Setup

d <- "data/01.txt" |>
  read_lines() |>
  as.numeric() |>
  enframe(value = "x")

d

## # A tibble: 2,000 x 2
##     name     x
##     
##  1     1   174
##  2     2   180
##  3     3   179
##  4     4   186
##  5     5   184
##  6     6   176
##  7     7   177
##  8     8   190
##  9     9   173
## 10    10   174
## # ... with 1,990 more rows

Part 1

d_1 <-
  d |>
  mutate(
    diff = x - lag(x)
  ) |>
  summarize(
    x = sum(diff > 0, na.rm = TRUE)
  )

d_1

## # A tibble: 1 x 1
##       x
##   
## 1  1316

Part 2

d_2 <- d

d_2$y <- rollsum(d$x, 3, align = "left", fill = 0)

d_2

## # A tibble: 2,000 x 3
##     name     x     y
##      
##  1     1   174   533
##  2     2   180   545
##  3     3   179   549
##  4     4   186   546
##  5     5   184   537
##  6     6   176   543
##  7     7   177   540
##  8     8   190   537
##  9     9   173   523
## 10    10   174   525
## # ... with 1,990 more rows

d_2 |>
  mutate(
    diff = y - lag(y)
  ) |>
  summarize(
    y = sum(diff > 0, na.rm = TRUE)
  )

## # A tibble: 1 x 1
##       y
##   
## 1  1344

Setup

d_init <- read_tsv(
  file = "data/02.txt",
  col_names = FALSE
)

d <-
  d_init |>
  separate(
    col = X1,
    into = c("position", "unit"),
    convert = TRUE
  ) |>
  mutate(
    unit = if_else(
      condition = position == "up",
      true = unit * -1L,
      false = unit
    ),
    group = if_else(
      condition = position == "forward",
      true = "x",
      false = "y"
    )
  )

d

## # A tibble: 1,000 x 3
##    position  unit group
##         
##  1 forward      4 x    
##  2 forward      6 x    
##  3 down         8 y    
##  4 forward      3 x    
##  5 forward      9 x    
##  6 down         7 y    
##  7 down         7 y    
##  8 down         1 y    
##  9 forward      1 x    
## 10 forward      5 x    
## # ... with 990 more rows

Part 1

d |>
  group_by(group) |>
  summarize(
    units = sum(unit)
  ) |>
  pull(units) |>
  prod()

## [1] 1714680

Part 2

d |>
  mutate(
    aim = cumsum(ifelse(group == "y", unit, 0))
  ) |>
  filter(group == "x") |>
  summarize(
    x = sum(unit),
    y = sum(unit * aim),
    solution = x * y
  )

## # A tibble: 1 x 3
##       x      y   solution
##           
## 1  1980 991459 1963088820

Setup

d_init <-
  read_tsv(
    file = "data/03.txt",
    col_names = FALSE
  )

n_cols <- nchar(d_init$X1[1])

col_names <- paste0(
  "col_",
  str_pad(0:n_cols, width = 2, pad = 0)
)

col_names

##  [1] "col_00" "col_01" "col_02" "col_03" "col_04" "col_05" "col_06" "col_07"
##  [9] "col_08" "col_09" "col_10" "col_11" "col_12"

d <-
  d_init |>
  separate(
    col = X1,
    into = col_names,
    sep = "",
    convert = TRUE
  ) |>
  select(-1)

d

## # A tibble: 1,000 x 12
##    col_01 col_02 col_03 col_04 col_05 col_06 col_07 col_08 col_09 col_10 col_11
##                         
##  1      1      1      1      1      1      0      1      1      0      1      1
##  2      1      0      0      1      1      1      0      0      0      1      1
##  3      0      1      1      1      0      1      1      1      1      1      0
##  4      0      1      1      0      1      1      0      1      0      0      1
##  5      0      0      1      0      1      0      0      0      1      0      1
##  6      1      1      1      1      0      1      0      0      1      0      0
##  7      0      1      0      0      0      1      1      1      0      0      1
##  8      1      0      0      0      0      1      0      0      1      1      1
##  9      1      1      0      1      0      1      0      1      0      0      0
## 10      1      0      0      0      0      1      1      0      0      0      1
## # ... with 990 more rows, and 1 more variable: col_12 

Part 1

d_1 <-
  d |>
  pivot_longer(
    cols = everything()
  ) |>
  count(name, value) |>
  group_by(name) |>
  summarize(
    gamma = value[which.max(n)],
    epsilon = value[which.min(n)]
  )

d_1

## # A tibble: 12 x 3
##    name   gamma epsilon
##         
##  1 col_01     1       0
##  2 col_02     0       1
##  3 col_03     0       1
##  4 col_04     1       0
##  5 col_05     1       0
##  6 col_06     1       0
##  7 col_07     0       1
##  8 col_08     1       0
##  9 col_09     1       0
## 10 col_10     0       1
## 11 col_11     0       1
## 12 col_12     0       1

gamma <-
  paste0(d_1$gamma, collapse = "") |>
  strtoi(base = 2)

epsilon <-
  paste0(d_1$epsilon, collapse = "") |>
  strtoi(base = 2)

gamma * epsilon

## [1] 3969000

Part 2

example_d_init <- c(
  "00100", "11110", "10110",
  "10111", "10101", "01111",
  "00111", "11100", "10000",
  "11001", "00010", "01010"
) |>
  enframe(value = "X1")

example_n_cols <- nchar(example_d_init$X1[1])

example_col_names <- paste0(
  "col_",
  str_pad(0:example_n_cols, width = 2, pad = 0)
)

example_d <-
  example_d_init |>
  separate(
    col = X1,
    into = example_col_names,
    sep = "",
    convert = TRUE
  ) |>
  select(-(1:2))

example_d

## # A tibble: 12 x 5
##    col_01 col_02 col_03 col_04 col_05
##             
##  1      0      0      1      0      0
##  2      1      1      1      1      0
##  3      1      0      1      1      0
##  4      1      0      1      1      1
##  5      1      0      1      0      1
##  6      0      1      1      1      1
##  7      0      0      1      1      1
##  8      1      1      1      0      0
##  9      1      0      0      0      0
## 10      1      1      0      0      1
## 11      0      0      0      1      0
## 12      0      1      0      1      0

i <- 1
i_max <- ncol(example_d)
d_oxygen <- d_co2 <- example_d

while (i <= i_max) {
  ref_col <- rlang::sym(example_col_names[i + 1])
  
  d_oxygen_count <-
    d_oxygen |>
    add_count({{ ref_col }}, name = "n")
  
  d_co2_count <-
    d_co2 |>
    add_count({{ ref_col }}, name = "n")
  
  if (length(unique(d_oxygen_count$n)) == 1) {
    d_oxygen <-
      d_oxygen_count |>
      filter({{ ref_col }} == 1)
    
    d_co2 <-
      d_co2_count |>
      filter({{ ref_col }} == 0)
    
  } else {
    d_oxygen <-
      d_oxygen_count |>
      filter(n == max(n))
    
    d_co2 <-
      d_co2_count |>
      filter(n == min(n))
  }
  
  i <- i + 1
}

oxygen <-
  d_oxygen |>
  select(-n) |>
  unite(
    col = binary,
    everything(),
    sep = ""
  ) |>
  mutate(decimal = strtoi(binary, base = 2))

oxygen

## # A tibble: 1 x 2
##   binary decimal
##       
## 1 10111       23

co2 <-
  d_co2 |>
  select(-n) |>
  unite(
    col = binary,
    everything(),
    sep = ""
  ) |>
  mutate(decimal = strtoi(binary, base = 2))

co2

## # A tibble: 1 x 2
##   binary decimal
##       
## 1 01010       10

## Matches!
oxygen$decimal * co2$decimal

## [1] 230

i <- 1
i_max <- ncol(d)
d_oxygen <- d_co2 <- d

while (i <= i_max) {
  ref_col <- rlang::sym(col_names[i + 1])
  
  d_oxygen_count <-
    d_oxygen |>
    add_count({{ ref_col }}, name = "n")
  
  d_co2_count <-
    d_co2 |>
    add_count({{ ref_col }}, name = "n")
  
  if (nrow(d_oxygen_count) == 1) {
    ## If there is only one row, do nothing
    d_oxygen <- d_oxygen_count
    
  } else if (length(unique(d_oxygen_count$n)) == 1) {
    ## If all counts are the same, keep the 1's
    d_oxygen <-
      d_oxygen_count |>
      filter({{ ref_col }} == 1)
    
    ## If there are no 1's, do not remove anything
    if (nrow(d_oxygen) == 0) {
      d_oxygen <- d_oxygen_count
    }
    
  } else {
    ## Keep the most common value
    d_oxygen <-
      d_oxygen_count |>
      filter(n == max(n))
  }
  
  if (nrow(d_co2_count) == 1) {
    ## If there is only one row, do nothing
    d_co2 <- d_co2_count
    
  } else if (length(unique(d_co2_count$n)) == 1) {
    ## If all counts are the same, keep the 0's
    d_co2 <-
      d_co2_count |>
      filter({{ ref_col }} == 0)
    
    ## If there are no 0's, do not remove anything
    if (nrow(d_co2) == 0) {
      d_co2 <- d_co2_count
    }
    
  } else {
    ## Keep the least common value
    d_co2 <-
      d_co2_count |>
      filter(n == min(n))
  }
  
  i <- i + 1
}

oxygen <-
  d_oxygen |>
  select(-n) |>
  unite(
    col = binary,
    everything(),
    sep = ""
  ) |>
  mutate(decimal = strtoi(binary, base = 2))

oxygen

## # A tibble: 1 x 2
##   binary       decimal
##             
## 1 100111001101    2509

co2 <-
  d_co2 |>
  select(-n) |>
  unite(
    col = binary,
    everything(),
    sep = ""
  ) |>
  mutate(decimal = strtoi(binary, base = 2))

co2

## # A tibble: 1 x 2
##   binary       decimal
##             
## 1 011010100101    1701

oxygen$decimal * co2$decimal

## [1] 4267809

Setup

bingo_numbers <- read_lines(
  file = "data/04.txt",
  n_max = 1
) %>% 
  str_split(",") %>% 
  unlist() %>% 
  as.integer()

bingo_numbers

##   [1]  4 75 74 31 76 79 27 19 69 46 98 59 83 23 90 52 87  6 11 92 80 51 43  5 94
##  [26] 17 15 67 25 30 48 47 62 71 85 58 60  1 72 99  3 35 42 10 96 49 37 36  8 44
##  [51] 70 40 45 39  0 63  2 78 68 53 50 77 20 55 38 86 54 93 26 88 12 91 95 34  9
##  [76] 14 33 66 41 13 28 57 29 73 56 22 89 21 64 61 32 65 97 84 18 82 81  7 16 24

d_init <- read_tsv(
  file = "data/04.txt",
  skip = 1,
  col_names = FALSE
)

d_init

## # A tibble: 500 x 1
##    X1            
##             
##  1 30 46 94 20  2
##  2 53 67 69 75 65
##  3 27 24 85 28 60
##  4 57 58 42 36 78
##  5 35 98 87 91 93
##  6 72 71 91 73 19
##  7 2 13 14  8 74 
##  8 42 34 31 56  9
##  9 82 59 44 67 79
## 10 49  6 98 10 30
## # ... with 490 more rows

(d_rows <- nrow(d_init))

## [1] 500

(col_names <- str_c("col_", 1:5))

## [1] "col_1" "col_2" "col_3" "col_4" "col_5"

l_d <-
  d_init %>% 
  separate(
    col = X1,
    into = col_names,
    convert = TRUE
  ) %>% 
  mutate(
    id = sort(rep(1:100, 5))
  ) %>% 
  group_split(id, .keep = FALSE)

l_d[1:3]

## 
## >[3]>
## [[1]]
## # A tibble: 5 x 5
##   col_1 col_2 col_3 col_4 col_5
##       
## 1    30    46    94    20     2
## 2    53    67    69    75    65
## 3    27    24    85    28    60
## 4    57    58    42    36    78
## 5    35    98    87    91    93
## 
## [[2]]
## # A tibble: 5 x 5
##   col_1 col_2 col_3 col_4 col_5
##       
## 1    72    71    91    73    19
## 2     2    13    14     8    74
## 3    42    34    31    56     9
## 4    82    59    44    67    79
## 5    49     6    98    10    30
## 
## [[3]]
## # A tibble: 5 x 5
##   col_1 col_2 col_3 col_4 col_5
##       
## 1    95    24    25    11    34
## 2    57    65    41    92     8
## 3    91    26     1    62    38
## 4    47    93     4    37     0
## 5    15    44    33    20    97

Part 1

l_d_1 <- l_d
bingo_check <- TRUE
i <- 1
winning_board <- numeric()

while (bingo_check) {
  print(i)
  bingo_call <- bingo_numbers[i]
  
  for (j in seq_along(l_d_1)) {
    ## Update values to 0 that are called
    l_d_1[[j]] <-
      l_d_1[[j]] %>% 
      mutate(
        across(
          .fns = ~ ifelse(
            test = .x == bingo_call,
            yes = 0,
            no = .x
          )
        )
      )
    
    if (any(colSums(l_d_1[[j]]) == 0 | rowSums(l_d_1[[j]]) == 0)) {
      print("Bingo!")
      print(str_c("Winning number was ", bingo_call))
      winning_board <- j
      bingo_check <- FALSE
      break
    }
  }
  
  i <- i + 1
}

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10
## [1] 11
## [1] 12
## [1] 13
## [1] 14
## [1] 15
## [1] 16
## [1] 17
## [1] 18
## [1] 19
## [1] 20
## [1] 21
## [1] 22
## [1] "Bingo!"
## [1] "Winning number was 51"

winning_board

## [1] 34

l_d_1[[winning_board]]

## # A tibble: 5 x 5
##   col_1 col_2 col_3 col_4 col_5
##       
## 1     0    18     0    35    55
## 2     0    85     0    56    82
## 3     0    26    24    29    43
## 4     0     0     0    45    13
## 5     0    12    99    94    47

(bingo_sum <- sum(l_d_1[[winning_board]]))

## [1] 763

bingo_call * bingo_sum

## [1] 38913

Part 2

l_d_2 <- l_d
bingo_check <- TRUE
i <- 1
winning_board <- numeric()
last_board <- numeric()

while (bingo_check) {
  bingo_call <- bingo_numbers[i]
  
  for (j in seq_along(l_d_2)) {
    ## Update values to 0 that are called
    l_d_2[[j]] <-
      l_d_2[[j]] %>% 
      mutate(
        across(
          .fns = ~ ifelse(
            test = .x == bingo_call,
            yes = 0,
            no = .x
          )
        )
      )
    
    if (any(colSums(l_d_2[[j]]) == 0 | rowSums(l_d_2[[j]]) == 0)) {
      if (!j %in% winning_board) {
        winning_board[length(winning_board) + 1] <- j
      }
    }
    
    if (length(winning_board) == length(l_d_2)) {
      bingo_check <- FALSE
      last_bard <- j
      break
    }
  }
  
  i <- i + 1
}

last_board <- l_d_2[winning_board[length(winning_board)]]

last_board

## 
## >[1]>
## [[1]]
## # A tibble: 5 x 5
##   col_1 col_2 col_3 col_4 col_5
##       
## 1     0    32     0     0     0
## 2     0     0     0     0    82
## 3     0     0    97     0     0
## 4     0     0     0     0     0
## 5     0     0     0    65     0

(bingo_sum <- sum(unlist(last_board)))

## [1] 276

bingo_call * bingo_sum

## [1] 16836