RoSE e-CONFERENCE 2025

Alt: R code used Day 7 Part B library(tidyverse) options(scipen = 999) data <- readLines("day7_input.txt") %>% tibble(lines = .) %>% separate(lines, into = c("answer", "parts"), sep = "\\:") %>% mutate(answer = as.numeric(answer), answer_id = row_number()) next_step <- function(a, b, ans){ test <- c(a + b, a * b, as.numeric(paste0(a,b))) return(test[test <= ans]) } total_sum <- 0 for(i in 1:nrow(data)){ stuff <- data %>% filter(answer_id == i) %>% separate_rows(parts, sep = " ") %>% filter(!parts == "") %>% mutate(parts = as.numeric(parts)) ans <- stuff$answer %>% unique() min <- stuff$parts %>% sum() max <- stuff$parts %>% prod() # check these ones first might get lucky # ok is vector of valid lines from part A if(min == ans || max == ans || i %in% ok){ total_sum <<- total_sum + ans } else { step <- 1 cur_sum <- stuff$parts[step] for(step in 2:nrow(stuff)){ next_num <- stuff$parts[step] cur_sum <<- next_step(cur_sum, next_num, ans) } if(sum(cur_sum == ans) > 0){ total_sum <<- total_sum + ans } } } total_sum

Statistics teaching at the high school level needs modernizing to include digital sources of data that students interact with every day. Algorithmic modeling approaches are recommended, as they can...