14: Reindeer Olympics

library(mistlecode)

To install `mistlecode` yourself, run `devtools::install_github('guslipkin/mistlecode')`.

 Also loading:  cipheR data.table dplyr purrr slider stringr tidyverse glue

dt <- 
  fread("input2.txt", sep = " ") |>
  select(V1, V4, V7, V14) |>
  `colnames<-`(c("reindeer", "speed", "flight_time", "rest_time"))
sec <- 2503

This bit was added after Part 1 was completed because it’s reused in Part 2.

do_part <- function(dt) {
  dt |>
  uncount(speed, .remove = FALSE, .id = "distance") |>
  filter(distance <= flight_time) |>
  mutate(total_time = flight_time + rest_time) |>
  uncount(total_time, .id = "total_time") |>
  filter(distance == 1) |>
  mutate(distance = ifelse(total_time <= flight_time, speed, 0)) |>
  pivot_wider(
    id_cols = total_time,
    names_from = reindeer,
    values_from = distance
  ) |>
  bind_rows(data.frame(
    "total_time" = rep(NA_integer_, sec - max(dt$flight_time + dt$rest_time))
  )) |>
  mutate(
    total_time = row_number(),
    across(
      -total_time, 
      \(x) {
        len_x <- length(x)
        x <- na.omit(x)
        rep(x, ceiling(sec / length(x)))[1:sec]
      }
    )
  )
}

Part 1

This was super annoying. My first version of my .qmd file was corrupted somehow so I could only run anything once after restarting RStudio. I thought it was because I had just updated to R 4.3 but deleting the old file and pasting everything into a new one seems to have fixed it.

Anyways, easy enough. Just a little annoying to get the vector recycling to line up how I wanted it to.

dt |>
  do_part() |>
  select(-total_time) |>
  map_int(sum, na.rm = TRUE) |>
  max()

[1] 2660

Part 2

This looks simple enough to change Part 1.

I ended up not changing the parts I thought I would

dt |>
  do_part() |>
  pivot_longer(
    cols = -total_time,
    names_to = "reindeer",
    values_to = "distance"
  ) |>
  arrange(reindeer, total_time) |>
  mutate(total_distance = cumsum(distance), .by = reindeer) |>
  mutate(
    points = ifelse(total_distance == max(total_distance), 1, 0), 
    .by = total_time
  ) |>
  mutate(total_points = cumsum(points), .by = reindeer) |>
  filter(total_time == sec) |>
  pull(total_points) |>
  max()

[1] 1564