Day 3: Binary Diagnostic

Note

To calculate the mode I used this approach.

Part One

library(tidyverse)

most_common <- function(x) {
    ux <- unique(x)
    ux[which.max(tabulate(match(x, ux)))]
}

least_common <- function(x) {
    ux <- unique(x)
    ux[which.min(tabulate(match(x, ux)))]
}

input <- read_lines(file = "inputs/2021/03.txt")

widths <- rep(1, 12) # each line has 12 digits
names <- map2_chr(rep("d", 12), 1:12, paste0)
names(widths) <- names

digits <- tibble(original = input) |> 
    separate_wider_position(cols = original, widths = widths)

gamma_rate <- digits |>
    map_chr(most_common) |> 
    paste0(collapse = "") |> 
    strtoi(base = 2)

epsilon_rate <- digits |>
    map_chr(least_common) |> 
    paste0(collapse = "")|> 
    strtoi(base = 2)

cat(gamma_rate * epsilon_rate)
## 3009600

Part Two

most_common <- function(x) {
    ux <- sort(unique(x))
    
    table <- tabulate(match(x, ux))
    
    # if 0 and 1 are equally common, keep values with a 1
    # in the position being considered
    if (table[1] == table[2]) return(ux[2])
    
    else return(ux[which.max(table)])
}

least_common <- function(x) {
    ux <- sort(unique(x))
    
    table <- tabulate(match(x, ux))
    
    # if 0 and 1 are equally common, keep values with a 0
    # in the position being considered
    if (table[1] == table[2]) return(ux[1])
    
    else return(ux[which.min(table)])
}

oxygen_generator_rating <- digits

for (i in names) {
    value <- oxygen_generator_rating |>
        select(all_of(i)) |>
        map_chr(most_common)
    
    oxygen_generator_rating <- oxygen_generator_rating |>
        filter(.data[[i]] == value)
    
    # if only one number left, stop
    if (nrow(oxygen_generator_rating) == 1) break
}

oxygen_generator_rating <- oxygen_generator_rating |>
    paste0(collapse = "")|>
    strtoi(base = 2)

CO2_scrubber_rating <- digits

for (i in names) {
    value <- CO2_scrubber_rating |>
        select(all_of(i)) |>
        map_chr(least_common)
    
    CO2_scrubber_rating <- CO2_scrubber_rating |>
        filter(.data[[i]] == value)
    
    # if only one number left, stop
    if (nrow(CO2_scrubber_rating) == 1) break
}

CO2_scrubber_rating <- CO2_scrubber_rating |>
    paste0(collapse = "")|>
    strtoi(base = 2)

cat(oxygen_generator_rating * CO2_scrubber_rating)
## 6940518