Day 14

Advent of Code: Worked Solutions

Setup

# Libraries
library(tidyverse)
library(unglue)

# Read input from file
input <- read_lines("../input/day14.txt", skip_empty_rows = TRUE)

# Parameters: dimensions of the room
room_w <- 101
room_h <- 103

Part 1

Extract numerical values from input text:

df <- input |>
  unglue_data("p={x},{y} v={vx},{vy}") |> 
  mutate(
    across(everything(), parse_number),
    robot_id = row_number(),
    .before = everything()
  )

For debugging: define a function to print the current state of the grid

print_grid <- function(df, w = room_w, h = room_h) {
  df |> 
    summarize(n = as.character(n()), .by = c(x, y)) |> 
    complete(
      x = 0:(room_w - 1), 
      y = 0:(room_h - 1), 
      fill = list(n = "")
    ) |> 
    mutate(n = str_pad(n, width = max(str_length(n)), side = "left")) |> 
    arrange(desc(y), x) |> 
    group_split(y) |> 
    map_chr(~ pull(.x, n) |> str_c(collapse = " ")) |> 
    cat(sep = "\n")
}

Define a function which gives the location of the robots after n seconds. Multiply n by the velocity, add to the current position, and modulo the room dimensions.

pass_time <- function(df, seconds, w = room_w, h = room_h) {
  df |> 
    mutate(
      x = (x + vx * seconds) %% w,
      y = (y + vy * seconds) %% h
    )
}

Define a function to get the current safety factor by counting the number of robots in each quadrant of the room:

get_safety_score <- function(df, w = room_w, h = room_h) {
  mid_w <- (room_w - 1) / 2
  mid_h <- (room_h - 1) / 2
  
  df |> 
    mutate(
      half_w = case_when(x < mid_w ~ 0, x > mid_w ~ 1),
      half_h = case_when(y < mid_h ~ 0, y > mid_h ~ 1),
      quadrant = half_w + 2 * half_h
    ) |> 
    drop_na(quadrant) |> 
    summarize(num_robots = n(), .by = quadrant) |> 
    pull(num_robots) |> 
    prod()
}

Compute safety score of puzzle input:

df |> 
  pass_time(100) |> 
  get_safety_score()

[1] 208437768

Part 2

First, find the cycle of seconds where the robots’ positions repeat (no more than the least common multiple of the room width and room height)

cycle <- DescTools::LCM(room_w, room_h)

For each unique snapshot in the cycle, test for randomness vs structure in the X-Y coordinates.

simulations <- map_dfr(
  1:cycle,
  \(seconds) {
    new <- pass_time(df, seconds)
    c(secs = seconds, xvar = var(new$x), yvar = var(new$y))
  }
)

simulations |> 
  arrange(xvar, yvar, secs) |> 
  filter(row_number() == 1) |> 
  pull(secs)

[1] 7492