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day11.R
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day11.R
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# Day 11 advent of code -- seating
#
# ------------------------------------------------------------------------------
# set up -----------------------------------------------------------------------
library(tidyverse)
# load data --------------------------------------------------------------------
seats <- read_table(paste0(here::here(), "/day11.txt"), col_names = FALSE)
seats_transformed <- seats %>%
separate(X1, paste("col", 1:nchar(seats$X1[1])), sep = "(?<=.)",extra = "drop") %>%
as.matrix()
seats_transformed[seats_transformed == "L"] <- 0
seats_transformed[seats_transformed == "."] <- NA
seats_transformed <- matrix(as.numeric(seats_transformed), nrow = nrow(seats_transformed), byrow = FALSE)
nrow <- nrow(seats_transformed)
ncol <- ncol(seats_transformed)
stop <- FALSE
while (stop == FALSE){
seats_transformed_current <- seats_transformed
seats_transformed[is.na(seats_transformed)] <- 0
# make shifted copies of the array
shiftN = rbind(seats_transformed[-1,],rep(0,ncol))
shiftS = rbind(rep(0,ncol),seats_transformed[-nrow,])
shiftE = cbind( rep(0,nrow) , seats_transformed[,-ncol])
shiftW = cbind(seats_transformed[,-1],rep(0,nrow))
shiftSE = rbind(rep(0,ncol),cbind(rep(0,nrow-1),seats_transformed[-nrow, -ncol]))
shiftSW = rbind(rep(0,ncol),cbind(seats_transformed[-nrow,-1],rep(0,ncol-1)))
shiftNW = rbind(cbind(seats_transformed[-1,-1],rep(0,ncol-1)),rep(0,ncol))
shiftNE = rbind(cbind(rep(0,ncol-1),seats_transformed[-1,-ncol]),rep(0,ncol))
new_seats <- shiftW + shiftNW + shiftN + shiftNE + shiftE + shiftSE + shiftS + shiftSW
new_seats[is.na(seats_transformed_current)] <- NA
new_seats[new_seats == 0] <- -1 # fill an empty seat
new_seats[new_seats >= 4] <- 0 # empty a seat if too many people are nearby
new_seats[new_seats > 0 & seats_transformed == 0] <- 0 # empty seats don't get filled if there's an adjacent seat filled
new_seats[new_seats != 0] <- 1
if (sum(new_seats != seats_transformed_current, na.rm = TRUE) == 0){
stop = TRUE
}
seats_transformed <- new_seats
print(sum(seats_transformed == 1, na.rm = TRUE))
}
# number of occupied seats
sum(seats_transformed == 1, na.rm = TRUE)
# ### alternative part 1 which should help more with part 2
# seats_transformed <- seats %>%
# separate(X1, paste("col", 1:nchar(seats$X1[1])), sep = "(?<=.)",extra = "drop") %>%
# as.matrix()
#
# seats_transformed[seats_transformed == "L"] <- 0
# seats_transformed[seats_transformed == "."] <- NA
#
# seats_transformed <- matrix(as.numeric(seats_transformed), nrow = nrow(seats_transformed), byrow = FALSE)
#
# stop <- FALSE
#
# while (stop == FALSE){
#
# new_seats <- matrix(NA, nrow = nrow(seats_transformed),
# ncol = ncol(seats_transformed))
# seats_transformed_current <- seats_transformed
# seats_transformed[is.na(seats_transformed)] <- 0
#
# # add fake rows to end to avoid errors in calculation
# seats_transformed_dummy <- rbind(seats_transformed, rep(0,ncol(seats_transformed)))
# seats_transformed_dummy <- cbind(seats_transformed_dummy, rep(0,nrow(seats_transformed_dummy)))
#
# for (i in 1:(nrow(seats_transformed))){
# for (j in 1:nrow(seats_transformed)){
#
# new_seats[i,j] <- sum(c(seats_transformed_dummy[i-1, j-1],
# seats_transformed_dummy[i-1, j],
# seats_transformed_dummy[i-1, j+1],
# seats_transformed_dummy[i, j-1],
# seats_transformed_dummy[i, j+1],
# seats_transformed_dummy[i+1, j-1],
# seats_transformed_dummy[i+1, j],
# seats_transformed_dummy[i+1, j+1]))
# }
# }
#
# new_seats[is.na(seats_transformed_current)] <- NA
#
# new_seats[new_seats == 0] <- -1 # fill an empty seat
# new_seats[new_seats >= 4] <- 0 # empty a seat if too many people are nearby
# new_seats[new_seats > 0 & seats_transformed == 0] <- 0 # empty seats don't get filled if there's an adjacent seat filled
#
# new_seats[new_seats != 0] <- 1
#
# if (sum(new_seats != seats_transformed_current, na.rm = TRUE) == 0){
# stop = TRUE
# }
#
# seats_transformed <- new_seats
# print(sum(seats_transformed == 1, na.rm = TRUE))
# }
#
# # number of occupied seats
# sum(seats_transformed == 1, na.rm = TRUE)
# part 2 - this time need to search each direction for first seat to determine whether it is empty or filled
seats_transformed <- seats %>%
separate(X1, paste("col", 1:nchar(seats$X1[1])), sep = "(?<=.)",extra = "drop") %>%
as.matrix()
seats_transformed[seats_transformed == "L"] <- 0
seats_transformed[seats_transformed == "."] <- NA
seats_transformed <- matrix(as.numeric(seats_transformed), nrow = nrow(seats_transformed), byrow = FALSE)
stop <- FALSE
# a fucntion to get the first non empty potision
get_seat_type <- function(position){
return(position[!is.na(position)][1])
}
# rotate matrices
rotate <- function(x) t(apply(x, 2, rev))
while (stop == FALSE){
new_seats <- matrix(NA, nrow = nrow(seats_transformed),
ncol = ncol(seats_transformed))
seats_transformed_current <- seats_transformed
# add fake rows to end to avoid errors in calculation
seats_transformed_dummy <- rbind(rep(0,ncol(seats_transformed)), seats_transformed, rep(0,ncol(seats_transformed)))
seats_transformed_dummy <- cbind(rep(0,nrow(seats_transformed_dummy)), seats_transformed_dummy, rep(0,nrow(seats_transformed_dummy)))
for (i in 1:(nrow(seats_transformed))){
for (j in 1:ncol(seats_transformed)){
# hardcode in the sum
# first seat seen to the right
right <- get_seat_type(seats_transformed_dummy[i+1,(j+2):ncol(seats_transformed_dummy)])
# first seat seen to the left
left <- get_seat_type(rev(seats_transformed_dummy[i+1,1:(j)]))
# first seat seen up
up <- get_seat_type(rev(seats_transformed_dummy[1:i,j+1]))
# first seat seen down
down <- get_seat_type(seats_transformed_dummy[(i+2):nrow(seats_transformed_dummy),j+1])
# first seat seen North West
top_row <- (i+1) - min(j+1, i+1) + 1
bottom_row <- i + 1
left_col <- (j+1) - min(j+1, i+1) + 1
right_col <- j + 1
nw <- get_seat_type(rev(diag(seats_transformed_dummy[top_row:bottom_row, left_col:right_col]))[-1])
# first seat seen North East
top_row <- (i+2) - min(ncol(seats_transformed_dummy) - (j), i+1)
bottom_row <- i + 1
right_col <- (j) + min(ncol(seats_transformed_dummy) - (j), i+1)
left_col <- j + 1
ne <- get_seat_type(diag(rotate(seats_transformed_dummy[top_row:bottom_row, left_col:right_col]))[-1])
# first seat seen South West
top_row <- i + 1
bottom_row <- (i+1) + min((j+1), nrow(seats_transformed_dummy) - (i+1))
right_col <- j + 1
left_col <- (j+1) - min((j+1), nrow(seats_transformed_dummy) - (i+1))
sw <- get_seat_type(diag(rotate(rotate(rotate(seats_transformed_dummy[top_row:bottom_row, left_col:right_col]))))[-1])
# first seat seen South East
top_row <- i + 1
bottom_row <- (i+1) + min(ncol(seats_transformed_dummy) - (j+1), nrow(seats_transformed_dummy) - (i+1))
left_col <- j + 1
right_col <- (j+1) + min(ncol(seats_transformed_dummy) -(j+1), nrow(seats_transformed_dummy) - (i+1))
se <- get_seat_type(diag(seats_transformed_dummy[top_row:bottom_row, left_col:right_col])[-1])
new_seats[i,j] <- up + down + left + right + sw + se + nw + ne
}
}
new_seats[is.na(seats_transformed_current)] <- NA
new_seats[new_seats == 0] <- -1 # fill an empty seat
new_seats[new_seats >= 5] <- 0 # empty a seat if too many people are nearby
new_seats[new_seats > 0 & seats_transformed == 0] <- 0 # empty seats don't get filled if there's an adjacent seat filled
new_seats[new_seats != 0] <- 1
if (sum(new_seats != seats_transformed_current, na.rm = TRUE) == 0){
stop = TRUE
}
seats_transformed <- new_seats
print(sum(seats_transformed == 1, na.rm = TRUE))
}
# number of occupied seats
sum(seats_transformed == 1, na.rm = TRUE)