "ggiraph"))

## ----simple_walk_example------------------------------------------------------
library(RandomWalker)
rw30() |> head(10)  # Generates 30 walks with 100 steps each

## ----visualize_example, fig.alt="Visualization of random walks showing multiple panels"----
library(RandomWalker)
rw30() |> visualize_walks()

## ----custom_walk_example, fig.alt="Custom normal random walk with specified parameters"----
random_normal_walk(
  .num_walks = 10,
  .n = 100,
  .mu = 0,
  .sd = 1,
  .initial_value = 0
) |> visualize_walks()

## ----seed_example-------------------------------------------------------------
set.seed(123)
walks <- rw30()

# Same seed produces same result
set.seed(123)
walks2 <- rw30()
identical(walks, walks2)  # TRUE

## ----custom_distribution_example, eval=FALSE----------------------------------
# # Custom displacement function
# my_displacement <- function() {
#   # Your custom logic here
#   return(some_value)
# }
# 
# custom_walk(
#   .num_walks = 10,
#   .n = 100,
#   .custom_fns = my_displacement
# )

## ----twod_example-------------------------------------------------------------
random_normal_walk(.num_walks = 10, .n = 100, .dimensions = 2)

## ----visualize_2d, fig.alt="2D random walk visualization with x-y coordinates"----
library(ggplot2)

walk_2d <- random_normal_walk(.num_walks = 10, .n = 100, .dimensions = 2)

ggplot(walk_2d, aes(x = cum_sum_x, y = cum_sum_y, color = walk_number)) +
  geom_path() +
  coord_equal() +
  theme_minimal()

## ----interactive_example, eval=FALSE------------------------------------------
# rw30() |> visualize_walks(.interactive = TRUE)

## ----pluck_example, fig.alt="Single panel visualization showing cumulative sum"----
# Single panel
random_normal_walk() |> visualize_walks(.pluck = "cum_sum_y")

## ----pluck_multiple, fig.alt="Multiple panel visualization showing y, cumulative sum, and cumulative mean"----
# Multiple panels
random_normal_walk() |> visualize_walks(.pluck = c("y", "cum_sum_y", "cum_mean_y"))

## ----alpha_example, eval=FALSE------------------------------------------------
# rw30() |> visualize_walks(.alpha = 0.3)  # More transparent
# rw30() |> visualize_walks(.alpha = 0.9)  # More opaque

## ----export_example, eval=FALSE-----------------------------------------------
# library(ggplot2)
# 
# p <- rw30() |> visualize_walks()
# ggsave("my_plot.png", p, width = 12, height = 8, dpi = 300)

## ----colors_example, fig.alt="Random walk with custom color palette"----------
p <- random_normal_walk(.num_walks = 5) |>
  visualize_walks(.pluck = "y")

p + scale_color_viridis_d()

## ----summary_example----------------------------------------------------------
walks <- rw30()

# Overall summary
walks |> summarize_walks(.value = y)

## ----summary_by_walk----------------------------------------------------------
# By walk
walks |> summarize_walks(.value = y, .group_var = walk_number) |> head()

## ----subset_example, fig.alt="Maximum and minimum walks visualization"--------
walks <- rw30()

# Get walk with maximum final value
max_walk <- walks |> subset_walks(.value = "y", .type = "max")

# Get walk with minimum final value
min_walk <- walks |> subset_walks(.value = "y", .type = "min")

# Visualize both walks together
combined <- dplyr::bind_rows(
  dplyr::mutate(max_walk, type = "Maximum"),
  dplyr::mutate(min_walk, type = "Minimum")
)
visualize_walks(combined, .pluck = "y") +
  ggplot2::facet_wrap(~type)

## ----speed_example, eval=FALSE------------------------------------------------
# # Sample walks
# walks_large |>
#   filter(walk_number %in% sample(levels(walk_number), 50)) |>
#   visualize_walks(.alpha = 0.2)
# 
# # Downsample steps
# walks_large |>
#   filter(step_number %% 10 == 0) |>
#   visualize_walks()

## ----parallel_example, eval=FALSE---------------------------------------------
# library(future)
# library(furrr)
# 
# plan(multisession, workers = 4)
# 
# walks_list <- future_map(1:10, ~random_normal_walk(.num_walks = 100), .options = furrr_options(seed = 123))

## ----attributes_example-------------------------------------------------------
walks <- rw30()
atb <- get_attributes(walks)
names(atb)

## ----convert_example, eval=FALSE----------------------------------------------
# # To base R data.frame
# as.data.frame(walks)
# 
# # To matrix (values only)
# walks |> select(y) |> as.matrix()
# 
# # To time series
# ts(walks$y, frequency = 1)
# 
# # To wide format
# walks |> tidyr::pivot_wider(names_from = walk_number, values_from = y)

## ----error_example1, eval=FALSE-----------------------------------------------
# # Wrong
# walks |> summarize_walks()
# 
# # Correct
# walks |> summarize_walks(.value = y)

## ----error_example2, eval=FALSE-----------------------------------------------
# walk_2d <- random_normal_walk(.dimensions = 2)
# 
# # Wrong
# walk_2d |> summarize_walks(.value = y)
# 
# # Correct
# walk_2d |> summarize_walks(.value = cum_sum_y)

## ----dplyr_example------------------------------------------------------------
library(dplyr)

random_normal_walk(.num_walks = 10) |>
  filter(step_number > 50) |>
  mutate(positive = cum_sum_y > 0) |>
  group_by(walk_number) |>
  summarize(prop_positive = mean(positive))

## ----shiny_example, eval=FALSE------------------------------------------------
# library(shiny)
# library(RandomWalker)
# 
# ui <- fluidPage(
#   numericInput("num_walks", "Number of Walks:", 10),
#   plotOutput("walks_plot")
# )
# 
# server <- function(input, output) {
#   output$walks_plot <- renderPlot({
#     random_normal_walk(.num_walks = input$num_walks) |>
#       visualize_walks(.pluck = "cum_sum_y")
#   })
# }
# 
# shinyApp(ui, server)

## ----ggplot2_example, fig.alt="Custom ggplot2 theme applied to random walk"----
library(ggplot2)

p <- rw30() |> visualize_walks(.pluck = "y")

# Customize further
p +
  labs(title = "My Custom Title") +
  theme_bw()

## ----stock_example, fig.alt="Stock price simulation using geometric Brownian motion"----
stock_prices <- geometric_brownian_motion(
  .num_walks = 100,
  .n = 252,  # Trading days
  .mu = 0.08,  # 8% expected return
  .sigma = 0.25,  # 25% volatility
  .initial_value = 100
)
visualize_walks(stock_prices)

## ----particle_example, eval=FALSE---------------------------------------------
# particles <- brownian_motion(
#   .num_walks = 50,
#   .n = 1000,
#   .dimensions = 3
# )

## ----algorithm_example, eval=FALSE--------------------------------------------
# # Generate test walks
# test_data <- discrete_walk(
#   .num_walks = 1000,
#   .n = 100,
#   .upper_probability = 0.5
# )
# 
# # Run your algorithm
# result <- my_algorithm(test_data)

## ----citation_example, eval=FALSE---------------------------------------------
# citation("RandomWalker")