It does these things without requiring major upfront investments in the way of code functionalisation or the like. Indeed, one will not ordinarily need to modify one’s existing code at all in order to implement a makepipe pipeline.

Converting an existing workflow

Assuming your workflow consists of a series of R scripts – one.R, two.R, etc. – you can construct a makepipe Pipeline simply by sourcing them using make_with_source().

You’ll just need to supply, along with the path to the source script, a set of targets (i.e. paths to files that the script is supposed to make) and optionally a set of dependencies (i.e. paths to files that the script needs so as to make the targets).

For example, you’ll create a pipeline.R script containing the following:

library(makepipe)

make_with_source(
  source = "one.R", 
  targets = "data/1 data.Rds", 
  dependencies = "data/raw.Rds"
)

make_with_source(
  source = "two.R", 
  targets = "data/2 data.Rds", 
  dependencies = c("data/1 data.Rds", "lookup/concordance.csv")
)

# etc.

Then, when this code is run through, each source file will be executed if and only if its targets are out-of-date with respect to its dependencies (and source file itself). This means that only those scripts which need to be run will be. So, without requiring you to think about it, you’ll be able to skip unnecessary computations.

Meanwhile, behind the scenes, each call to make_with_source() will add a Segment onto the Pipeline. These Segment objects keep track of the relationships between targets, dependencies and source files and they also log metadata relating to the execution of the source file such as whether it was executed on the last run and how long it took to execute.

You can inspect this metadata by getting ahold of the Pipeline. For example, you might see something like this:

pipe <- get_pipeline()
pipe$segments

#> [[1]]
#> # makepipe segment
#> 
#> ## one.R
#> 
#> * Source: 'one.R'
#> * Targets: 'data/1 data.Rds'
#> * File dependencies: 'data/raw.Rds'
#> * Executed: TRUE
#> * Execution time: 22.5 secs
#> * Result: 0 object(s)
#> * Environment: 0x00000253c8573268
#> 
#> [[2]]
#> # makepipe segment
#> 
#> ## two.R
#> 
#> * Source: 'two.R'
#> * Targets: 'data/2 data.Rds'
#> * File dependencies: 'data/1 data.Rds', 'lookup/concordance.csv'
#> * Executed: TRUE
#> * Execution time: 38.2 secs
#> * Result: 0 object(s)
#> * Environment: 0x00000253c8738660

Additionally, you can visualise the relationships between the various files by viewing the Pipeline itself:

show_pipeline()

This will display a flow chart exhibiting the relationships between the targets, dependencies, and code.

`make_*()`

We used make_with_source() above since, in most cases, that will be the simplest way to convert an existing workflow. In some cases, however, your pipeline may include short pieces of code that don’t need to reside in their own script. In such cases, you can use make_with_recipe():

make_with_recipe(
  recipe = rmarkdown::render(
        "report.Rmd",
        output_file = "output/report.html"
    ),
    targets = "output/report.html",
    dependencies = c("report.Rmd", "data/2 data.Rds")
)

As with make_with_source(), when a make_with_recipe() block is run the code (this time the recipe) will only be executed if the relevant targets are out-of-date with respect to their dependencies

roxygen

Instead of maintaining a separate pipeline script containing calls to make_with_source(), you can add roxygen-like headers to the .R files in your pipeline containing the @makepipe tag along with @targets, @dependencies, and so on. For example, at the top of script one.R you might have

#'@title One
#'@description This is the first script in our pipeline
#'@dependencies "data/raw.Rds"
#'@targets "data/1 data.Rds"
#'@makepipe
NULL

You can then call make_with_dir(), which will construct a pipeline using all the scripts it finds in the provided directory containing the @makepipe tag.

If you want to use a hybrid approach – keeping the documentation of dependencies and targets close to the source code – while maintaining the flexibility of a separate pipeline script you can use make_with_roxy(). Thus you might have

make_with_roxy("one.R")

# do other stuff

make_with_roxy("two.R")

# etc.

Clean and build

Once you’ve constructed a Pipeline by calling make_*(), you can re-run the entire pipeline using the build() method. As when using make_*() directly, only code that needs to be run will be when build() is called.

For example, if you’ve just executed the Pipeline and nothing has changed, then nothing will be re-executed and you’ll be told has much:

pipe <- get_pipeline()
pipe$build() 
#> √ Targets are up to date
#> √ Targets are up to date

If you want to start from scratch and ‘rebuild’ all targets, you can use the build() method together with the clean() method.

pipe$clean()
pipe$build()

#> i Targets are out of date. Updating...
#> √ Finished updating                   
#> i Targets are out of date. Updating...
#> √ Finished updating

The clean() and build() methods are especially useful when used with a Pipeline that has previously been saved out. In particular, if you’ve already created your Pipeline by stringing make_*() calls together and you’ve saved your Pipeline object out as pipeline.Rds you can re-run the whole Pipeline to ensure everything is up-to-date simply by calling:

pipe <- readRDS("pipeline.Rds")
pipe$build()

Return values and registration

Each Segment on the Pipeline is associated with a result. This is akin to a return value. Indeed, in the case of make_with_recipe() it is the return value of the recipe. For example:

res <- make_with_recipe(
    recipe = {
        saveRDS(mtcars, "data/mtcars.Rds")
        nrow(mtcars)
    },
    targets = "data/mtcars.Rds"
)
#> i Targets are out of date. Updating...
#> √ Finished updating                   

res$result
#> [1] 32

Note, however, that the result is captured when the recipe is executed. If your recipe is never executed, then there will be no result available. Thus, for instance:

res <- make_with_recipe(
    recipe = {
        saveRDS(mtcars, "data/mtcars.Rds")
        nrow(mtcars)
    },
    targets = "data/mtcars.Rds"
)
#> √ Targets are up to date

res$result
#> NULL

Things are a little more complicated in the case of make_with_source(), as you can imagine. Given that source scripts do not really have return values, the result cannot be what source returns when run. So what is it?

The result associated with a source Segment is an environment containing objects ‘registered’ in the source script. Objects are registered using make_register(), which has a similar API to base::assign(). Thus, imagine that three.R contains the following code:

# ...
makepipe::make_register(nrow(dat), "num_rows")
# ...

Then we will have:

res <- make_with_source(
    source = "three.R",
    targets = "data/3 data.Rds",
    dependencies = "data/2 data.Rds"
)
#> i Targets are out of date. Updating...
#> √ Finished updating 

res$result
#> <environment: 0x0000029f6840f610>

res$result$num_rows
#> [1] 32

As with make_with_recipe(), a result will only be captured if the source script is executed.

Execution details

So when does a source file or a recipe get executed? The answer is: when and only when the relevant targets are out-of-date with respect to the dependencies. But what does that mean? Specifically, the targets are out-of-date if and only if:

One or more of the targets do not exist, OR
One or more of the dependencies is newer (i.e. has a more recent file modification time) than one or more of the targets. In other words, the dependencies have been updated since the targets were last made.

By default the execution will take place in a fresh environment which is a child of the calling environment. So if you’re calling make_*() in a top-level script then the execution will take place in a fresh environment whose parent is the global environment.

There are a number of less commonly used arguments to make_*() which alter this behaviour. In particular:

packages can be used to supply the names of packages which serve as dependencies for the targets. If any of these packages have been updated since the targets were last made, the targets will be remade. This is particularly useful when you’re relying on a package for lookups which are liable to change.
envir can be used to supply an environment in which the execution of the source or recipe will take place. Supplying envir = base::globalenv(), for example, will mean that all execution takes place in the global environment. If you do this, then all the objects bound in the recipe/source will be available in the global environment.
force can be used to ensure that the recipe/source is executed no matter what. This is useful, e.g., when you are pulling in some data from an external database.