Day 3

Author

Julia Romanowska

Data

Each line is a list of items in one rucksack.

(rucksack_data <- read_table(
  here("DATA", "2022", "input_day_03.txt"),
  col_names = FALSE
))


── Column specification ────────────────────────────────────────────────────────
cols(
  X1 = col_character()
)

# A tibble: 300 × 1
   X1                                            
   <chr>                                         
 1 DMwrszrfMzSSCpLpfCCn                          
 2 RMvhZhQqlvhMvRtbvbcPclPlncddppLTdppd          
 3 tVMQhFtjjWmsFJsmsW                            
 4 trRtvNhfJhSzzSTFVhQQZQhHGphP                  
 5 CnLMBWLwDMgMcwwdngdHGPVTQGpTHZdGPGpd          
 6 LLDqcDgwqCMnLWqtvzrzbbtJqPjJ                  
 7 wQQwHNQLmbWQbQRHwHNFBbwqPfjqlzRMGRqzpSfvPlzplM
 8 nCtGCZZtsGsrtDMZpfMpSlMlvlZq                  
 9 cJctJCgVJsCJnDTsCthGhGLwBWBbbQmLbgQLQQdWbbbQ  
10 ZWnNlTNTnhhQQlDNdmmpwrrrqBwjwjZd              
# ℹ 290 more rows

Find the repeating item

First, we need to find the middle of each string and divide.

rucksack_divided <- map(
  rucksack_data$X1,
  function(item_list){
    split_at <- str_length(item_list)/2
    out_strings <- c(
      str_sub(item_list, start = 1, end = split_at),
      str_sub(item_list, start = split_at + 1, end = -1)
    )
    return(out_strings)
  }
)
rucksack_divided[[1]]

[1] "DMwrszrfMz" "SSCpLpfCCn"

Next, compare the strings in each compartment to find the repeating item for each rucksack.

repeating_items <- map(
  rucksack_divided,
  function(compartments){
    unique_contents <- map(
      compartments,
      function(item_list){
        strsplit(item_list, split = "") %>%
          unlist() %>%
          unique()
      }
    )
    return(Reduce(f = intersect, x = unique_contents))
  }
)
repeating_items[[1]]

[1] "f"

Checking - do I get only one item per rucksack?

lengths(repeating_items) %>%
  unique()

[1] 1

✔️ Yes, great!

Priority of items

Lowercase letters have priorities from 1 through 26, uppercase letters - from 27 through 52.

priority_low <- str_locate(
  paste0(letters, collapse = ""), letters
)[, "start"]
priority_low <- tibble(
  letter = letters,
  prior = priority_low
)
priority_low

# A tibble: 26 × 2
   letter prior
   <chr>  <int>
 1 a          1
 2 b          2
 3 c          3
 4 d          4
 5 e          5
 6 f          6
 7 g          7
 8 h          8
 9 i          9
10 j         10
# ℹ 16 more rows

priority_up <- str_locate(
  paste0(LETTERS, collapse = ""), LETTERS
)[, "start"] + 26
priority_up <- tibble(
  letter = LETTERS,
  prior = priority_up
)
priority_up

# A tibble: 26 × 2
   letter prior
   <chr>  <dbl>
 1 A         27
 2 B         28
 3 C         29
 4 D         30
 5 E         31
 6 F         32
 7 G         33
 8 H         34
 9 I         35
10 J         36
# ℹ 16 more rows

priority_all <- bind_rows(
  priority_low, priority_up
)

Now, find the sum of the priorities of repeated items.

total_priority <- tibble(
  repeat_item = unlist(repeating_items)
  ) %>%
  left_join(
    priority_all,
    by = c("repeat_item" = "letter")
  ) %>%
  summarise(sum_priority = sum(prior))

The total priority is 7811! 🥳

Group badges

Each three elves create a group and they are recognized by the item that they carry. This item is the only item that is shared by these elves.

# there are 3 members in each group
no_members <- 3
(rucksack_groups <- rucksack_data %>%
  add_column(group_no = 
               rep(
                 seq(1:(nrow(rucksack_data)/no_members)),
                 each = no_members
                )
             ))

# A tibble: 300 × 2
   X1                                             group_no
   <chr>                                             <int>
 1 DMwrszrfMzSSCpLpfCCn                                  1
 2 RMvhZhQqlvhMvRtbvbcPclPlncddppLTdppd                  1
 3 tVMQhFtjjWmsFJsmsW                                    1
 4 trRtvNhfJhSzzSTFVhQQZQhHGphP                          2
 5 CnLMBWLwDMgMcwwdngdHGPVTQGpTHZdGPGpd                  2
 6 LLDqcDgwqCMnLWqtvzrzbbtJqPjJ                          2
 7 wQQwHNQLmbWQbQRHwHNFBbwqPfjqlzRMGRqzpSfvPlzplM        3
 8 nCtGCZZtsGsrtDMZpfMpSlMlvlZq                          3
 9 cJctJCgVJsCJnDTsCthGhGLwBWBbbQmLbgQLQQdWbbbQ          3
10 ZWnNlTNTnhhQQlDNdmmpwrrrqBwjwjZd                      4
# ℹ 290 more rows

find_common_item <- function(items_list){
  unique_items <- map(items_list, function(items){
    str_split(items, pattern = "") %>%
      unlist() %>%
      unique()
  })
  return(
    Reduce(intersect, unique_items)
  )
}

(rucksack_groups <- rucksack_groups %>%
  group_by(group_no) %>%
  summarise(
    common_item = find_common_item(X1)
  ) %>%
  left_join(priority_all, by = c("common_item" = "letter")))

# A tibble: 100 × 3
   group_no common_item prior
      <int> <chr>       <dbl>
 1        1 M              39
 2        2 P              42
 3        3 G              33
 4        4 l              12
 5        5 s              19
 6        6 t              20
 7        7 Z              52
 8        8 n              14
 9        9 p              16
10       10 z              26
# ℹ 90 more rows

sum_prior <- rucksack_groups %>%
  summarise(sum_prior = sum(prior)) %>%
  pull()

The total priority is 2639! 🥳