Day 2 complete

I was lazy and did not do tests again. I also didn't refactor when I was
given terminology, which I almost certainly will need to do when later
problems push for using this code again and expanding it.

Some key things worth noting:
  * R starts indexes with 1, so there are a bunch of places in the
    instruction code where I have to do + 1s etc
  * pmap is really cool-- it's what until a couple of months ago I
    thought rowwise was all about, and thankfully someone on twitter
    said the right thing to make me realize this is how it works.
  * there's almost definitely a better way than producing all the
    results and looking for the right one, but who knows. This was fast.
  * scoping in R is cool, and understanding when you are and are not
    overwriting state in `pmap` and `for` loops is key to finding the
    quick ways to do this. In a new language, the "careful to reset"
    advice would be more important because of pass-by-reference
    semantics and mutable data structures.

day_2.R

@@ -0,0 +1,45 @@
+# This input has the values in 1 and 2 replaced as needed to solve part 1.
+input <- c(1,12,2,3,1,1,2,3,1,3,4,3,1,5,0,3,2,6,1,19,1,19,5,23,2,9,23,27,1,5,27,31,1,5,31,35,1,35,13,39,1,39,9,43,1,5,43,47,1,47,6,51,1,51,13,55,1,55,9,59,1,59,13,63,2,63,13,67,1,67,10,71,1,71,6,75,2,10,75,79,2,10,79,83,1,5,83,87,2,6,87,91,1,91,6,95,1,95,13,99,2,99,13,103,1,103,9,107,1,10,107,111,2,111,13,115,1,10,115,119,1,10,119,123,2,13,123,127,2,6,127,131,1,13,131,135,1,135,2,139,1,139,6,0,99,2,0,14,0)
+
+
+step_function <- function(input) {
+  for (i in seq(1, length(input), 4)) {
+    op <- input[i]
+    if (op == 99 | i == length(input)) {
+      return(input)
+    }
+    # fucking index 1
+    x_pos <- input[i + 1] + 1 
+    y_pos <- input[i + 2] + 1
+    res_pos <- input[i + 3] + 1
+    result <- ifelse(op == 1, 
+                     input[x_pos] + input[y_pos],
+                     ifelse(op == 2,
+                            input[x_pos] * input[y_pos], NA))
+    input[res_pos] <- result
+  }
+  input
+}
+
+# I don't want to refactor for their terminology quite yet, so here's
+# my part 2 process.
+
+# First, provide a fresh input:
+library(magrittr)
+library(purrr)
+initial_memory_state <- c(1,0,0,3,1,1,2,3,1,3,4,3,1,5,0,3,2,6,1,19,1,19,5,23,2,9,23,27,1,5,27,31,1,5,31,35,1,35,13,39,1,39,9,43,1,5,43,47,1,47,6,51,1,51,13,55,1,55,9,59,1,59,13,63,2,63,13,67,1,67,10,71,1,71,6,75,2,10,75,79,2,10,79,83,1,5,83,87,2,6,87,91,1,91,6,95,1,95,13,99,2,99,13,103,1,103,9,107,1,10,107,111,2,111,13,115,1,10,115,119,1,10,119,123,2,13,123,127,2,6,127,131,1,13,131,135,1,135,2,139,1,139,6,0,99,2,0,14,0)
+
+# Just calculate all possible results, because I'm lazy.
+possible_parameters <- expand.grid(noun = seq(0L,99L), verb = seq(0L,99L))
+
+pmap_dbl(possible_parameters, function(noun,verb) {
+   initial_memory_state[2] <- noun
+   initial_memory_state[3] <- verb 
+   step_function(initial_memory_state)[1]
+  }
+) %>%
+bind_cols(possible_parameters, results = .) %>%
+filter(results == 19690720) %>%
+mutate(answer = 100 * noun + verb) %$%
+answer 
+