‘mustashe’ Explained

The purpose of the ‘mustashe’ R package is to save objects that result from some computation, then load the object from file the next time the computation is performed. In other words, the first time a chunk of code is evaluated, the output can be stashed for the next time the code chunk is run.

This post explains how ‘mustashe’ works. See the previous post for an introduction to the package.

‘mustashe’ can be installed from CRAN or from GitHub.

install.packages("mustashe")

# install.packages("devtools")
devtools::install_github("jhrcook/mustashe")

How ‘mustashe’ works

Overview

The following is the actual code for the stash() function, the main function of the ‘mustashe’ package. I have only added a few more comments for clarification.

stash <- function(var, code, depends_on = NULL) {
# Make sure the stashing directory ".mustashe" is available.
check_stash_dir()
# Deparse and format the code.
deparsed_code <- deparse(substitute(code))
formatted_code <- format_code(deparsed_code)
# Make sure the `var` and `code` are not `NULL`.
if (is.null(var)) stop("`var` cannot be NULL")
if (formatted_code == "NULL") stop("`code` cannot be NULL")
# Make a new hash table.
new_hash_tbl <- make_hash_table(formatted_code, depends_on)
# if the variable has been stashed:
# if the hash tables are equivalent:
# load the stored variable
# else:
# make a new stash
# else:
# make a new stash
if (has_been_stashed(var)) {
old_hash_tbl <- get_hash_table(var)
if (hash_tables_are_equivalent(old_hash_tbl, new_hash_tbl)) {
message("Loading stashed object.")
load_variable(var)
} else {
message("Updating stash.")
new_stash(var, formatted_code, new_hash_tbl)
}
} else {
message("Stashing object.")
new_stash(var, formatted_code, new_hash_tbl)
}
invisible(NULL)
}

Overall, I believe the logic is quite simple. The steps that the stash() function follows, further explained in the following sections, are:

1. Deparse and format the code.
2. Make a hash table based on the code and dependencies.
3. If the object has previously been stashed, then the new hash table and the stashed one are compared. If they are the same, then the object is loaded from memory.
4. If the hash tables are different or the object has never been stored, then the code is evaluated and the object and its hash table are stashed.

Deparsing and formatting code

The first step taken by the stash() function is to deparse and format the code.

Deparsing the code means to turn the unevaluated expression into a string. The deparsing is done by passing the code immediately to substitute() and deparse(). This must be done immediately, else the code will be evaluated. The substitute() function “returns the parse tree for the (unevaluated) expression expr, substituting any variables bound in env.”

substitute(x <- 1)

#> x <- 1

The deparse() function “Turn[s] unevaluated expressions into character strings.” Paired with substitute(), it returns a string of the unevaluated code.

deparse(substitute(x <- 1))

#> [1] "x <- 1"

With the code now as a string, it is formatted using the tidy_source() function from ‘formatR’. An internal function in ‘mustashe’, format_code() handles this process:

format_code <- function(code) {
fmt_code <- formatR::tidy_source(
text = code,
comment = FALSE,
blank = FALSE,
arrow = TRUE,
brace.newline = FALSE,
indent = 4,
wrap = TRUE,
output = FALSE,
width.cutoff = 80
)$text.tidy
paste(fmt_code, sep = "", collapse = "\n")
}
format_code("x <- 2")

#> [1] "x <- 2"

The purpose of formatting the code is so any stylistic changes to the code input do not affect the hash table. To demonstrate this, notice how the output from format_code() is the same between the two different code examples.

format_code("x=2")

#> [1] "x <- 2"

format_code(("x <- 2 # a comment"))

#> [1] "x <- 2"

Making a hash table

The hash table is a two-column table with the name and hash value of the code and any (optional) dependencies.

The hashing is handled by the ‘digest’ package. It takes a value and reproducibly produces a unique hash value.

digest::digest("mustashe")

#> [1] "ac2aad9fdb730500c56009bff6154a7e"

A hash value is made for the code and for any of the dependencies linked to the object. This process is handled by the make_hash_table(code, depends_on) internal function.

Comparing hash tables

To tell if the code or dependencies have changed, the new hash table and stashed hashed table are compared. The function underlying this process is all.equal() from base R. This function compares two objects and “If they are different, [a] comparison is still made to some extent, and a report of the differences is returned.”

Here is an example of using all.equal() to compare two data frames.

# Two data frames with a small difference *
df1 <- data.frame(a = c(1, 2, 3), b = c(5, 6, 7))
df2 <- data.frame(a = c(1, 2, 3), b = c(5, 6, 8))
# When the two data frames are equivalent.
all.equal(df1, df1)

#> [1] TRUE

# When the two data frmaes are not equivalent.
all.equal(df1, df2)

#> [1] "Component \"b\": Mean relative difference: 0.1428571"

A word of caution, if using all.equal() for a boolean comparison (like in an if-statement), make sure to wrap it with isTRUE, otherwise it will return TRUE or comments on the differences, but not FALSE.

Evaluating code and making a stash

If the hash tables are different, that means the code must be evaluated, the new object be assigned to the desired name (var), and the new hash table and value stashed. This is handled by the internal function new_stash().

# Make a new stash from a variable, code, and hash table.
new_stash <- function(var, code, hash_tbl) {
val <- evaluate_code(code)
assign_value(var, val)
write_hash_table(var, hash_tbl)
write_val(var, val)
}

The first step is to evaluate the code with the evaluate_code(code) function. It uses the parse() and eval() functions and returns the resulting value.

# Evaluate the code in a new environment.
evaluate_code <- function(code) {
eval(parse(text = code), envir = new.env())
}

This value is then assigned the desired name in the global environment using the internal assign_value(var, val) function, where .TargetEnv is a variable in the package pointing to .GlobalEnv.

# Assign the value `val` to the variable `var`.
assign_value <- function(var, val) {
assign(var, val, envir = .TargetEnv)
}

Lastly, the hash table and value are written to file using wrapper functions around readr::write_tsv() and saveRDS().

Contact

Any issues and feedback on ‘mustashe’ can be submitted here. I can be reached through the contact form on my website or on Twitter @JoshDoesa.