2021-09: Smoke Basin

library(stringr)
library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ forcats 0.5.2 
✔ readr   2.1.3      
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(data.table)

Attaching package: 'data.table'

The following objects are masked from 'package:dplyr':

    between, first, last

The following object is masked from 'package:purrr':

    transpose

library(dbscan)

Attaching package: 'dbscan'

The following object is masked from 'package:stats':

    as.dendrogram

library(factoextra)

Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

dt <- fread("input.txt", header = FALSE, colClasses = "character")
dt <- data.frame(str_split(dt$V1, "", simplify = TRUE))
dtPadTop <- dt[1,]
dtPadTop[!is.na(dtPadTop)] <- 10
dt <- rbind(rbind(dtPadTop, dt), dtPadTop)
dtPadSides <- dt[,1]
dtPadSides[!is.na(dtPadSides)] <- 10
dt <- cbind(cbind(dtPadSides, dt), dtPadSides)
dt <- data.frame(apply(dt, 2, as.numeric))

Part 1

dt2 <- dt

coordX <- c()
coordY <- c()
rLow <- c()
for(r in 2:(nrow(dt2) - 1)) {
  for(c in 2:(ncol(dt2) - 1)) {
    if(dt2[r,c] == min(dt[(r-1):(r+1),(c-1):(c+1)])) {
      rLow <- append(rLow, dt2[r,c])
      coordX <- append(coordX, c)
      coordY <- append(coordY, r)
    }
  }
}

s <- 0
for(n in 1:length(rLow)) {
  s <- s + (rLow[n] + 1)
}
s

[1] 575

Part 2

dt2 <- dt
dt2[dt2 == 9 | dt2 == 10] <- NA

coordX <- c()
coordY <- c()
rLow <- c()
for(r in 2:(nrow(dt2) - 1)) {
  for(c in 2:(ncol(dt2) - 1)) {
    if(!is.na(dt2[r,c])) {
      rLow <- append(rLow, dt2[r,c])
      coordX <- append(coordX, c)
      coordY <- append(coordY, r)
    }
  }
}

dt3 <- data.frame("x" = coordX, "y" = coordY, "d" = rLow)
dt3 <- dt3[dt3$d <= 8,]
db <- dbscan(dt3[,1:2], eps = 1, minPts = 0)
dt3$cluster <- db$cluster
dt <- dt3 %>%
  group_by(cluster) %>%
  count() %>%
  filter(cluster != 0) %>%
  arrange(desc(n)) %>%
  head(3)
prod(dt$n)

[1] 1019700

dt3$cluster <- ifelse(dt3$cluster %in% dt$cluster, dt3$cluster, 0)
dt3 %>%
  mutate(cluster = as.factor(cluster)) %>%
  ggplot() +
  geom_point(aes(x = x, y = y, color = cluster))

ggsave("cluster.png")

Saving 7 x 5 in image