‘mustashe’

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.

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

install.packages("mustashe")

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

Check out the next post the see how ‘mustashe’ works under-the-hood.

Basic example

Below is a breif example outlining the use of the primary function from the package, stash(). First we must load the ‘mustashe’ library.

library(mustashe)

Say we are performing a long-running computation (simulated here using Sys.sleep() to pause for a few seconds) that produces the object named x. The name of the object to stash "x" and the code itself are passed to stash() as follows. (I used the package ‘tictoc’ to time the execution of the code.)

tic("long-running computation")
stash("x", {
Sys.sleep(5)
x <- 5
})

#> Stashing object.

toc()

#> long-running computation: 5.885 sec elapsed

‘mustashe’ tells us that the object was stashed, and we can see that x was successfully assigned the value 5.

x

#> [1] 5

Say we are done for the day, so we close RStudio and go home. When we return the next day and continue on the same analysis, we really don’t want to have to run the same computation again since it will have the same result as yesterday. Thanks to ‘mustashe’, the code is not evaluated and, instead, the object x is loaded from file.

tic("long-running computation")
stash("x", {
Sys.sleep(5)
x <- 5
})

#> Loading stashed object.

toc()

#> long-running computation: 0.217 sec elapsed

That’s the basic use case of ‘mustashe’! Any issues and feedback can be submitted here. Continue reading below for explanations of other useful features of ‘mustashe’.

Why not use ’ProjectTemplate’s cache() function?

Originally I tried to use the cache() function from ‘ProjectTemplate’, but ran into a few problems.

The first was, to use it without modification, I would need to be using the ‘ProjectTemplate’ system for my whole analysis project. It first checks if all of the expected directories and components are in place, throwing an error when they are not.

ProjectTemplate::cache("x")

#> Current Directory: R_playground is not a valid ProjectTemplate directory because one or more mandatory directories are missing. If you believe you are in a ProjectTemplate directory and seeing this message in error, try running migrate.project(). migrate.project() will ensure the ProjectTemplate structure is consistent with your version of ProjectTemplate.
#> Change to a valid ProjectTemplate directory and run cache() again.
#> Error in .quietstop():

I then tried copying the source code for the cache() function to my project and tweaking it to work (mainly removing internal checks for ‘ProjectTemplate’ system). I did this and thought it was working: on the first pass it would cache the result, and on the second it would load from the cache. However, in a new session of R, it would not just load from the cache, but, instead, evaluate the code and cache the results. After a bit of exploring the cache() source code, I realized the problem was that ‘ProjectTemplate’ compares the current value of the object to be cached with the object that is cached. Of course, this requires the object to be in the environment already, which it is in a ‘ProjectTemplate’ system after running load.project() because that loads the cache (lazily) into the R environment. I do not want this behaviour, and thus the caching system used by ‘ProjectTemplate’ was insufficient for my needs.

That said, I heavily relied upon the code for cache() when creating stash(). This would have been far more difficult to do without reference to ‘ProjectTemplate’.

Features

There are two major features of the stash() function from ‘mustashe’ not covered in the basic example above:

1. ‘mustashe’ “remembers” the code passed to stash() and will re-evalute the code if it has changed.
2. Dependencies can be explicitly linked to the stashed object so that the code is re-evaluated if the dependencies change.

These two features are demonstrated below.

‘mustashe’ “remembers” the code

If the code that creates an object changes, then the object itself is likely to have changed. Thus, ‘mustashe’ “remembers” the code and re-evaluates the code if it has been changed. Here is an example, again using ‘tictoc’ to indicate when the code is evaluated.

tic()
stash("a", {
Sys.sleep(3)
a <- runif(5)
})

#> Stashing object.

toc()

#> 3.013 sec elapsed

tic()
stash("a", {
Sys.sleep(3)
a <- runif(10)
})

#> Updating stash.

toc()

#> 3.012 sec elapsed

However, ‘mustashe’ is insensitive to changes in comments and other style-based adjustments to the code. In the next example, a comment has been added, but we see that the object is loaded from the stash.

stash("a", {
Sys.sleep(3)
# Here is a new comment.
a <- runif(10)
})

#> Loading stashed object.

And below is the code from a horrible person, but ‘mustashe’ still loads the object from the stash.

# styler: off
stash("a", {
Sys.sleep( 3 )
# Here is a comment.
a=runif( 10 ) # Another comment
})

#> Loading stashed object.

# styler: on

Dependencies

Dependencies can be explcitly linked to an object to make sure that if they change, the stashed object is re-evaluated. “Dependency” in this case could refer to data frames that are used to create another (e.g. summarising a data frame’s columns), inputs to a function, etc.

The following demonstrates this with a simple example where x is used to calculate y. By passing "x" to the depends_on argument, when the value of x is changed, the code to create y is re-evaluated

x <- 1
stash("y", depends_on = "x", {
y <- x + 1
})

#> Stashing object.

# Value of `y`
y

#> [1] 2

The second time this is run without changing x, the value for y is loaded from the stash.

stash("y", depends_on = "x", {
y <- x + 1
})

#> Loading stashed object.

However, if we change the value of x, then the code is re-evaluated and the stash for y is updated.

x <- 100
stash("y", depends_on = "x", {
y <- x + 1
})

#> Updating stash.

# Value of `y`
y

#> [1] 101

Multiple dependencies can be passed as a vector to depends_on.

stash("y", depends_on = c("x", "a"), {
y <- x + a
})

#> Updating stash.

Unstashing and clearing stash

To round up the explanation of the ‘mustashe’ package, the stash can be cleared using unstash() and specific stashes can be removed using unstash().

unstash("a")

#> Unstashing 'a'.

clear_stash()

#> Clearing stash.

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.