Answer to Question #215593 in Python for Kyamo

Question

Simulate a two dimensional random walk and provide a picture of it

Answer 1

move_step <- function(cur_pos, grid, prob = 0.04, size = 1){
  opts <- grid %>%
    rowwise() %>%
    mutate(across(.fns = ~(.x-.env$cur_pos[[cur_column()]])^2,
                  .names = '{.col}_square_diff')) %>%
    filter(sqrt(sum(c_across(ends_with("_square_diff"))))<=.env$size) %>%
    select(-ends_with("_square_diff")) %>%
    left_join(y = mutate(cur_pos, current = TRUE), by = names(grid)) 
  new_pos <- opts %>%
    mutate(weight = case_when(current ~ 1-(prob*(n()-1)), #calculate chance to move, 
                              TRUE ~ prob),               #in higher dimensions, we may have more places to move
           weight = if_else(weight<0, 0, weight)) %>%    #thus depending on prob, we may always move.
    sample_n(size = 1, weight = weight) %>%
    select(-weight, -current)
  new_pos
}

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(gganimate)

move_step <- function(cur_pos, grid, prob = 0.04, size = 1){
  opts <- grid %>%
    filter(across(.fns =  ~ between(.x, .env$cur_pos[[cur_column()]]-.env$size, .env$cur_pos[[cur_column()]]+.env$size))) %>%
    left_join(y = mutate(cur_pos, current = TRUE), by = names(grid)) 
  new_pos <- opts %>%
    mutate(weight = case_when(current ~ 1-(prob*(n()-1)), #calculate chance to move, 
                              TRUE ~ prob),               #in higher dimensions, we may have more places to move
           weight = if_else(weight<0, 0, weight)) %>%    #thus depending on prob, we may always move.
    sample_n(size = 1, weight = weight) %>%
    select(-weight, -current)
  new_pos
}

sim_walk <- function(cur_pos, grid, grid_prob = 0.04, steps = 50, size = 1){
  iterations <- cur_pos
  for(i in seq_len(steps)){
    cur_pos <- move_step(cur_pos, grid, prob = grid_prob, size = size)
    iterations <- bind_rows(iterations, cur_pos)
  }
  iterations$i <- 1:nrow(iterations)
  iterations
}

origin <- data.frame(x = 0, y =0)
small_grid <- expand.grid(x = -1:1, y = -1:1)
small_walk <- sim_walk(cur_pos = origin,
                       grid = small_grid)

ggplot(small_walk, aes(x, y)) +
  geom_path() +
  geom_point(color = "red") +
  transition_reveal(i) +
  labs(title = "Step {frame_along}") +
  coord_fixed()

large_grid <- expand.grid(x = -10:10, y = -10:10)
large_walk <- sim_walk(cur_pos = origin,
                       grid = large_grid,
                       steps = 100)

ggplot(large_walk, aes(x,y)) +
  geom_path() +
  geom_point(color = "red") +
  transition_reveal(i)  +
  labs(title = "Step {frame_along}") +
  xlim(c(-10,10)) + ylim(c(-10,10))+
  coord_fixed()

large_walk %>% 
  count(x, y) %>%
  right_join(y = expand.grid(x = -10:10, y = -10:10), by = c("x","y")) %>%
  mutate(n = if_else(is.na(n), 0L, n)) %>%
  ggplot(aes(x,y)) +
  geom_tile(aes(fill = n)) +
  coord_fixed()

multi_dim_walk <- sim_walk(cur_pos = data.frame(x = 0, y = 0, z = 0),
                           grid =  expand.grid(x = -20:20, y = -20:20, z = -20:20),
                           steps = 100, size = 2)

library(cowplot)
plot_grid(
  ggplot(multi_dim_walk, aes(x, y)) + geom_path(),
  ggplot(multi_dim_walk, aes(x, z)) + geom_path(),
  ggplot(multi_dim_walk, aes(y, z)) + geom_path())