# AoC 2018 Day 8: Checksum Treeification

### Source: The Sum of Its Parts

Part 1: A custom tree data structure is defined as:

• child count
• child count additional subtrees (recursive)
• metadata count metadata nodes

Calculate the sum of all metadata nodes.

Yay! Something Racket is great at! Trees!

First, let’s read in the structure. Reading recursive data is wonderfully simple in Racket:

(struct tree (children metadata) #:transparent)

(tree (for/list ([i (in-range child-count)])
(read))))

And calculating the sum recursively across a tree is just about as short:

; Sum all metadata values for a simple checksum
(define (simple-checksum tr)
(+ (for/sum ([child (in-list (tree-children tr))])
(simple-checksum child))
(apply + (tree-metadata tr))))

Fun!

Part 2: Now, calculate the sum as follows:

• If a node has no children, sum the metadata nodes
• Otherwise, sum the metadata nodes as follows:

• If a metadata value is small enough to be a 1-based index of children, use that child’s checksum
• Otherwise, use 0
• Calculate the new checksum.

Honestly, it’s just as long to describe the algorithm as to make it into code. There’s a bit of weird since the data structure is using 1-based indexing (what are we, Lua? Matlab?), but not too bad:

; Checksum with no children is sum of metadata
; Checksum with children uses the metadata as index, sums those children
(define (complex-checksum tr)
(cond
[(null? (tree-children tr))
[else
(cond
[(<= 1 index (length (tree-children tr)))
(complex-checksum (list-ref (tree-children tr) (sub1 index)))]
[else
0]))]))

Print them and we go:

\$ cat input.txt | racket checksum-treeificator.rkt

[part 1] 37262
[part 2] 20839

I like trees. 😄