library(tidyverse)
library(here)path_data <- here("2021/inputs/06-input.txt")
input <- tibble(x = read_lines(path_data))raw <- input %>%
mutate(x = str_split(x, ",")) %>%
pull(x)
fish <- as.numeric(raw[[1]])
fish## [1] 2 5 2 3 5 3 5 5 4 2 1 5 5 5 5 1 2 5 1 1 1 1 1 5 5 1 5 4 3 3 1 2 4 2 4 5 4 5 5 5 4 4 1 3
## [45] 5 1 2 2 4 2 1 1 2 1 1 4 2 1 2 1 2 1 3 3 3 5 1 1 1 3 4 4 1 3 1 5 5 1 5 3 1 5 2 2 2 2 1 1
## [89] 1 1 3 3 3 1 4 3 5 3 5 5 1 4 4 2 5 1 5 5 4 5 5 1 5 4 4 1 3 4 1 2 3 2 5 1 3 1 5 5 2 2 2 1
## [133] 3 3 1 1 1 4 2 5 1 2 4 4 2 5 1 1 3 5 4 2 1 2 5 4 1 5 5 2 4 3 5 2 4 1 4 3 5 5 3 1 5 1 3 5
## [177] 1 1 1 4 2 4 4 1 1 1 1 1 3 4 5 2 3 4 5 1 4 1 2 3 4 2 1 4 4 2 1 5 3 4 1 1 2 2 1 5 5 2 5 1
## [221] 4 4 2 1 3 1 5 5 1 4 2 2 1 1 1 5 1 3 4 1 3 3 5 3 5 5 3 1 4 4 1 1 1 3 3 2 3 1 1 1 5 4 2 5
## [265] 3 5 4 4 5 2 3 2 5 2 1 1 1 2 1 5 3 5 1 4 1 2 1 5 3 5 2 1 3 1 2 4 5 3 4 3Do it by adding to a vector. Will come back to bite us later.
# decrement the fish
# check for negatives
# reset negatives and add that many new fish
one_day <- function(fish) {
curr_fish <- fish - 1 # decrement fish by one
total_negatives <- sum(curr_fish < 0) # count fish that will make new fish
curr_fish <- replace(curr_fish, curr_fish < 0, 6) # reset fish
curr_fish <- append(curr_fish, rep(8, total_negatives)) # add new fish
return(curr_fish)
}
fish_status <- fish
for (i in seq(80)) {
fish_status <- one_day(fish_status)
}
length(fish_status)## [1] 350605Adding to a vector wont work here. Make a dictionary of sorts and update it.
# use dataframe as a dictionary
one_day_better <- function(df) {
df %>% mutate(
coming_soon = num_0,
num_0 = num_1,
num_1 = num_2,
num_2 = num_3,
num_3 = num_4,
num_4 = num_5,
num_5 = num_6,
num_6 = num_7,
num_7 = num_8,
num_8 = coming_soon,
num_6 = num_6 + coming_soon
) %>%
mutate(coming_soon = 0)
}
# make a blank dataframe to start
blank <- tibble(x = c(paste("num", seq(0, 8), sep = "_"), "coming_soon"), value = 0) %>%
pivot_wider(names_from = x, values_from = value)# manually find out the starting values
tibble(fish) %>%
count(fish)## # A tibble: 5 × 2
## fish n
## <dbl> <int>
## 1 1 89
## 2 2 50
## 3 3 46
## 4 4 48
## 5 5 67# update blank to starting conditions
start <- blank %>%
mutate(
num_1 = 89,
num_2 = 50,
num_3 = 46,
num_4 = 48,
num_5 = 67
)
# make bunches of lanternfish!
fish_status2 <- start
for (i in seq(256)) {
fish_status2 <- one_day_better(fish_status2)
}
# add up the big lanternfish population!
fish_status2 %>%
pivot_longer(cols = everything()) %>%
summarise(total = sum(value))## # A tibble: 1 × 1
## total
## <dbl>
## 1 1592778185024And we have made bunches of lanternfish. Hope you learned something!
How would you do it? What’s your shortcut? Please share!
Till next time!