Day 3

Advent of Code: Worked Solutions

Setup

Import libraries:

library(vctrs)
library(tidyverse)

Read input from file:

input <- read_lines("../input/day03.txt")

Part 1

Define functions to compute the least and most common values in a vector (using greatest/least values as tiebreakers, as defined later in part 2):

most_common  <- \(x) vec_count(x) |> slice_max(count) |> pull(key) |> max()
least_common <- \(x) vec_count(x) |> slice_min(count) |> pull(key) |> min()

Define a function to convert a vector of binary integers to a decimal number:

bin_to_num <- \(x) strtoi(str_flatten(x), base = 2)

Prep the input by splitting binary numbers into separate digits by positions:

df <- input |> 
  str_split("") |> 
  enframe(name = "id") |> 
  unnest_longer(value, indices_to = "pos") |>   
  mutate(value = as.integer(value))

Compute the most and least common values in each position to get the gamma and epsilon values, then multiply them together to get the power consumption:

df |> 
  summarize(
    gamma = most_common(value), 
    epsilon = least_common(value), 
    .by = pos
  ) |> 
  summarize(across(c(gamma, epsilon), bin_to_num)) |> 
  prod()

Part 2

Define a set of functions to compute the oxygen and co2 ratings:

get_rating <- function(data, f = c("most_common", "least_common")) {
  
  for (i in unique(data$pos)) {
    id_lst <- data |> 
      filter(pos == i) |> 
      filter(value == get(f)(value)) |> 
      pull(id)
    
    data <- filter(data, id %in% id_lst)
    
    if (length(id_lst) == 1)
      return(bin_to_num(data$value))
  }
}

oxygen_rating <- partial(get_rating, f = "most_common")
co2_rating    <- partial(get_rating, f = "least_common")

Compute the final life support rating:

oxygen_rating(df) * co2_rating(df)