AoC 3rd Advent Sunday Wrap Up

Be warned: spoilers ahead.

Days 5 to 11 posed a bit more challenge than the first four and gave the opportunity to explore various parts of R.

Day 5

The actual logic of the puzzle was quite easy:

do_move <- function(stacks, count, from, to, move_fun = identity) {
  stacks[[to]] <- c(stacks[[to]], move_fun(tail(stacks[[from]], count)))
  stacks[[from]] <- stacks[[from]][seq_len(length(stacks[[from]]) - count)]

where move_fun was either identity() or rev(). Getting the data into shape was more interesting and the native pipe could be put to good use as well as a new experminental feature:

stacks <- gsub("    ", " [_]", parts[[1L]]) |>
  ustrsplit(split = "\n") |>
  stacks => head(stacks, length(stacks) - 1L) |>
  strsplit(split = " ") |>
  data.table::transpose() |>
  lapply(rev) |>

The pipebind operator => in the middle of this pipeline can be used to the current argument to a name in the middle of a pipe. This allows using the current argument of the pipeline without the need to resort to ad hoc anonymous functions. Since it’s an experimental feature, it must be activated. This can be done by putting this in your .Rprofile: Sys.setenv("R_USE_PIPEBIND"=TRUE). This also works in RStudio.

Day 6

Day 6 was the easiest puzzle until now but I learned one small trick: The base function match() has an optional argument nomatch which specifies the return value if no match is found. In this case an if statement can be avoided by setting nomatch=0. The code below gets the part from a index to the end or keeps the buffer with one char added:

index <- match(char, buffer, nomatch = 0L)
buffer <- c(buffer, char)
buffer <- buffer[(index + 1L):length(buffer)]

Day 7

This puzzle gave a good reason to start the Dictionary class in recollections! Unlike the builtin list datatype the recollections dictionary can be used by reference which allowed finding a directory and directly using it without the need to copy it back into the directory tree. On top of that, the C++ code that underlies the Dictionary class is more efficient than that of list. With the tree of dictionaries in place, the logic to find the sizes of all leaves in the tree is a standard use of recursion.

Day 8

I’m not completely happy with this solution to this puzzle. After tinkering and looking at profvis output I managed to create a solution that runs in less than 10 seconds on this old machine but if the forest gets much bigger this code will struggle. Putting the puzzle input into a data.table might not be the most natural thing to do but in the end to write a quite clear solution so maybe it’s not all bad.

Day 9

In this puzzle we were asked to implement some weird version of snake. Again, this seems to be best solved using recollections::Dictionary to keep track of which cells have been visited. My initial solution solution had quite a bit of logic to determine the moves in the tail but this Reddit comment that simplified the logic quite a bit. Despite being a similar solution it’s interesting to see that the Python and R solution use quite different language features.

Day 10

Now we are asked to emulate a simple instruction set. The instruction set is so simple that execution and a history of all states can be handled using just data.table (with a big help of shift() and nafill().

Day 11

In this exercise it seems logical to put all properties of the individual monkeys in some kind of class. So, this was a good moment to play around with S4 classes. This worked out quite well but I did notice there is a bit of overhead when one interacts with the slots of the classes. By batching slot manipulation a speed of 50% was achieved. This performance improvement is unlikely to be relevant for most uses of R though as a lot of R code won’t have such tight loops.

AoC 2022 2nd Advent Sunday wrap up

This Advent period I’m participating in the Advent of Code (AoC) using R base + data.table and put the code on GitHub. Till now, the given data is easily be manipulated and there was even no need to use conditions or loops to get to the answers. Just using the functions in R base or loading and manipulating the data with data.table was sufficient thanks to tstrsplit(), and foverlaps().