2021 Day 3

Author

Nathan Moore

— Day 3: Binary Diagnostic —

Use the binary numbers in your diagnostic report to calculate the gamma rate and epsilon rate, then multiply them together. What is the power consumption of the submarine? (Be sure to represent your answer in decimal, not binary.)

library(tidyverse)

my_file <- here::here("2021", "data-2021-03.txt")
x <- readLines(my_file)

Maybe write an explanation of the solution approach here

# split into individual columns
bins = str_split_fixed(x, "", 12) %>% as.data.frame()

# need them to be numeric
bin_num = sapply(bins, as.numeric)

gamma_rate =  strtoi(paste(as.numeric(colSums(bin_num) >= 500), collapse = ""), base = 2)

epsilon_rate = strtoi(paste(as.numeric(colSums(bin_num) < 500), collapse = ""), base = 2)

part_one = gamma_rate * epsilon_rate
  
part_one

[1] 3895776

— Part Two —

Use the binary numbers in your diagnostic report to calculate the oxygen generator rating and CO2 scrubber rating, then multiply them together. What is the life support rating of the submarine? (Be sure to represent your answer in decimal, not binary.)

return_oxygen <- function(z, colnum = 1) {
  # z is a matrix of binary digits
  # calculate the length
  # then which digit is more common in the column
  # input of colnum

  # number of rows of array
  lngth = nrow(z)

  # wdth for stopping
  wdth = ncol(z)

  # column with greater number of digits
  dgt = as.integer(sum(z[,colnum]) >= ceiling(lngth / 2))

  # filter (is that the right word for non-tidyverse?)
  row_bool = z[,colnum] == dgt
  z = z[row_bool, , drop = FALSE]

  # return if we are at the end
  if (colnum == wdth) {
    print("colnum = 12")
    return(z)
  }

  if (nrow(z) == 1) {
    print("only one row")
    return(z)
  }

  # increment by 1
  colnum = colnum + 1

  # call same function again with new column to check
  z = return_oxygen(z, colnum)

}

return_c02 <- function(z, colnum = 1) {
  # this is the same as above but with less than sign

  # number of rows of array
  lngth = nrow(z)

  # wdth for stopping
  wdth = ncol(z)

  # column with greater number of digits
  dgt = as.integer(sum(z[,colnum]) < ceiling(lngth / 2))

  # filter (is that the right word for non-tidyverse?)
  row_bool = z[,colnum] == dgt
  z = z[row_bool, , drop = FALSE]

  # return if we are at the end
  if (colnum == wdth) return(z)

  if (nrow(z) == 1) return(z)

  # increment by 1
  colnum = colnum + 1

  # call same function again with new column to check
  z = return_c02(z, colnum)

}

And then use the functions

# width of one string
wdth = nchar(x[[1]])

# split into individual columns
bins = str_split_fixed(x, "", wdth) %>% as.data.frame()

# need them to be numeric
bin_num = sapply(bins, as.numeric)

# recursive function call
oxygen_binary = return_oxygen(bin_num)

[1] "colnum = 12"

# convert to integer for oxygen
oxygen_rating = strtoi(paste(oxygen_binary, collapse = ""), base = 2)

# recursive with less than, c02
c02_binary = return_c02(bin_num)

# convert to integer
c02_rating = strtoi(paste(c02_binary, collapse = ""), base = 2)

# answer
oxygen_rating * c02_rating

[1] 7928162