My 2022 Attempts

Day 1

Part 1

library(tidyverse)

input <- read_csv(here::here("2022", "day_01", "input_day01.txt"), 
                  col_names = "calories", skip_empty_rows = FALSE)

input |> 
  mutate(empty = ifelse(is.na(calories), 1, 0),
         elf = cumsum(empty)) |> 
  drop_na() |> 
  group_by(elf) |> 
  summarise(total_calories = sum(calories)) |>
  slice_max(total_calories) |> 
  pull(total_calories) |> 
  print()

[1] 72602

0.261 sec elapsed

Part 2

library(tidyverse)

input <- read_csv(here::here("2022", "day_01", "input_day01.txt"), 
                  col_names = "calories", skip_empty_rows = FALSE)

input |> 
  mutate(empty = ifelse(is.na(calories), 1, 0),
         elf = cumsum(empty)) |> 
  drop_na() |> 
  group_by(elf) |> 
  summarise(total_calories = sum(calories)) |>
  slice_max(total_calories, n = 3) |> 
  summarise(total_calories = sum(total_calories)) |> 
  pull(total_calories) |> 
  print()

[1] 207410

0.084 sec elapsed

Day 2

Didn’t have much time, so all I managed in the time was a series of case_when()’s. If I revisit I might use matrices instead.

Part 1

library(tidyverse)

input <- read_table(here::here("2022", "day_02", "input_day02.txt"), 
                    col_names = c("x", "y"))

input |> 
  rename(opp = x,
         me = y) |> 
  mutate(opp_base_score = case_when(opp == "A" ~ 1,
                                    opp == "B" ~ 2,
                                    opp == "C" ~ 3),
         me_base_score = case_when(me == "X" ~ 1,
                                   me == "Y" ~ 2,
                                   me == "Z" ~ 3),
         score_diff = me_base_score - opp_base_score,
         me_turn_score = case_when(score_diff == -1 | score_diff == 2 ~ 0,
                                   score_diff == 0 ~ 3,
                                   score_diff == 1 | score_diff == -2 ~ 6),
         me_game_score = me_base_score + me_turn_score) |> 
  summarise(total_score = sum(me_game_score)) |> 
  pull(total_score) |> 
  print()

[1] 14531

0.038 sec elapsed

Part 2

library(tidyverse)

input <- read_table(here::here("2022", "day_02", "input_day02.txt"), 
                    col_names = c("x", "y"))

input |> 
  rename(opp = x,
         outcome = y) |> 
  mutate(opp_base_score = case_when(opp == "A" ~ 1,
                                    opp == "B" ~ 2,
                                    opp == "C" ~ 3),
         me_turn_score = case_when(outcome == "X" ~ 0,
                                   outcome == "Y" ~ 3,
                                   outcome == "Z" ~ 6),
         me_base_score = case_when(me_turn_score == 3 ~ opp_base_score,
                                   me_turn_score == 0 & 
                                     opp_base_score > 1 ~ opp_base_score - 1,
                                   me_turn_score == 0 & opp_base_score == 1 ~ 3,
                                   me_turn_score == 6 & 
                                     opp_base_score < 3 ~ opp_base_score + 1,
                                   me_turn_score == 6 & opp_base_score == 3 ~ 1),
         me_game_score = me_turn_score + me_base_score) |> 
  summarise(total_score = sum(me_game_score)) |> 
  pull(total_score) |> 
  print()

[1] 11258

0.021 sec elapsed

Day 3

Would have made for a nice exercise in Python, but who doesn’t love a good regex challenge? 😊

Part 1

library(tidyverse)

input <- read_table(here::here("2022", "day_03", "input_day03.txt"), 
                    col_names = "rucksack")

priority_values <- tibble(char = c(letters, LETTERS)) |> 
  mutate(priority = seq(n()))

input |> 
  mutate(nitems = nchar(rucksack),
         comp1 = str_sub(rucksack, 1, nitems/2),
         comp2 = str_sub(rucksack, nitems/2 + 1, nitems),
         comp1_regex = map_chr(strsplit(comp1, ""), str_c, collapse = "|"),
         common = str_extract(comp2, comp1_regex)) |> 
  left_join(priority_values, by = c("common" = "char")) |> 
  summarise(sum = sum(priority)) |> 
  pull(sum) |> 
  print()

[1] 7793

0.046 sec elapsed

Part 2

library(tidyverse)

input <- read_table(here::here("2022", "day_03", "input_day03.txt"), 
                    col_names = "rucksack")

priority_values <- tibble(char = c(letters, LETTERS)) |> 
  mutate(priority = seq(n()))

input |> 
  mutate(group = rep(seq(unique(n())/3), each = 3)) |> 
  group_by(group) |> 
  summarise(group_items = str_c(rucksack, collapse = "-")) |> 
  separate(group_items, into = c("comp1", "comp2", "comp3")) |> 
  mutate(comp1_regex = map_chr(strsplit(comp1, ""), str_c, collapse = "|"),
         common_12 = str_extract_all(comp2, comp1_regex),
         comp12_regex = map_chr(common_12, str_c, collapse = "|"),
         common_all = str_extract(comp3, comp12_regex)) |> 
  left_join(priority_values, by = c("common_all" = "char")) |> 
  summarise(sum = sum(priority)) |> 
  pull(sum) |> 
  print()

[1] 2499

0.043 sec elapsed

Day 4

Part 1

library(tidyverse)

input <- read_table(here::here("2022", "day_04", "input_day04.txt"), 
                    col_names = "pairs")

input |> 
  separate(pairs, sep = ",|\\-", into = c("section1_start","section1_end", 
                                      "section2_start", "section2_end")) |> 
  mutate(across(everything(), ~ as.numeric(.x)),
         overlap_1_2 = section1_start >= section2_start & 
           section1_end <= section2_end,
         overlap_2_1 = section2_start >= section1_start & 
           section2_end <= section1_end) |> 
  filter(overlap_1_2 | overlap_2_1) |>
  nrow() |> 
  print()

[1] 453

0.043 sec elapsed

Part 2

library(tidyverse)

input <- read_table(here::here("2022", "day_04", "input_day04.txt"), 
                    col_names = "pairs")

input |> 
  separate(pairs, sep = ",|\\-", into = c("section1_start","section1_end", 
                                      "section2_start", "section2_end")) |> 
  mutate(across(everything(), ~ as.numeric(.x)),
         section1 = map2(section1_start, section1_end, ~seq(.x,.y)),
         section2 = map2(section2_start, section2_end, ~seq(.x,.y)),
         intersect = map2(section1, section2, ~ intersect(.x, .y)),
         n_overlapping = lengths(intersect)) |>
  filter(n_overlapping > 0) |> 
  nrow() |> 
  print()

[1] 919

0.071 sec elapsed

Day 5

Took some help from Anthony Durrant here, this one was difficult!

Part 1

library(tidyverse)

crates <- read_fwf(here::here("2022", "day_05", "input_day05.txt"), 
                   col_positions = fwf_widths(widths = NA)) |> 
  janitor::clean_names() |> 
  filter(str_detect(x1, "\\[")) |> 
  separate(x1,
           into = str_glue("stack_{seq(9)}"), 
           sep = c(seq(from = 4, to = 37, by = 4))) |> 
  mutate(across(everything(), ~ str_extract(.x, "[A-Z]"))) |> 
  as.list() |> 
  map(~ .x[grepl("[A-Z]", .x)])

steps <- read_delim(here::here("2022", "day_05", "input_day05.txt"), skip = 10,
                    col_names = c("action", "index", "f", "from", "t", "to")) |> 
  select(index, from, to) |> 
  mutate(across(c(from, to), ~ str_c("stack_", .x)))

rearrange_crates <- function(x, steps){
  from <- steps$from
  to <- steps$to
  n <- steps$index
  
  for(i in seq(n)) {  
    x[[to]] <- c(x[[from]][1], x[[to]])
    x[[from]]<- tail(x[[from]], length(x[[from]])-1)
  }
  return(x)
}

reduce(rlist::list.parse(steps), rearrange_crates, .init = crates) |> 
  map_chr(~ .x[1]) |> 
  paste(collapse = "") |> 
  print()

[1] "WHTLRMZRC"

0.371 sec elapsed

Part 2

library(tidyverse)

crates <- read_fwf(here::here("2022", "day_05", "input_day05.txt"), 
                   col_positions = fwf_widths(widths = NA)) |> 
  janitor::clean_names() |> 
  filter(str_detect(x1, "\\[")) |> 
  separate(x1,
           into = str_glue("stack_{seq(9)}"), 
           sep = c(seq(from = 4, to = 37, by = 4))) |> 
  mutate(across(everything(), ~ str_extract(.x, "[A-Z]"))) |> 
  as.list() |> 
  map(~ .x[grepl("[A-Z]", .x)])

steps <- read_delim(here::here("2022", "day_05", "input_day05.txt"), skip = 10,
                    col_names = c("action", "index", "f", "from", "t", "to")) |> 
  select(index, from, to) |> 
  mutate(across(c(from, to), ~ str_c("stack_", .x)))

rearrange_crates_pt2 <- function(x, steps){
  from <- steps$from
  to <- steps$to
  n <- steps$index
  
  x[[to]] <- c(x[[from]][seq(n)], x[[to]])
  x[[from]]<- tail(x[[from]], length(x[[from]])-n)
  return(x)
}

reduce(rlist::list.parse(steps), rearrange_crates_pt2, .init = crates) |> 
  map_chr(~ .x[1]) |> 
  paste(collapse = "") |> 
  print()

[1] "GMPMLWNMG"

0.143 sec elapsed

Day 6

Actually one of the first ones without any {tidyverse} functions!

Part 1

input <- readLines(here::here("2022", "day_06", "input_day06.txt"))

strsplit(input, split = "")[[1]] |> 
  slider::slide_lgl(~ length(.x) == 4 & length(.x) == length(unique(.x)), 
                    .before = 3) |> 
  (\(x) {which(x == TRUE)[1]})() |> 
  print()

[1] 1300

0.046 sec elapsed

Part 2

input <- readLines(here::here("2022", "day_06", "input_day06.txt"))

strsplit(input, split = "")[[1]] |> 
  slider::slide_lgl(~ length(.x) == 14 & length(.x) == length(unique(.x)), 
                    .before = 13) |> 
  (\(x) {which(x == TRUE)[1]})() |> 
  print()

[1] 3986

0.027 sec elapsed

Day 7

First Python solution this year, with some help from Viliam Pucik

Part 1

from collections import defaultdict
from itertools import accumulate

lines = open('./2022/day_07/input_day07.txt').read().splitlines()

dirs = defaultdict(int)

for line in lines:
  match line.split():
    case "$", "cd", "/":
      path = ["/"]
    case "$", "cd", "..":
      path.pop()
    case "$", "cd", dir:
      path.append(dir + "/")
    case "$" | "dir", *_:
      continue
    case size, _:
      for p in accumulate(path):
        dirs[p] += int(size)

total_sizes = sum(size for size in dirs.values() if size <= 1e5)
print(total_sizes)

0.013 seconds elapsed

Part 2

from collections import defaultdict
from itertools import accumulate

lines = open('./2022/day_07/input_day07.txt').read().splitlines()

dirs = defaultdict(int)

for line in lines:
  match line.split():
    case "$", "cd", "/":
      path = ["/"]
    case "$", "cd", "..":
      path.pop()
    case "$", "cd", dir:
      path.append(dir + "/")
    case "$" | "dir", *_:
      continue
    case size, _:
      for p in accumulate(path):
        dirs[p] += int(size)

dir_to_remove = min(size for size in dirs.values() if size >= dirs["/"] - 40e6)
print(dir_to_remove)

0.010 seconds elapsed

Day 8

For-loops just feel more appropriate in Python, with help from nitekat

Part 1

lines = open('./2022/day_08/input_day08.txt').read().splitlines()

trees = [[*map(int, line)] for line in lines]

n = 2 * (len(trees[0]) + len(trees) - 2)

for y in range(1, len(trees) - 1):
  for x in range(1, len(trees[0]) - 1):
    tree = trees[y][x]
    
    row = trees[y]
    col = [i[x] for i in trees]
    
    left = row[:x]
    right = row[x + 1 :]
    top = col[:y]
    bottom = col[y + 1 :]
    
    if tree > min(max(left), max(right), max(top), max(bottom)):
      n += 1

print(n)

0.092 seconds elapsed

Part 2

lines = open('./2022/day_08/input_day08.txt').read().splitlines()

trees = [[*map(int, line)] for line in lines]

s = 0
w = len(trees[0])
h = len(trees)

for y in range(1, len(trees) - 1):
  for x in range(1, len(trees[0]) - 1):
    t = trees[y][x]
    
    row = trees[y]
    col = [i[x] for i in trees]
    
    left = row[:x][::-1]
    right = row[x + 1 :]
    top = col[:y][::-1]
    bottom = col[y + 1 :]
    
    left_blocked = [i for i, v in enumerate(left) if v - t >= 0]
    left_score = x if len(left_blocked) == 0 else left_blocked[0] + 1
    
    right_blocked = [i for i, v in enumerate(right) if v - t >= 0]
    right_score = w - x - 1 if len(right_blocked) == 0 else right_blocked[0] + 1
    
    top_blocked = [i for i, v in enumerate(top) if v - t >= 0]
    top_score = y if len(top_blocked) == 0 else top_blocked[0] + 1
    
    bottom_blocked = [i for i, v in enumerate(bottom) if v - t >= 0]
    bottom_score = h - y - 1 if len(bottom_blocked) == 0 else bottom_blocked[0] + 1
    
    ss = left_score * right_score * top_score * bottom_score
    if ss > s:
        s = ss

print(s)

0.203 seconds elapsed

Day 9

Day 10

Part 1

library(tidyverse)

input <- read_table(here::here("2022", "day_10", "input_day10.txt"), 
                    col_names = c("operation", "argument"))

input |> 
  replace_na(list(argument = 0)) |> 
  mutate(steps = case_when(str_detect(operation, "add") ~ 2, 
                           str_detect(operation, "noop") ~ 1),
         step_num_start = tail(accumulate(steps, `+`, .init = 1), -1) - steps,
         x_reg = 1 + cumsum(argument),
         x_reg_prev = c(1, head(x_reg, -1))) |> 
  uncount(steps) |> 
  rowid_to_column(var = "step_num") |> 
  select(step_num, x_reg_prev) |>
  rename(x_reg = x_reg_prev) |> 
  filter(step_num %in% seq(20, n(), 40)) |> 
  summarise(total = sum(step_num * x_reg)) |> 
  pull(total) |> 
  print()

[1] 14060

0.039 sec elapsed

Part 2

library(tidyverse)

input <- read_table(here::here("2022", "day_10", "input_day10.txt"), 
                    col_names = c("operation", "argument"))

input |> 
  replace_na(list(argument = 0)) |> 
  mutate(steps = case_when(str_detect(operation, "add") ~ 2, 
                           str_detect(operation, "noop") ~ 1),
         step_num_start = tail(accumulate(steps, `+`, .init = 1), -1) - steps,
         x_reg = 1 + cumsum(argument),
         x_reg_prev = c(1, head(x_reg, -1))) |> 
  uncount(steps) |> 
  mutate(step_num = seq(1, n())) |>
  select(step_num, x_reg_prev) |>
  rename(x_reg = x_reg_prev) |> 
  mutate(x_pos = (step_num - 1) %% 40,
         pixel_on = abs(x_pos - x_reg) < 2,
         pixel = ifelse(pixel_on, "#", NA),
         row_num = cumsum(x_pos == 0)) |> 
  group_by(row_num) |> 
  mutate(x = row_number()) |> 
  filter(pixel_on) |> 
  ggplot(aes(x = x, y = row_num)) +
  geom_tile(fill = "#f2f2f2") +
  geom_text(label = "#", color = "#b3b3b3", size = 4) +
  scale_y_reverse() +
  coord_equal() + 
  theme_void() + 
  theme(plot.margin = margin(rep(10,4), unit = "mm"),
        plot.background = element_rect(fill = "#0f0f23"))

ggsave(here::here("2022", "day_10", "plot_day10_pt2.png"), last_plot(), 
       height = 3, width = 8, dpi = 300)

0.342 sec elapsed

Day 1

Part 1

Part 2

Day 2

Part 1

Part 2

Day 3

Part 1

Part 2

Day 4

Part 1

Part 2

Day 5

Part 1

Part 2

Day 6

Part 1

Part 2

Day 7

Part 1

Part 2

Day 8

Part 1

Part 2

Day 9

Day 10

Part 1

Part 2

Day 11

Day 12

Day 13

Day 14

Day 15

Day 16

Day 17

Day 18

Day 19

Day 20

Day 21

Day 22

Day 23

Day 24

Day 25