7 Tricks I Learned During Advent of Code 2020

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I got into the Advent of Code through some co-workers for the first time this year. For those not familiar, its a series of programming puzzles created by Eric Wastl released once a day for the first 25 days of December. The puzzles are programming language agnostic so some use it to learn new a language and others, like myself, just thought it would be something fun to do. While I use R often in my job and for writing this blog, the Advent of Code puzzles are quite different my usual use case. As I did the puzzles, I kept track of some tricks that I learned that I thought were useful (I learned a lot of things but to keep things short, I’ll only list a couple).

Not A Trick.. But Credit Where Credit Is Due

I can’t imagine the amount of work that goes into creating these puzzles.

A bit a cop-out that the first item has nothing to do with R. But I did want to specifically give props to Eric Wastl for making these puzzles. As hard as it was at times to complete the puzzles, I found myself constantly thinking how difficult it must be to create them and ensure that they are solvable.

Now onto the R.

Trick #1: Break apart a string of text into a vector with str_split() and unlist()

The inputs for Advent of Code are usually flat files and its often necessary to break up the input in order to fill out a matrix or columns in a data frame.

Suppose there is an input like:


and we want to have each character as a vector element . A function like readLines will input each row as a vector, but in order to split the string into each element we’ll call upon str_split() to break apart the string by a delimiter. Using the empty string (’’) will separate each character to create a list. Then unlist() will break each character into its own element in the vector

input <- "....#..."

print(str_split(input, '') %>% unlist())
## [1] "." "." "." "." "#" "." "." "."

Now as opposed to having 1 string, we have a character vector with each character as its own element.

Trick #2: Combining str_split() with unnest() can turn a vector of strings into a tidy data frame.

One thing that I worked with more in Advent of Code than I have in the last few years have been matrices. As shown before, most of the input comes as a flat file needing to be processed. Sometimes it was helpful to represent the matrix as a tidy data-set with columns for row_id, col_id, and value vs. the traditional matrix format. The unnest() function will break apart each element of a list into its own row. Using a using a similar input to before but with more rows.

input <- c("....#.......",

tibble(raw = input) %>% 
    row_id = row_number(), #Create Row ID
    value = str_split(raw, '') #Break Each Row Into A List Of Elements
  ) %>% 
  unnest(value) %>% #Break Each Element Into Its Own Row
  group_by(row_id) %>% 
  mutate(col_id = row_number()) %>% #Create Column ID
  head(10) %>% 
  kable(align = 'c')
raw row_id value col_id
….#……. 1 . 1
….#……. 1 . 2
….#……. 1 . 3
….#……. 1 . 4
….#……. 1 # 5
….#……. 1 . 6
….#……. 1 . 7
….#……. 1 . 8
….#……. 1 . 9
….#……. 1 . 10

Now each element of the character vector is its own row its with own row_id and col_id.

Trick #3: extract() is a powerhouse function for working with strings

I’ve mentioned before that I think regular expressions are amazing and opens up a world of possibilities. extract() allows for the use to regular expressions and capture groups to create any number of new columns. Its similar to separate() but to me seems more customizable. Given the inputs:

6-7 z: dqzzzjbzz 67
13-16 j: jjjvjmjjkjjjjjjj 123
5-6 m: mmbmmlvmbmmgmmf 5

And you wanted to create a data.frame that had columns for the number range, the character before the ‘:’, the series of characters after the ‘:’ and a final digit . This could be done with str_match() or similar but extract() just makes it so easy. Just give extract() a regular expression and capture in parentheses the things to turn into columns.

input <- c("6-7 z: dqzzzjbzz 67",
           "13-16 j: jjjvjmjjkjjjjjjj 123",
           "5-6 m: mmbmmlvmbmmgmmf 5")

tibble(raw = input) %>% 
          into = c('number_range', 'single_char', 
                   'many_char', 'single_digit'),
          regex = '(\\d+-\\d+) (\\w+): (\\w+) (\\d+)',
          convert = T) %>% 
  kable(align = 'c')
number_range single_char many_char single_digit
6-7 z dqzzzjbzz 67
13-16 j jjjvjmjjkjjjjjjj 123
5-6 m mmbmmlvmbmmgmmf 5

Done and Done (and with convert=T it even turned the single_digit into an int)!

Trick #4: Memoization

Some of the puzzles in AoC use programming concepts I haven’t thought about in a long-term (linked lists) and some used concepts I didn’t know existed. Memoization is one of those terms that I’d heard before but had no idea what it meant. There were a number of puzzles where my initial brute force solutions would take hours or days to complete. But in certain cases, memoization sped things up immensely.

Memoization caches the results of function calls so that if the same call happens a second time, rather than doing the work again, the program can just recall the value from the cache.

Functions can be memoised in R using the memoise::memoise() function to wrap the function.

For this example, I’m borrowing the Fibonacci example from this post on IWNT Statistics.


# Vanilla Function
fibb <- function(x){
  else if(x==1){return(1)}
  else{return(fibb(x - 1) + fibb(x-2))}

# Same Function But Wrapped In Memoise
memo_fib <- memoise(function(x){
  else if(x==1){return(1)}
  else{return(memo_fib(x - 1) + memo_fib(x-2))}

Running the original version:

## [1] 14930352
## 26.58 sec elapsed

And the memoised version:

## [1] 14930352
## 0.08 sec elapsed

The memoised version produces a way faster result! While hard to believe, the original function makes close to 30 million calls on its way to finding fibb(35). However, the memoised version, only needs to solve for the 35 unique function calls and can recall the answer from cache for the recursive calls.

Trick #5 - String Replacement with Back References

Back to string manipulation!

Within regular expressions there is a concept of “capture groups” which is when you wrap something in parenthesis and then are able to extract it from the string match (like how str_match() can work). However, you can also reference what is in the capture group to use it for replacement in functions like str_replace_all().

In our example, image we have a string of animals, "the cat, a bird, the dog, ze goat" and we want to insert the adjective red between “the” and each animal. There are many ways to do this, but I will use back-references, which will reference the contents of the capture group without knowing specifically what’s in it.

input <- "the cat, a bird, the dog, ze goat"

str_replace_all(input, '(\\w+) (\\w+)', '\\1 red \\2')
## [1] "the red cat, a red bird, the red dog, ze red goat"

The \\1 is a back-reference to the first capture group in parenthesis (the, a, the, and ze) while \\2 is a reference to the animals.

Trick #6 - Escaping stringR’s regular expression matching with coll()

More often than not, stringR’s use of regular expressions as the pattern is a blessing. One place where it was troublesome was when I was trying to use one variable as a pattern to replace another variable. In these cases, the special characters in my pattern (the ‘+’) were treated as part of a RegEx rather than the literal string I wanted to match.

For this example, suppose I want to replace an equation within parenthesis with the word ‘hi’ (not sure why I’d want to do this, but oh well).

  eq = c("(1 + 1)", "(7 - 3)", "(12 * 1)")
) %>% 
  mutate(ptrn = str_extract(eq, '\\(.+\\)'),
         new_eq = str_replace_all(eq, ptrn, 'hi'),
  ) %>% 
  kable(align = 'c')
eq ptrn new_eq
(1 + 1) (1 + 1) (1 + 1)
(7 - 3) (7 - 3) (hi)
(12 * 1) (12 * 1) (12 * 1)

Notice that the str_replace_all either didn’t work 100% correctly or didn’t work at all for all three cases. Even though as a person this obviously should be a match, in computer-land the symbols “(”, “)”, “+”, and "*" all are special characters for regular expressions and therefore aren’t matching the literal symbols they are intended to match.

Fortunately, there is a function coll() which will compare strings using standard collation rules rather than using RegExp rules. Wrapping the pattern variable in coll() should solve all problems.

  eq = c("(1 + 1)", "(7 - 3)", "(12 * 1)")
) %>% 
  mutate(ptrn = str_extract(eq, '\\(.+\\)'),
         new_eq = str_replace_all(eq, ptrn, 'hi'),
         with_coll = str_replace_all(eq, coll(ptrn), 'hi')
  ) %>%
  kable(align = 'c')
eq ptrn new_eq with_coll
(1 + 1) (1 + 1) (1 + 1) hi
(7 - 3) (7 - 3) (hi) hi
(12 * 1) (12 * 1) (12 * 1) hi

Now everything works!

Trick #7 - Use the assign() function to programatically create new objects

I always struggle with doing programmatic naming of objects. In the course of one of the puzzles I came across the assign() function which takes a variable name, and a object that will be given the variable name.

Suppose we have data in a data.frame with a column for Player and a value for the cards help by the player and we want to create 2 vectors; one for player 1 and one for player 2. We can use assign to create those objects.

input <- tibble::tribble(
  ~Player, ~Cards,
       1L,     1L,
       1L,     2L,
       1L,     3L,
       2L,     4L,
       2L,     5L,
       2L,     6L

# Generate the string for the variable name with paste and assign an object
for(i in seq_len(n_distinct(input$Player))){
  assign(paste0('player_',i), input %>% filter(Player == i) %>% pull(Cards))

## [1] 1 2 3
## [1] 4 5 6

Now there are two objected in the environment with names “player_1” and “player_2”

Thanks for Reading!

I would highly encourage everyone to try Advent of Code at some point. I found it really enjoyable to do a different type of programming from my day to day. Although there were instances where doing it in R made things difficult (mainly R being a 1-indexed language) I found the experience really enjoyable.

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