Day 4

Data

Each line contains a pair of sections which two elves should clean.

(sections_list <- read_table(
  here("DATA", "2022", "input_day_04.txt"),
  col_names = "section_pairs"
))

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

# A tibble: 1,000 × 1
   section_pairs
   <chr>        
 1 22-65,22-66  
 2 91-94,63-91  
 3 6-88,5-31    
 4 85-86,64-86  
 5 40-65,40-65  
 6 25-82,24-94  
 7 68-69,68-92  
 8 60-91,89-92  
 9 3-72,3-73    
10 44-85,84-85  
# ℹ 990 more rows

Cleaning the data a bit.

(
  sections_list <- sections_list %>%
   #extract sections for elves
   separate(col = section_pairs, into = c("elf1", "elf2"), sep = ",") %>%
   # extract minimums and maximums for each elf
   separate(col = elf1, into = c("elf1_min", "elf1_max"), sep = "-") %>%
   separate(col = elf2, into = c("elf2_min", "elf2_max"), sep = "-") %>%
   mutate(across(.cols = everything(), .fns = as.numeric))
)

# A tibble: 1,000 × 4
   elf1_min elf1_max elf2_min elf2_max
      <dbl>    <dbl>    <dbl>    <dbl>
 1       22       65       22       66
 2       91       94       63       91
 3        6       88        5       31
 4       85       86       64       86
 5       40       65       40       65
 6       25       82       24       94
 7       68       69       68       92
 8       60       91       89       92
 9        3       72        3       73
10       44       85       84       85
# ℹ 990 more rows

Overlapping

Find entirely overlapping sections.

find_overlap <- function(min1, min2, max1, max2){
  # check whether the second set of numbers is contained in the first
  return(
    (min2 >= min1) & (max2 <= max1)
  )
}

(sections_list_entire_overlap <- sections_list %>%
  rowwise() %>%
  mutate(elf2_overlap = find_overlap(elf1_min, elf2_min, elf1_max, elf2_max)) %>%
  mutate(elf1_overlap = find_overlap(elf2_min, elf1_min, elf2_max, elf1_max)))

# A tibble: 1,000 × 6
# Rowwise: 
   elf1_min elf1_max elf2_min elf2_max elf2_overlap elf1_overlap
      <dbl>    <dbl>    <dbl>    <dbl> <lgl>        <lgl>       
 1       22       65       22       66 FALSE        TRUE        
 2       91       94       63       91 FALSE        FALSE       
 3        6       88        5       31 FALSE        FALSE       
 4       85       86       64       86 FALSE        TRUE        
 5       40       65       40       65 TRUE         TRUE        
 6       25       82       24       94 FALSE        TRUE        
 7       68       69       68       92 FALSE        TRUE        
 8       60       91       89       92 FALSE        FALSE       
 9        3       72        3       73 FALSE        TRUE        
10       44       85       84       85 TRUE         FALSE       
# ℹ 990 more rows

Find the total.

tot_overlap <- sections_list_entire_overlap %>%
  ungroup() %>%
  mutate(any_overlap = elf2_overlap | elf1_overlap) %>%
  summarise(sum_overlap = sum(any_overlap)) %>%
  pull()

The total number of entirely overlapping regions is 584! 🥳

Find overlapping at all

find_any_overlap <- function(min1, min2, max1, max2){
  # check whether the second set of numbers is contained in the first
  return(
    ((min2 >= min1) & (min2 <= max1)) |
    ((max2 <= max1) & (max2 >= min1))
  )
}

(sections_list_any_overlap <- sections_list %>%
  rowwise() %>%
  mutate(elf2_overlap = find_any_overlap(elf1_min, elf2_min, elf1_max, elf2_max)) %>%
  mutate(elf1_overlap = find_any_overlap(elf2_min, elf1_min, elf2_max, elf1_max)))

# A tibble: 1,000 × 6
# Rowwise: 
   elf1_min elf1_max elf2_min elf2_max elf2_overlap elf1_overlap
      <dbl>    <dbl>    <dbl>    <dbl> <lgl>        <lgl>       
 1       22       65       22       66 TRUE         TRUE        
 2       91       94       63       91 TRUE         TRUE        
 3        6       88        5       31 TRUE         TRUE        
 4       85       86       64       86 TRUE         TRUE        
 5       40       65       40       65 TRUE         TRUE        
 6       25       82       24       94 FALSE        TRUE        
 7       68       69       68       92 TRUE         TRUE        
 8       60       91       89       92 TRUE         TRUE        
 9        3       72        3       73 TRUE         TRUE        
10       44       85       84       85 TRUE         TRUE        
# ℹ 990 more rows

Find total

tot_any_overlap <- sections_list_any_overlap %>%
  ungroup() %>%
  mutate(any_overlap = elf2_overlap | elf1_overlap) %>%
  summarise(sum_overlap = sum(any_overlap)) %>%
  pull()

The total number of at tall overlapping regions is 933! 🥳