I recently completed all 25 days of Advent of Code 2017, an annual series of recreational programming puzzles. Each day describes a programming puzzle and illustrates a handful of simple examples of the problem. The puzzle then requires the participant to solve a much, much larger form of the problem.

For five or so of the puzzles, I used nonstandard evaluation to implement my solution. As I previously wrote, nonstandard evaluation is a way of bottling up magic spells (lines of R code) and changing how or where they are cast (evaluated). I chose to use special evaluation not because it was the easiest or most obvious solution but because I wanted to develop my skills with computing on the language. In this post, I work through one of the examples where I used nonstandard evaluation to write an interpreter for a simple machine.

## Puzzle description

Day 8 requires us to simulate the state of a register machine as it receives a series of instructions.

Each instruction consists of several parts: the register to modify, whether to increase or decrease that register’s value, the amount by which to increase or decrease it, and a condition. If the condition fails, skip the instruction without modifying the register. The registers all start at 0. The instructions look like this:

b inc 5 if a > 1
a inc 1 if b < 5
c dec -10 if a >= 1
c inc -20 if c == 10


[…]

You might also encounter <= (less than or equal to) or != (not equal to). However, the CPU doesn’t have the bandwidth to tell you what all the registers are named, and leaves that to you to determine.

What is the largest value in any register after completing the instructions in your puzzle input?

If I squint long enough at the register instructions, I can basically see R code.

# b inc 5 if a > 1
b <- if (a > 1) b + 5 else b

# a inc 1 if b < 5
a <- if (b < 5) a + 1 else a

# c dec -10 if a >= 1
c <- if (a >= 1) c - -10 else c

# c inc -20 if c == 10
c <- if (c == 10) c + -20 else c


If we can set up a way to convert the machine instructions into R code, R will handle the job of looking up values, modifying values and evaluating the logic and if statements. In other words, if we can convert register instructions into R code, the problem simplifies into something like running an R script.

And that’s a good strategy, because we have a lot of instructions to process. Each user receives some unique (I think) input for each problem, and my problem input contains 1,000 instructions.

library(magrittr)
full_input <- "https://raw.githubusercontent.com" %>%
"inst", "input08.txt") %>%

length(full_input)
#>  1000

#>  "kd dec -37 if gm <= 9"   "x dec -715 if kjn == 0"
#>  "ey inc 249 if x < 722"   "n dec 970 if t > 3"
#>  "f dec -385 if msg > -3"  "kd dec -456 if ic <= -8"


Our strategy for simulating the register machine will have the following steps:

• Parsing a register instruction
• Creating an R expression from an instruction
• Evaluating an R expression inside of a register machine
• Changing the evaluation rules to adapt to the quirks of this problem

## Parsing the register instructions with regular expressions

The instructions have a very simple grammar. Here is how I would tag the first few lines of my problem input.

[target] [verb] [num1] if [s1] [op] [num2]
kd    dec    -37 if   gm   <=     9
x    dec   -715 if  kjn   ==     0
ey    inc    249 if    x    <   722
n    dec    970 if    t    >     3
f    dec   -385 if  msg    >    -3


We can parse these lines using regular expressions. Regular expressions are an incredibly powerful language for processing text using pattern-matching rules. I will walk through each of the regular expression patterns used to parse an instruction.

To match the verbs, we can use the or | operator, so (inc|dec) matches inc or dec. We can also match the six different comparison operators using | too. In the code below, I put the patterns in parentheses so that they will be treated as a single group.

re_verb <- "(inc|dec)"
re_op <- "(>|<|==|!=|>=|<=)"


A register name is just a sequence of letters. The special character \w matches any word character; that is, it matches uppercase/lowercase letters, digits and underscores. The (token)+ suffix matches 1 or more repetitions of a token. Putting these two together, \w+ will match 1 or more adjacent word characters. That pattern in principle could matches things beside register names (like numbers) but the instruction format here is so constrained that it’s not a problem.

# We have to double the backslashes when using them in R strings
re_name <- "(\\w+)"


Numbers are just integers, and sometimes they are negative. A number here is an optional - plus some digits. The special character \d matches any digit from 0 to 9, so \d+ matches 1 or more successive digits. We can use the (token)? suffix to match an optional token. In our case, -?\d+ will match a sequence of digits and match leading hyphen if one is present.

re_number <- "(-?\\d+)"


Each pattern in parentheses is a matching group, and the function str_match() from the stringr package will return a matrix with a column for each matched group. I also include an extra set of parentheses around the condition in the if statement to also match the whole condition as well as its parts.

# Combine the sub-patterns together
re <- sprintf("%s %s %s if (%s %s %s)", re_name, re_verb,
re_number, re_name, re_op, re_number)
re
#>  "(\\w+) (inc|dec) (-?\\d+) if ((\\w+) (>|<|==|!=|>=|<=) (-?\\d+))"

text <- "b inc 5 if a > 1"
stringr::str_match(text, re)
#>      [,1]               [,2] [,3]  [,4] [,5]    [,6] [,7] [,8]
#> [1,] "b inc 5 if a > 1" "b"  "inc" "5"  "a > 1" "a"  ">"  "1"

# Column 5 matches the subgroups in columns 6, 7 and 8 as a single
# group because of the extra grouping parentheses after the if.


We can package this step into a function that takes an instruction’s text and returns a list with the labelled parts of that instruction.

parse_instruction <- function(text) {
stopifnot(length(text) == 1)
re <- "(\\w+) (inc|dec) (-?\\d+) if ((\\w+) (>|<|==|!=|>=|<=) (-?\\d+))"

text %>%
stringr::str_match(re) %>%
as.list() %>%
setNames(c("instruction", "target", "verb", "num1",
"cond", "s1", "op", "num2"))
}

str(parse_instruction(text))
#> List of 8
#>  $instruction: chr "b inc 5 if a > 1" #>$ target     : chr "b"
#>  $verb : chr "inc" #>$ num1       : chr "5"
#>  $cond : chr "a > 1" #>$ s1         : chr "a"
#>  $op : chr ">" #>$ num2       : chr "1"


## Creating R code

Next, we need to convert some strings into R code. We can do this with rlang::parse_expr(). It takes a string and creates an R expression, something I’ve described as a kind of bottled up magic spell: An expression captures magic words (code) allow us to manipulate or cast (evaluate) them.

code <- rlang::parse_expr("print('hello')")
code
#> print("hello")

code <- rlang::parse_expr("if (a > 1) b + 5 else b")
code
#> if (a > 1) b + 5 else b


The format of the instructions is relatively straightforward. We can fill in a template with the parts of the parsed line. Because inc/dec are just addition and subtraction, we replace them with the appropriate math operations.

create_r_instruction <- function(parsed) {
parsed$math <- if (parsed$verb == "inc") "+" else "-"
code <- sprintf("if (%s) %s %s %s else %s", parsed$cond, parsed$target, parsed$math, parsed$num1,
parsed$target) rlang::parse_expr(code) } r_code <- "b inc 5 if a > 1" %>% parse_instruction() %>% create_r_instruction() r_code #> if (a > 1) b + 5 else b  ## Create the register machine We have to figure out where we want to evaluate the generated R code. We create a register object to hold the values. The object will just be a list() with some extra metadata. This object will be the location where the R code is evaluated. create_register_machine <- function(...) { initial <- list(...) data <- c(initial, list(.metadata = list())) structure(data, class = c("register_machine", "list")) } # Give the machines a pretty print method print.register_machine <- function(x, ...) { utils::str(x, ...) invisible(x) } create_register_machine() #> List of 1 #>$ .metadata: list()
#>  - attr(*, "class")= chr [1:2] "register_machine" "list"


For now, we can initialize registers by using named arguments to the function.

create_register_machine(a = 0, b = 0)
#> List of 3
#>  $a : num 0 #>$ b        : num 0
#>  $.metadata: list() #> - attr(*, "class")= chr [1:2] "register_machine" "list"  ## Evaluating code inside of the machine So far, we have: • A way to analyze register instructions and convert them into R code • An object that holds register values Now, we need to evaluate an expression inside of the register. We will use tidy evaluation; the function eval_tidy() lets us evaluate an R expression inside of a data object. r_code #> if (a > 1) b + 5 else b # b + 5 r <- create_register_machine(a = 4, b = 7) rlang::eval_tidy(r_code, data = r) #>  12 # just b r <- create_register_machine(a = 0, b = 7) rlang::eval_tidy(r_code, data = r) #>  7  Now, we need to actually do something. We need to update the register machine using the value from the evaluated instruction. Otherwise, the machine will just read expressions and forget everything it’s read. To update the machine, we have to determine the register to update. Fortunately, our generated code always ends with an else branch that has the target register. r_code #> if (a > 1) b + 5 else b  If we can pull out that symbol after the else, we will have the name of register to update in the machine. Because the code is so formulaic, we can just extract the symbol directly using the code’s abstract syntax tree (AST). pryr::call_tree() shows the AST for an expression. pryr::call_tree(r_code) #> \- () #> \- if #> \- () #> \- > #> \- a #> \- 1 #> \- () #> \- + #> \- b #> \- 5 #> \- b  We can extract elements from the tree like elements in a list by selecting indices. # The numbers match one of the slashs at the first level of indentation r_code[] #> if r_code[] #> a > 1 # We can crawl down subtrees too r_code[][] #> a # But what we want is the last branch from the if level r_code[] #> b  If we convert the symbol into a string, we can look it up in the register using the usual list lookup syntax. r <- create_register_machine(a = 4, b = 7) target <- rlang::as_string(r_code[]) r[[target]] #>  7  We can also use list lookup syntax with assignment to modify the register. r[[target]] <- rlang::eval_tidy(r_code, data = r) r #> List of 3 #>$ a        : num 4
#>  $b : num 12 #>$ .metadata: list()
#>  - attr(*, "class")= chr [1:2] "register_machine" "list"


Let’s wrap these steps into a function.

eval_instruction <- function(register_machine, instruction) {
target <- rlang::as_string(instruction[])
register_machine[[target]] <- rlang::eval_tidy(
expr = instruction,
data = register_machine)
register_machine
}

create_register_machine(a = 2, b = 0) %>%
eval_instruction(r_code)
#> List of 3
#>  $a : num 2 #>$ b        : num 5
#>  $.metadata: list() #> - attr(*, "class")= chr [1:2] "register_machine" "list" create_register_machine(a = 2, b = 0) %>% # For quick testing, we pass in quoted expressions eval_instruction(quote(if (a > 1) b - 100 else b)) %>% # Should not run eval_instruction(quote(if (a < 1) b + 5 else b)) %>% # Should run eval_instruction(quote(if (a > 1) a + 10 else a)) #> List of 3 #>$ a        : num 12
#>  $b : num -100 #>$ .metadata: list()
#>  - attr(*, "class")= chr [1:2] "register_machine" "list"


## Time for some extra nonstandard evaluation

The code so far only works if the machine already has registers that match the registers in an instruction. Otherwise, we raise an error.

create_register_machine() %>%
eval_instruction(quote(if (a > 1) b - 100 else b))

# "Overscope" is the tidy evaluation term for the data context, so failing to
# find the name in the data is failing to find the name in the overscope.


To solve the problem, we could study the 1,000 lines of input beforehand, extract the register names, initialize them to 0 and then evaluate the instructions.1 Or… or… we could procrastinate and only initialize a register name to 0 when the machine encounters a name it doesn’t recognize. If, for some reason, our machine received instructions one at a time, like over a network connection, then the procrastinated approach seems even more reasonable.

This latter strategy will involve some very nonstandard evaluation. I emphasize the “very” because we are changing one of the fundamental rules of R evaluation :smiling_imp:. R throws an error if you ask it to evaluate the name of a variable that doesn’t exist. But here we are going to detect those missing variables and set them to 0 before they get evaluated.

To find the brand-new register names, we can inspect the call tree and find the names of the registers. We already know where the target is. The other place where names show up is in the condition of the if statement.

pryr::call_tree(r_code)
#> \- ()
#>   \- if
#>   \- ()
#>     \- >
#>     \- a
#>     \-  1
#>   \- ()
#>     \- +
#>     \- b
#>     \-  5
#>   \- b

extract_register_names <- function(instruction) {
reg_target <- rlang::as_string(instruction[])
reg_condition <- rlang::as_string(instruction[][])
list(target = reg_target,
registers = unique(c(reg_target, reg_condition))
)
}

extract_register_names(quote(if (a > 1) b - 100 else b)) %>% str()
#> List of 2
#>  $target : chr "b" #>$ registers: chr [1:2] "b" "a"

# Just returns unique names
extract_register_names(quote(if (b > 1) b - 100 else b)) %>% str()
#> List of 2
#>  $target : chr "b" #>$ registers: chr "b"


We can define a helper function which checks for missing names—names that yield NULL values when we try to retrieve them—and initializes them to 0.

initialize_new_registers <- function(register_machine, registers) {
for (each_register in registers) {
if (is.null(register_machine[[each_register]])) {
register_machine[[each_register]] <- 0
}
}
register_machine
}

# Before
r
#> List of 3
#>  $a : num 4 #>$ b        : num 12
#>  $.metadata: list() #> - attr(*, "class")= chr [1:2] "register_machine" "list" initialize_new_registers(r, c("a", "b", "w", "a", "s", "j")) #> List of 6 #>$ a        : num 4
#>  $b : num 12 #>$ .metadata: list()
#>  $w : num 0 #>$ s        : num 0
#>  $j : num 0 #> - attr(*, "class")= chr [1:2] "register_machine" "list"  Finally, we update our evaluation function to do this step automatically. I’m also going to add some code to record the value of the maximum register whenever an instruction is evaluated because, ummm, that’s the whole point of puzzle. eval_instruction <- function(register_machine, instruction) { # Set any new registers to 0 registers <- extract_register_names(instruction) register_machine <- initialize_new_registers( register_machine, registers$registers)

# Evaluate instruction
register_machine[[registers$target]] <- rlang::eval_tidy( expr = instruction, data = register_machine) # Find the maximum value register_names <- setdiff(names(register_machine), ".metadata") current_max <- max(unlist(register_machine[register_names])) register_machine$.metadata$max <- current_max register_machine }  Let’s try four instructions from a clean slate. create_register_machine() %>% # b gets 5 eval_instruction(quote(if (d < 1) b + 5 else b)) %>% # c gets 10 eval_instruction(quote(if (b > 1) c + 10 else c)) %>% # b gets 5 more eval_instruction(quote(if (a < 1) b + 5 else b)) #> List of 5 #>$ .metadata:List of 1
#>   ..$max: num 10 #>$ b        : num 10
#>  $d : num 0 #>$ c        : num 10
#>  $a : num 0 #> - attr(*, "class")= chr [1:2] "register_machine" "list"  Now, for the moment of truth… Let’s process all 1,000 instructions. r <- create_register_machine() for (each_instruction in full_input) { parsed <- each_instruction %>% parse_instruction() %>% create_r_instruction() r <- eval_instruction(r, parsed) } r #> List of 27 #>$ .metadata:List of 1
#>   ..$max: num 4832 #>$ kd       : num -2334
#>  $gm : num -4239 #>$ x        : num -345
#>  $kjn : num -1813 #>$ ey       : num 209
#>  $n : num -764 #>$ t        : num 2997
#>  $f : num 4468 #>$ msg      : num -3906
#>  $ic : num -263 #>$ zv       : num -599
#>  $gub : num 2025 #>$ yp       : num -2530
#>  $lyr : num -2065 #>$ j        : num 3619
#>  $e : num -4230 #>$ riz      : num 863
#>  $lzd : num 4832 #>$ ucy      : num -3947
#>  $i : num 3448 #>$ omz      : num -3365
#>  $djq : num 392 #>$ bxy      : num 1574
#>  $tj : num 1278 #>$ y        : num 1521
#>  $m : num 2571 #> - attr(*, "class")= chr [1:2] "register_machine" "list"  :star: Ta-da! The maximum register value is 4,832. Problem solved! ## And then the rules change Advent of Code problems come in two parts, and we don’t learn the question behind Part 2 until we complete Part 1. In this case, after submitting our solution for Part 1, we receive the following requirement: To be safe, the CPU also needs to know the highest value held in any register during this process so that it can decide how much memory to allocate to these operations. Accounting for this twist requires a small change to the evaluation code. We add another metadata variable to track the highest value ever stored in a register. eval_instruction <- function(register_machine, instruction) { # Set any new registers to 0 registers <- extract_register_names(instruction) register_machine <- initialize_new_registers( register_machine, registers$registers)

# Evaluate instruction
register_machine[[registers$target]] <- rlang::eval_tidy( expr = instruction, data = register_machine) # Find the maximum value register_names <- setdiff(names(register_machine), ".metadata") current_max <- max(unlist(register_machine[register_names])) register_machine$.metadata$max <- current_max # Create the max-ever value if necessary if (is.null(register_machine$.metadata$max_ever)) { register_machine$.metadata$max_ever <- 0 } # Maybe update the max-ever value if (register_machine$.metadata$max_ever < current_max) { register_machine$.metadata$max_ever <- current_max } register_machine }  Admittedly, eval_instruction() is starting to get bloated. Conceptually, we could probably the break this function down into three functions: pre-evaluation steps, evaluation, and post-evaluation steps.2 But this is good enough for now. We run the instructions again to get the updated metadata. r <- create_register_machine() for (each_instruction in full_input) { parsed <- each_instruction %>% parse_instruction() %>% create_r_instruction() r <- eval_instruction(r, parsed) } r$.metadata
#> $max #>  4832 #> #>$max_ever
#>  5443


:star2: And boom! Another problem solved.

## eval(thoughts, envir = this_problem)

I like this kind of nonstandard evaluation approach for converting problems into R code, but it’s mostly useful when the problem describes a series of instructions that can be parsed and evaluated. For problems like this register machine simulation, the nonstandard evaluation route is straightforward. But it’s also a viable problem-solving strategy when the “machine” or the “instructions” are subtler, as in this problem about simulating “dance” moves.

Odds are, you’ll never have to write an interpreter for a toy machine or language. Nevertheless, here are some R functions that we used for this puzzle that are helpful in other contexts:

• stringr::str_match() to extract all the groups in a regular expression at once.
• rlang::parse_expr() to convert a string of text into an R expression.
• pryr::call_tree() to visualize an expression’s syntax tree and expression[[i]][[j]] to pluck out symbols from locations in a tree.
• rlang::as_string() to convert a symbol into a string.
• rlang::eval_tidy() to evaluate an expression inside of a data context.

1. That actually would be pretty easy. Get a dataframe with purrr::map_df(full_input, parse_instruction). Find the unique register names. Create a list of 0’s with those names. Use do.call() to call create_register_machine() with that list. With no special evaluation trickery, this approach is closer to the idea of “just running R code”.

2. If all I did for a living was write code to simulate machines or toy languages, I might try to formalize this custom evaluation process with pre-evaluation and post-evaluations “hooks” that could be arguments to a custom evaluation function. I’m just brainstorming though.

Tags:

Updated: