My 2021 Attempts

Day 1

Part 1

library(tidyverse)

data <- read_table("./2021/day_01/input_day01.txt", col_names = "depth")

data %>% 
  mutate(depth_lag = lag(depth),
         diff = depth - depth_lag,
         diff_bin = diff / abs(diff)) %>% 
  count(diff_bin)

data %>% 
  mutate(depth_lag = lag(depth),
         diff = depth - depth_lag,
         diff_bin = diff / abs(diff),
         diff_bin_label = case_when(diff_bin == -1 ~ "Smaller than last",
                                    diff_bin == 1 ~ "Larger than last",
                                    TRUE ~ "No measurement/no difference")) %>% 
  count(diff_bin_label) %>% 
  filter(str_detect(diff_bin_label, "Larger")) %>% 
  pull(n) %>% 
  print()

[1] 1390

0.159 sec elapsed

Part 2

library(tidyverse)

data <- read_table("./2021/day_01/input_day01.txt", col_names = "depth")

data %>% 
  slider::slide(., ~.x, .after = 2, .f = ~sum(.x)) %>% 
  enframe(name = "window_no", value = "depth") %>% 
  unnest(depth) %>% 
  mutate(depth_lag = lag(depth),
         diff = depth - depth_lag,
         diff_bin = diff / abs(diff),
         diff_bin_label = case_when(diff_bin == -1 ~ "Smaller than last",
                                    diff_bin == 1 ~ "Larger than last",
                                    TRUE ~ "No measurement/no difference")) %>% 
  count(diff_bin_label) %>% 
  filter(str_detect(diff_bin_label, "Larger")) %>% 
  pull(n) %>% 
  print()

[1] 1457

0.219 sec elapsed

Day 2

Part 1

library(tidyverse)

commands <- read_table("./2021/day_02/input_day02.txt", 
                       col_names = c("direction","value"))

final_position <- commands %>% 
  mutate(value_bin = case_when(direction == "up" ~ value * -1,
                               TRUE ~ value),
         direction_bin = case_when(direction == "forward" ~ "x",
                                   TRUE ~ "y")) %>% 
  group_by(direction_bin) %>% 
  summarise(pos = sum(value_bin)) %>% 
  ungroup()

final_position %>% 
  summarise(mult = prod(pos)) %>% 
  pull(mult) %>% 
  print()

[1] 1654760

0.025 sec elapsed

Part 2

library(tidyverse)

commands <- read_table("./2021/day_02/input_day02.txt", 
                       col_names = c("direction","value"))

aim <- depth <- 0
for (i in seq(nrow(commands))) {
  
  t_direction <- commands %>% 
    slice(i) %>% 
    pull(direction)
  t_value <- commands %>% 
    slice(i) %>% 
    pull(value)
  
  if (t_direction == "forward") {
    depth <- depth + (aim * t_value)
  } else if (t_direction == "down") {
    aim <- aim + t_value
  } else if (t_direction == "up") {
    aim <- aim - t_value
  }
  
}

final_position %>% 
  filter(direction_bin == "x") %>% 
  pull(pos) %>% 
  prod(., depth) %>% 
  print()

[1] 1956047400

2.342 sec elapsed

Day 3

Part 1

library(tidyverse)

input <- read_table("./2021/day_03/input_day03.txt", col_names = "full_binary")

n_bits <- input %>% 
  pull(full_binary) %>% 
  nchar() %>% 
  unique()

input_conv <- input %>% 
  separate(full_binary, into = str_glue("bit{seq(0,n_bits)}"), sep = "") %>% 
  select(-bit0) %>% 
  pivot_longer(starts_with("bit"), names_to = "bit_no") %>%
  mutate(value = as.numeric(value),
         bit_no = parse_number(bit_no)) %>% 
  group_by(bit_no) %>% 
  count(value)

gamma_rate <- input_conv %>% 
  slice_max(n) %>% 
  pull(value) %>% 
  str_c(collapse = "")

gamma_rate_dec <- strtoi(gamma_rate, base = 2)

epsilon_rate <- input_conv %>% 
  slice_min(n) %>% 
  pull(value) %>% 
  str_c(collapse = "")

epsilon_rate_dec <- strtoi(epsilon_rate, base = 2)

print(gamma_rate_dec * epsilon_rate_dec)

[1] 3148794

0.09 sec elapsed

Part 2

library(tidyverse)

input <- read_table("./2021/day_03/input_day03.txt", col_names = "full_binary")

input_conv_wide <- input %>% 
  separate(full_binary, into = str_glue("bit{seq(0,n_bits)}"), sep = "") %>% 
  select(-bit0) %>% 
  mutate(across(everything(), ~ as.numeric(.x)))

apply_rule <- function(df, iteration, minmax = "max") {
  
  most_common <- df %>% 
    select(all_of(iteration)) %>% 
    group_by(across(everything())) %>% 
    count() %>% 
    ungroup()
  
  if (minmax == "max") {
    most_common <- most_common %>% 
      slice_max(n) %>% 
      pull(1)
  } else {
    most_common <- most_common %>% 
      slice_min(n) %>% 
      pull(1)
  }
  
  if (length(most_common) > 1) {
    most_common <- ifelse(minmax == "max", 1, 0)
  }
  
  df <- df %>% 
    filter(across(iteration, ~ .x == most_common))
  
  return(df)
  
}

oxygen_rating <- reduce(seq(n_bits), ~ apply_rule(.x, .y, minmax = "max"), 
                        .init = input_conv_wide) %>% 
  as.numeric() %>% 
  str_c(collapse = "") %>% 
  strtoi(base = 2)

co2_rating <- reduce(seq(n_bits), ~ apply_rule(.x, .y, minmax = "min"), 
                     .init = input_conv_wide) %>% 
  as.numeric() %>% 
  str_c(collapse = "") %>% 
  strtoi(base = 2)

print(oxygen_rating * co2_rating)

[1] 2795310

1.513 sec elapsed

Day 4

Part 1

library(tidyverse)

input_numbers <- read_csv("./2021/day_04/input_day04.txt", col_names = FALSE) %>% 
  slice(1) %>% 
  mutate(across(everything(), ~ as.numeric(.x))) %>% 
  as_vector()

boards <- read_table("./2021/day_04/input_day04.txt", skip = 2, 
                     col_names = FALSE, skip_empty_rows = TRUE) %>% 
  rename_with(~ str_glue("col{parse_number(.x)}")) %>% 
  group_by(board_no = ceiling(row_number()/5)) %>% 
  rownames_to_column("row_no")

n_boards <- boards %>%
  ungroup() %>% 
  slice_max(board_no) %>% 
  pull(board_no) %>% 
  unique()

boards_long <- boards %>% 
  pivot_longer(starts_with("col"), names_to = "col_no", values_to = "number") %>% 
  mutate(col_no = parse_number(col_no)) %>% 
  relocate(board_no, .after = last_col())

run_numbers <- function(df, in_number, n_boards = NULL) {
  
  if (any(str_detect(names(df),"final_number"))) {
    return(df)
    stop()
  }
  
  board_filt <- df %>% 
    filter(number != in_number)
  
  n_unique_rows <- board_filt %>% 
    count(row_no) %>% 
    count(board_no) %>% 
    pull(n)
  
  n_unique_cols <- board_filt %>% 
    count(col_no) %>% 
    count(board_no) %>% 
    pull(n)
  
  #print(in_number)
  
  if (sum(n_unique_rows * n_unique_cols) < n_boards * 5 * 5) {
    board_filt <- df %>% 
      mutate(final_number = in_number)
    return(board_filt)
  } else {
    return(board_filt)
  }
  
}

bingo_game <- reduce(input_numbers, ~ run_numbers(.x, .y, n_boards = n_boards), 
                     .init = boards_long)

winning_board <- bingo_game %>% 
  filter(number != final_number) %>% 
  summarise(nrow = length(unique(row_no)),
            ncol = length(unique(col_no))) %>% 
  filter(nrow * ncol < 25) %>% 
  pull(board_no)

bingo_game %>% 
  ungroup() %>% 
  filter(number != final_number,
         board_no == winning_board) %>% 
  summarise(sum_unmarked = sum(number),
            final_number = unique(final_number)) %>% 
  as_vector() %>% 
  prod() %>% 
  print()

[1] 63424

1.574 sec elapsed

Part 2

library(tidyverse)

input_numbers <- read_csv("./2021/day_04/input_day04.txt", col_names = FALSE) %>% 
  slice(1) %>% 
  mutate(across(everything(), ~ as.numeric(.x))) %>% 
  as_vector()

boards <- read_table("./2021/day_04/input_day04.txt", skip = 2, 
                     col_names = FALSE, skip_empty_rows = TRUE) %>% 
  rename_with(~ str_glue("col{parse_number(.x)}")) %>% 
  group_by(board_no = ceiling(row_number()/5)) %>% 
  rownames_to_column("row_no")

n_boards <- boards %>%
  ungroup() %>% 
  slice_max(board_no) %>% 
  pull(board_no) %>% 
  unique()

boards_long <- boards %>% 
  pivot_longer(starts_with("col"), names_to = "col_no", values_to = "number") %>% 
  mutate(col_no = parse_number(col_no)) %>% 
  relocate(board_no, .after = last_col())

run_numbers <- function(df, in_number, n_boards = NULL) {
  
  if (any(str_detect(names(df),"final_number"))) {
    return(df)
    stop()
  }
  
  board_filt <- df %>% 
    filter(number != in_number)
  
  n_unique_rows <- board_filt %>% 
    count(row_no) %>% 
    count(board_no) %>% 
    pull(n)
  
  n_unique_cols <- board_filt %>% 
    count(col_no) %>% 
    count(board_no) %>% 
    pull(n)
  
  if (all(n_unique_rows * n_unique_cols < 5 * 4 + 1)) {
    board_filt <- df %>% 
      mutate(final_number = in_number)
    return(board_filt)
  } else {
    return(board_filt)
  }
  
}

bingo_game <- reduce(input_numbers, ~ run_numbers(.x, .y, n_boards = n_boards), 
                     .init = boards_long)

losing_board <- bingo_game %>% 
  summarise(nrow = length(unique(row_no)),
            ncol = length(unique(col_no))) %>% 
  filter(nrow * ncol == 5 * 5) %>% 
  pull(board_no)

bingo_game %>% 
  ungroup() %>% 
  filter(number != final_number,
         board_no == losing_board) %>% 
  summarise(sum_unmarked = sum(number),
            final_number = unique(final_number)) %>% 
  as_vector() %>% 
  prod() %>% 
  print()

[1] 23541

3.565 sec elapsed

Day 5

Part 1

library(tidyverse)
library(showtext)

input <- read_csv(here::here("2021", "day_05","input_day05.txt"), 
                  col_names = FALSE) %>% 
  rename(x1 = X1,
         y2 = X3) %>% 
  separate(X2, into = c("y1","x2")) %>% 
  mutate(across(everything(), ~ as.numeric(.x)))

font_add_google("Josefin Sans", family = "google")
showtext_auto()

input %>% 
  filter(x1 == x2 | y1 == y2) %>% 
  rowid_to_column("segment_no") %>% 
  mutate(x_steps = map2(x1, x2, .f = seq),
         y_steps = map2(y1, y2, .f = seq)) %>% 
  select(segment_no, x_steps, y_steps) %>% 
  unnest(c(x_steps, y_steps)) %>% 
  count(x_steps, y_steps) %>% 
  count(overlapping_segments = n > 1) %>% 
  filter(overlapping_segments) %>% 
  pull(n) %>% 
  print()

input %>% 
  filter(x1 == x2 | y1 == y2) %>% 
  rowid_to_column("segment_no") %>% 
  ggplot() +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2, color = segment_no),
               size = 1.5, alpha = 0.6, lineend = "round") + 
  labs(title = "**Advent of Code - Day 5**",
       color = "**Hydrothermal vent n<sup>o</sup>**") +
  scico::scale_color_scico(palette = "batlow", breaks = c(1,seq(100,nrow(input), 100)), 
                           guide = guide_colorbar(title.position = "top", title.hjust = 0.5, 
                                                  barwidth = 10, barheight = 0.75, ticks = FALSE)) +
  scale_y_reverse() +
  theme_void(base_size = 24) + 
  theme(text = element_text(family = "google", color = "grey90"),
        plot.title = ggtext::element_markdown(hjust = 0.5, size = 80,
                                              margin = margin(rep(5,4), unit = "pt")),
        legend.title = ggtext::element_markdown(margin = margin(b = -10, unit = "pt")),
        legend.text = element_text(margin = margin(t = -10, "pt")),
        legend.position = "bottom",
        plot.margin = margin(rep(10,4), unit = "mm"),
        plot.background = element_rect(fill = "#0f0f23"))

ggsave(here::here("2021", "day_05", "plot_day05.png"), last_plot(), 
       height = 6, width = 8, dpi = 300)

[1] 6572

4.124 sec elapsed

plot_day05

Part 2

library(tidyverse)
library(showtext)

input <- read_csv(here::here("2021", "day_05","input_day05.txt"), 
                  col_names = FALSE) %>% 
  rename(x1 = X1,
         y2 = X3) %>% 
  separate(X2, into = c("y1","x2")) %>% 
  mutate(across(everything(), ~ as.numeric(.x)))

font_add_google("Josefin Sans", family = "google")
showtext_auto()

input %>% 
  rowid_to_column("segment_no") %>% 
  mutate(x_steps = map2(x1, x2, .f = seq),
         y_steps = map2(y1, y2, .f = seq)) %>% 
  select(segment_no, x_steps, y_steps) %>% 
  unnest(c(x_steps, y_steps)) %>% 
  count(x_steps, y_steps) %>% 
  count(overlapping_segments = n > 1) %>% 
  filter(overlapping_segments) %>% 
  pull(n) %>% 
  print()

[1] 21466

2.036 sec elapsed

Day 6

Part 1

library(tidyverse)

input <- read_csv(here::here("2021", "day_06","input_day06.txt"), 
                  col_names = FALSE) %>% 
  unname() %>% 
  as_vector()

n_days <- 80

gen_fish <- function(x, day) {
  
  n_new <- sum(x == 0)
  new_fish <- rep(8,n_new)
  
  old_fish_repl <- replace(x - 1, x - 1 < 0, 6)
  
  new_fish_vector = c(old_fish_repl, new_fish)
  
  n_fish <- length(new_fish_vector)
  
  #print(str_glue("Day {day}: {n_fish} fish"))
  
  return(new_fish_vector)
  
}

final_fish_vector <- reduce(seq(n_days), ~ gen_fish(.x, .y), .init = input)

print(length(final_fish_vector))

[1] 386640

0.365 sec elapsed

Part 2

library(tidyverse)

input <- read_csv(here::here("2021", "day_06","input_day06.txt"), 
                  col_names = FALSE) %>% 
  unname() %>% 
  as_vector()

data <- tabulate(input, nbins = 8) %>% 
  c(0, .) %>% 
  as_tibble() %>% 
  rename(n = value) %>% 
  mutate(value = row_number() - 1) %>% 
  relocate(value, .before = 1)

get_fish_count <- function(x) {
  
  data_out <- x %>% 
    select(-n) %>% 
    full_join(x %>% mutate(value = value - 1), by = "value") %>% 
    mutate(n = replace_na(n, 0))
  
  data_out$n[data_out$value == 6] <- data_out %>% 
    filter(value %in% c(-1,6)) %>% 
    pull(n) %>% 
    sum()
  
  data_out$n[data_out$value == 8] <- data_out %>%
    filter(value == -1) %>% 
    pull(n)
  
  data_out <- data_out %>% filter(value >= 0)
  
  return(data_out)
}

final_fish_count <- reduce(seq(256), ~ get_fish_count(.x), .init = data)

options(scipen = 999)
final_fish_count %>% 
  summarise(total_fish = sum(n)) %>% 
  pull(total_fish) %>% 
  print()

[1] 1733403626279

3.29 sec elapsed

Day 7

Part 1

library(tidyverse)

input <- read_csv(here::here("2021","day_07","input_day07.txt"), 
                  col_names = FALSE) %>% 
  unname() %>% 
  as_vector()

calc_fuel <- function(x, pos) {
  fuel <- tibble(
    position = pos,
    consumption = sum(abs(input - pos))
  ) 
  return(fuel)
}

fuel <- map_dfr(seq(0, max(input)), ~ calc_fuel(input, .x))

fuel %>% 
  slice_min(consumption) %>% 
  pull(consumption) %>% 
  print()

[1] 328187

1.641 sec elapsed

Part 2

library(tidyverse)

input <- read_csv(here::here("2021","day_07","input_day07.txt"), 
                  col_names = FALSE) %>% 
  unname() %>% 
  as_vector()

calc_fuel <- function(x, pos) {
  
  consumption <-  abs(input - pos) %>% 
    map_dbl(., ~ sum(seq(.x)))
  
  fuel <- tibble(
    position = pos,
    consumption = sum(consumption)
  ) 
  return(fuel)
}

fuel <- map_dfr(seq(0, max(input)), ~ calc_fuel(input, .x))

fuel %>% 
  slice_min(consumption) %>% 
  pull(consumption) %>% 
  print()

[1] 91257582

16.047 sec elapsed

Day 8

Part 1

library(tidyverse)

input <- read_delim(here::here("2021","day_08","input_day08.txt"), delim = "|", 
                    col_names = FALSE)

numbers <- c("1" = 2, "4" = 4, "7" = 3, "8" = 7)

input %>% 
  select(output = X2) %>% 
  mutate(output = str_trim(output)) %>% 
  separate(output, into = str_glue("digit{seq(4)}")) %>% 
  mutate(across(everything(), ~ nchar(.x))) %>% 
  pivot_longer(cols = everything(), names_to = "digit", 
               values_to = "n_segments") %>% 
  filter(n_segments %in% numbers) %>% 
  nrow() %>% 
  print()

[1] 294

0.055 sec elapsed

Part 2

Got a lot of help from Emil Hvitveldt on this one. Reorganized his solution so I could make sense of it. This one hurt me 🤕

library(tidyverse)

input <- read_delim(here::here("2021","day_08","input_day08.txt"), delim = "|", 
                    col_names = FALSE)

split_segments <- function(x) {
  x %>% 
    str_split(., " ") %>%
    map(., ~ str_split(.x, pattern = "")) %>%
    map(., ~ map(.x, ~ sort(.x)))
}

data <- input %>%
  rename(input = X1,
         output = X2) %>% 
  mutate(across(everything(), ~ str_trim(.x))) %>% 
  mutate(across(c("input", "output"), split_segments))

solver <- function(input, output) {
  
  setdiff_length <- function(x, y) {
    lengths(map(x, ~setdiff(x[[which(y)]], .x)))
  }
  
  x1 <- lengths(input) == 2
  x4 <- lengths(input) == 4
  x7 <- lengths(input) == 3
  x8 <- lengths(input) == 7
  x6 <- lengths(input) == 6 & setdiff_length(input, x1) == 1
  x0 <- lengths(input) == 6 & setdiff_length(input, x4) == 1 & !x6
  x9 <- lengths(input) == 6 & !x6 & !x0
  x5 <- lengths(input) == 5 & setdiff_length(input, x6) == 1
  x3 <- lengths(input) == 5 & setdiff_length(input, x9) == 1 & !x5
  x2 <- lengths(input) == 5 & !x5 & !x3
  
  cont <- list(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9) %>%
    map(~ input[[which(.x)]]) %>%
    map(sort)
  
  output %>%
    match(cont) %>%
    magrittr::subtract(., 1) %>%
    str_c(collapse = "") %>%
    as.numeric()
}

data %>%
  mutate(res = map2_dbl(input, output, ~ solver(.x, .y))) %>%
  summarise(sum = sum(res)) %>% 
  pull(sum) %>% 
  print()

[1] 973292

0.419 sec elapsed

Day 9

Part 1

library(tidyverse)

input <- read_table(here::here("2021","day_09","input_day09.txt"), 
                    col_names = FALSE) %>%
  rename(value = X1)

full_table <- input %>% 
  separate(value, into = str_glue("coord{seq(0,10)}"), sep = "") %>% 
  mutate(across(everything(), ~ as.numeric(.x))) %>% 
  rowid_to_column("row_no") %>% 
  add_row(tibble(row_no = c(0, nrow(.) + 1))) %>% 
  mutate(across(everything(), ~ replace_na(.x, Inf)),
         coord_last = Inf) %>% 
  arrange(row_no)

row_wise_table <- full_table %>% 
  pivot_longer(starts_with("coord"), names_to = "column_no") %>% 
  group_by(column_no) %>% 
  mutate(lag_value = lag(value),
         lead_value = lead(value),
         low_point_row = ifelse(value < lag_value & value < lead_value, 
                                yes = TRUE, no = FALSE)) %>%
  select(row_no, column_no, value, low_point_row)

col_wise_table <- full_table %>% 
  pivot_longer(starts_with("coord"), names_to = "column_no") %>% 
  group_by(row_no) %>% 
  mutate(lag_value = lag(value),
         lead_value = lead(value),
         low_point_col = ifelse(value < lag_value & value < lead_value, 
                                yes = TRUE, no = FALSE)) %>% 
  select(row_no, column_no, value, low_point_col)


inner_join(col_wise_table, row_wise_table) %>%
  filter(low_point_col, low_point_row) %>% 
  ungroup() %>% 
  mutate(value = value + 1) %>% 
  summarise(sum = sum(value)) %>% 
  pull(sum) %>% 
  print()

[1] 128

0.121 sec elapsed

Part 2

Got stuck here, and once I found this amazingly clever solution from Ildikó Czeller using {igraph}. I wanted to know how this worked, and then I learned from Jarosław Nirski about a similar solution using {tidygraph}, which is very neat:

library(tidyverse)

input <- read_table(here::here("2021","day_09","input_day09.txt"), 
                    col_names = FALSE) %>%
  rename(value = X1)

data_long <- input %>% 
  rowid_to_column("row_no") %>% 
  separate_rows(value, sep = "", convert = TRUE) %>% 
  filter(!is.na(value)) %>% 
  group_by(row_no) %>% 
  mutate(col_no = row_number()) %>% 
  ungroup() %>% 
  select(value, row_no, col_no)

tidygraph::create_lattice(c(100, 100)) %>%
  mutate(!!!data_long) %>%
  filter(value != 9) %>%
  mutate(group = tidygraph::group_components()) %>%
  as_tibble() %>%
  count(group, sort = TRUE) %>%
  head(3) %>%
  pull(n) %>%
  prod()

0.436 sec elapsed

Day 10

Part 1

library(tidyverse)

input <- read_table(here::here("2021","day_10","input_day10.txt"), 
                    col_names = "syntax")

full_patterns <- c("\\(\\)","\\[\\]","\\{\\}","\\<\\>")

symbols <- tibble(symbols = c("(","[","{","<",")","]","}",">"),
                  valid = c(rep(TRUE, 4), rep(FALSE, 4)))

points <- c(")" = 3, "]" = 57, "}" = 1197, ">" = 25137) %>% 
  enframe()

input %>% 
  mutate(nchar = max(nchar(syntax))) %>% 
  mutate(syntax = reduce(seq(nchar), .init = syntax,
                         ~ str_remove_all(.x, str_c(full_patterns, 
                                                    collapse = "|")))) %>% 
  mutate(symbols = str_split(syntax, "")) %>% 
  unnest(symbols) %>%
  group_by(syntax) %>% 
  mutate(position = row_number()) %>% 
  left_join(symbols) %>% 
  filter(!valid) %>% 
  slice_min(position) %>% 
  ungroup() %>% 
  left_join(points, by = c("symbols" = "name")) %>% 
  summarise(total = sum(value)) %>% 
  pull(total) %>% 
  print()

[1] 321237

0.134 sec elapsed

Part 2

library(tidyverse)

input <- read_table(here::here("2021","day_10","input_day10.txt"), 
                    col_names = "syntax")

full_patterns <- c("\\(\\)","\\[\\]","\\{\\}","\\<\\>")

symbols <- tibble(symbols = c("(","[","{","<",")","]","}",">"),
                  valid = c(rep(TRUE, 4), rep(FALSE, 4)))

points <- c(")" = 1, "]" = 2, "}" = 3, ">" = 4) %>% 
  enframe()

full_patterns <- c("\\(\\)","\\[\\]","\\{\\}","\\<\\>")

pairs <- tibble(open = c("(","[","{","<"),
                close = c(")","]","}",">"))

calc_score <- function(scores) {
  
  tot_score <- reduce(seq_along(scores), ~ .x * 5 + scores[.y], .init = 0)
  return(tot_score)
  
}

input %>% 
  mutate(nchar = max(nchar(syntax))) %>% 
  mutate(syntax = reduce(seq(nchar), .init = syntax,
                         ~ str_remove_all(.x, str_c(full_patterns, 
                                                    collapse = "|")))) %>% 
  mutate(symbols = str_split(syntax, "")) %>% 
  unnest(symbols) %>%
  group_by(syntax) %>% 
  left_join(symbols) %>% 
  filter(all(valid)) %>% 
  mutate(inv_symbols = rev(symbols)) %>% 
  left_join(pairs, by = c("inv_symbols" = "open")) %>% 
  mutate(close_syntax = str_c(close, collapse = "")) %>% 
  left_join(points, by = c("close" = "name")) %>% 
  select(syntax,value) %>% 
  nest(scores = value) %>% 
  mutate(scores = map(scores, ~ as_vector(.x)),
         tot_score = map_dbl(scores, ~ calc_score(.x))) %>% 
  ungroup() %>% 
  summarise(median_score = median(tot_score)) %>% 
  pull(median_score) %>% 
  print()

[1] 2360030859

0.316 sec elapsed

Day 11

Nope, not a chance…

Day 12

No idea, sorry!

Day 13

Part 1

library(tidyverse)

input_paper <- read_csv(here::here("2021","day_13","input_day13.txt"), 
                        col_names = c("x","y"), n_max = 1004)
input_folds <- read_delim(here::here("2021","day_13","input_day13.txt"), 
                          delim = "=", 
                          col_names = c("fold_direction", "fold_loc"), 
                          skip = 1005) %>% 
  mutate(fold_direction = str_extract(fold_direction, "[x|y]"))

fold_paper <- function(df, instructions, step = 1) {
  
  dir <- instructions %>% 
    slice(step) %>% 
    pull(fold_direction)
  
  loc <- instructions %>% 
    slice(step) %>% 
    pull(fold_loc)
  
  folded_paper <- df %>% 
    mutate(x = if_else(x <= loc | dir == "y", x, 2 * loc - x),
           y = if_else(y <= loc | dir == "x", y, 2 * loc - y)) %>% 
    distinct(x, y)
  
  return(folded_paper)
  
}

fold_paper(input_paper, input_folds) %>% 
  count() %>% 
  pull(n) %>% 
  print()

[1] 847

0.13 sec elapsed

Part 2

library(tidyverse)

input_paper <- read_csv(here::here("2021","day_13","input_day13.txt"), 
                        col_names = c("x","y"), n_max = 1004)
input_folds <- read_delim(here::here("2021","day_13","input_day13.txt"), 
                          delim = "=", 
                          col_names = c("fold_direction", "fold_loc"), 
                          skip = 1005) %>% 
  mutate(fold_direction = str_extract(fold_direction, "[x|y]"))

fold_paper <- function(df, instructions, step = 1) {
  
  dir <- instructions %>% 
    slice(step) %>% 
    pull(fold_direction)
  
  loc <- instructions %>% 
    slice(step) %>% 
    pull(fold_loc)
  
  folded_paper <- df %>% 
    mutate(x = if_else(x <= loc | dir == "y", x, 2 * loc - x),
           y = if_else(y <= loc | dir == "x", y, 2 * loc - y)) %>% 
    distinct(x, y)
  
  return(folded_paper)
  
}

reduce(seq(nrow(input_folds)), ~ fold_paper(.x, input_folds, step = .y), 
       .init = input_paper) %>% 
  mutate(dot = "#") %>% 
  ggplot(aes(x = x, y = y)) +
  geom_tile(fill = "#f2f2f2") +
  geom_text(aes(label = dot), color = "#b3b3b3", size = 8) +
  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("2021", "day_13", "plot_day13_pt2.png"), last_plot(), 
       height = 3, width = 8, dpi = 300)

0.322 sec elapsed

plot_day13

Day 14

Part 1

With some help once again from Ildikó Czeller 🙌!

library(tidyverse)

input_sequence <- read_lines(here::here("2021","day_14","input_day14.txt"), 
                             n_max = 1)
input_rules <- read_delim(here::here("2021","day_14","input_day14.txt"), 
                          delim = "->",  skip = 2, 
                          col_names = c("pair","insert")) %>% 
  mutate(across(everything(), ~ str_trim(.x)))

rules <- input_rules %>% 
  mutate(pair_chars = str_split(pair, "")) %>% 
  unnest_wider(pair_chars, names_sep = "") %>% 
  mutate(new_pair1 = str_glue("{pair_chars1}{insert}"), 
         new_pair2 = str_glue("{insert}{pair_chars2}")) %>% 
  rowwise() %>% 
  mutate(new_pair = list(c(new_pair1, new_pair2))) %>% 
  select(pair, new_pair) %>% 
  ungroup()

pairs_from_string <- function(string) {
  ind_chars <- str_split(string, "") %>% 
    unlist()
  pairs <- head(str_glue("{ind_chars}{lead(ind_chars)}"), -1)
  return(pairs)
}

initial_pair_frequencies <- input_sequence %>% 
  pairs_from_string() %>% 
  as_tibble() %>% 
  rename(pair = value) %>% 
  count(pair, name = "freq")

apply_rules <- function(df) {
  
  out <- df %>% 
    inner_join(rules, by = "pair") %>% 
    unnest_longer(new_pair) %>% 
    group_by(new_pair) %>% 
    summarize(freq = sum(freq)) %>% 
    rename(pair = new_pair)
  return(out)
  
}

reduce(seq(10), ~ apply_rules(.x), .init = initial_pair_frequencies) %>% 
  separate_rows(pair, sep = "") %>% 
  filter(pair != "") %>% 
  group_by(pair) %>% 
  summarise(freq = ceiling(sum(freq) / 2)) %>% 
  ungroup() %>% 
  summarise(diff = max(freq) - min(freq)) %>% 
  pull(diff) %>% 
  print()

[1] 2068

0.259 sec elapsed

Part 2

library(tidyverse)

input_sequence <- read_lines(here::here("2021","day_14","input_day14.txt"), 
                             n_max = 1)
input_rules <- read_delim(here::here("2021","day_14","input_day14.txt"), 
                          delim = "->",  skip = 2, 
                          col_names = c("pair","insert")) %>% 
  mutate(across(everything(), ~ str_trim(.x)))

rules <- input_rules %>% 
  mutate(pair_chars = str_split(pair, "")) %>% 
  unnest_wider(pair_chars, names_sep = "") %>% 
  mutate(new_pair1 = str_glue("{pair_chars1}{insert}"), 
         new_pair2 = str_glue("{insert}{pair_chars2}")) %>% 
  rowwise() %>% 
  mutate(new_pair = list(c(new_pair1, new_pair2))) %>% 
  select(pair, new_pair) %>% 
  ungroup()

pairs_from_string <- function(string) {
  ind_chars <- str_split(string, "") %>% 
    unlist()
  pairs <- head(str_glue("{ind_chars}{lead(ind_chars)}"), -1)
  return(pairs)
}

initial_pair_frequencies <- input_sequence %>% 
  pairs_from_string() %>% 
  as_tibble() %>% 
  rename(pair = value) %>% 
  count(pair, name = "freq")

apply_rules <- function(df) {
  
  out <- df %>% 
    inner_join(rules, by = "pair") %>% 
    unnest_longer(new_pair) %>% 
    group_by(new_pair) %>% 
    summarize(freq = sum(freq)) %>% 
    rename(pair = new_pair)
  return(out)
  
}

options(scipen = 999)

reduce(seq(40), ~ apply_rules(.x), .init = initial_pair_frequencies) %>% 
  separate_rows(pair, sep = "") %>% 
  filter(pair != "") %>% 
  group_by(pair) %>% 
  summarise(freq = ceiling(sum(freq) / 2)) %>% 
  ungroup() %>% 
  summarise(diff = max(freq) - min(freq)) %>% 
  pull(diff) %>% 
  print()

[1] 2158894777814

0.873 sec elapsed

Day 15

Sorry! Guess I need to dive into {tidygraph} a bit more.

Day 16

Nope, no idea!

Day 17

Part 1

library(tidyverse)

input <- read_lines(here::here("2021","day_17","input_day17.txt")) %>% 
  str_extract_all("-?[0-9]+", simplify = TRUE) %>% 
  as.numeric() %>% 
  tibble(value = .) %>% 
  mutate(pos = c('xmin', 'xmax', 'ymin', 'ymax')) %>% 
  pivot_wider(names_from = pos, values_from = value)

input %>% 
  mutate(ylim = (abs(ymin) - 1) * abs(ymin) / 2) %>% 
  pull(ylim) %>% 
  print()

[1] 5460

0.025 sec elapsed

Part 2

I really admire Ildikó Czeller’s neat and tidy solution here, which (with some trial-and-error) worked beautifully!

library(tidyverse)

input <- read_lines(here::here("2021","day_17","input_day17.txt")) %>% 
  str_extract_all("-?[0-9]+", simplify = TRUE) %>% 
  as.numeric() %>% 
  tibble(value = .) %>% 
  mutate(pos = c('xmin', 'xmax', 'ymin', 'ymax')) %>% 
  pivot_wider(names_from = pos, values_from = value)

positions <- crossing(vx = seq(0, input[["xmax"]]), 
                      vy = seq(input[["ymin"]], 250), 
                      s = seq(0, 500)) %>%
  mutate(dx = pmax(vx - s, 0),
         dy = vy - s) %>% 
  group_by(vx, vy) %>% 
  mutate(x = cumsum(dx),
         y = cumsum(dy)) %>% 
  ungroup()

positions %>%
  filter(x >= input[["xmin"]] & x <= input[["xmax"]] & 
           y >= input[["ymin"]] & y <= input[["ymax"]]) %>%
  distinct(vx, vy) %>% 
  count() %>% 
  pull(n) %>% 
  print()

[1] 3618

10.358 sec elapsed

Day 18

Yeah, I’m getting out of my depth here.

Day 19

Nope, sorry!

Day 20

Cool puzzle, but I’m not sure how to solve it, and I don’t want to use MATLAB either.

Day 21

Part 1

library(tidyverse)

input <- read_delim(here::here("2021","day_21","input_day21.txt"), delim = ":", 
                    col_names = c("player","start_pos")) %>% 
  mutate(across(everything(), ~ parse_number(.x))) %>% 
  pull(start_pos)

scores <- c(0, 0)
die_roll <- 0

die_value <- function(die_roll) {
  value <- if_else(die_roll %% 100 == 0, 100, die_roll %% 100)
  return(value)
}

place_value <- function(place_idx) {
  value <- if_else(place_idx %% 10 == 0, 10, place_idx %% 10)
  return(value)
}

roll_for_player <- function(player) {
  die_sum <- sum(die_value(die_roll + seq(3)))
  die_roll <<- die_roll + 3
  scores[player] <<- scores[player] + place_value(input[player] + die_sum)
  input[player] <<- place_value(input[player] + die_sum)
}

while (max(scores) < 1000) {
  roll_for_player(1)
  if (max(scores) < 1000) {
    roll_for_player(2)
  }
}

print(min(scores) * die_roll)

[1] 1002474

0.625 sec elapsed

Part 2

Sorry, part 1 was I could manage here.

Day 22

No idea!

Day 23

Definitely too complicated for me at this stage.

Day 24

Sorry, definitely too complex for me.

Day 25

Merry Christmas! 🎄 No solution from me today either, both because of time and because of complexity. This was fun though, I’m looking forward to next year!