Drake God 39;s Plan

I started to use {drake} for a data production pipeline. The raw data I work with is quite large and is split up into ~130 separate (Stata) files. Thus, each file should be processed separately. In order to keep it readable, I use target(), transform() and map() to specify my plan. This looks similar to the code below:

From How to combine multiple drake targets into a single cross target without combining the datasets? I got the idea of predefining a grid, which actually works as suggested. But since I do only need a vector, not a complex grid, this looks like over-engineering to me.

Drake God 39;s Plan

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When you use target(transform = ...), it is always a best to visualize the plan before you feed it to make(). It could take a couple iterations to get it right. Here is what your current plan looks like.

Most data analysis workflows consist of several steps, such as data cleaning, model fitting, visualization, and reporting. A drake plan is the high-level catalog of all these steps for a single workflow. It is the centerpiece of every drake-powered project, and it is always required. However, the plan is almost never the first thing we write. A typical plan rests on a foundation of carefully-crafted custom functions.

A drake plan is a data frame with columns named target and command. Each row represents a step in the workflow. Each command is a concise expression that makes use of our functions, and each target is the return value of the command. (The target column has the names of the targets, not the values. These names must not conflict with the names of your functions or other global objects.)

drake automatically understands the relationships among targets in the plan. It knows data depends on raw_data because the symbol raw_data is mentioned in the command for data. drake represents this dependency relationship with an arrow from raw_data to data in the graph.

drake supports custom formats for saving and loading large objects and highly specialized objects. For example, the "fst" and "fst_tbl" formats use the fst package to save data.frame and tibble targets faster. Simply enclose the command and the format together with the target() function.

There are several formats, each with their own system requirements. These system requirements, such as the fst R package for the "fst" format, do not come pre-installed with drake. You will need to install them manually.

drake has special functions to declare relationships between targets and external storage on disk. file_in() is for input files and directories, file_out() is for output files and directories, and knitr_in() is for R Markdown reports and knitr source files. If you use one of these functions inline in the plan, it tells drake to rerun a target when a file changes (or any of the files in a directory).

As for knitr_in(), recall what happened when we changed the create_plot(). Not only did hist rerun, report ran as well. Why? Because knitr_in() is special. It tells drake to look for mentions of loadd() and readd() in the code chunks. drake finds the targets you mention in those loadd() and readd() calls and treats them as dependencies of the report. This lets you choose to run the report either inside or outside a drake pipeline.

file_in(), file_out(), and knitr_in() require you to mention file and directory names explicitly. You cannot use a variable containing the name of a file. The reason is that drake detects dependency relationships with static code analysis. In other words, drake needs to know the names of all your files ahead of time (before we start building targets in make()). Here is an example of a bad plan.

drake >= 7.11.0 supports dynamic files through a specialized format. With dynamic files, drake can watch local files without knowing them in advance. This is a more flexible alternative to file_out() and file_in(), and it is fully compatible with dynamic branching.

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However this syntax loses the dependency of merged_data on file1.csv and file2.csv. In high brow terms, we call source() for its side effects of creating files, declaring functions (and drake_example("main") is arguably guilty of that too as one has to call functions.R exclusively for that sort of a side effect).

In traditional workflows, your code is a bunch of declarative scripts. But in drake, your scripts should mostly load packages and custom functions. In other words, most of your scripts prepare to do the work rather than actually doing the work directly.

So before invoking drake magic, I would still need to run all of the R/*.R files (or wrap everything into a package so all of them get loaded on autopilot). I am sure your thought was put into NOT making these declarative scripts a part of drake plan; would that create circular dependencies, or are there other reasons?

Yes, drake::make() looks at the packages and functions in the current session, and it assumes you already ran all the setup scripts. Same goes for predict_runtime(), vis_drake_graph(), outdated(), and pretty much anything with a config or envir argument. Other functions like loadd() and readd() just need the cache, so they do not usually need to source() any setup scripts.

In Makefiles, targets and dependencies are files. But in drake, the dependencies are not scripts, but rather the functions and variables produced by the scripts. This may seem counterintuitive for people who are already familiar with pipeline tools, but it is a deliberate design choice, and it is part of what it means for drake to focus on R.

So yes, R scripts should not be invoked in the plan itself. If a script creates new variables, running it as part of a target could create new/malformed dependency relationships well after drake thinks it has already figured out what to run when. Also, drake does not dive into file_in() files to hunt for dependencies, so it is likely to miss dependencies you mention in those scripts. Circularity is also a possibility.

Good idea. People have certainly done this, and I do encourage it. However, it requires extra care. A package creates its own environment to put functions and data objects, so if you write a drake workflow as a formal R package, you will need to call expose_imports() to make those functions available to drake's dependency detection system.

My primary project build background is Stata project ( ). Somewhat like make, it's dependency tracking principle is tracking files. Unlike make or drake, Stata project does not have a concept of goals. Instead, there is a master script that calls other scripts with Stata's analogue of source(), and it is each script's responsibility to declare its own dependencies with Stata's syntax analogue of project::creates( filename ), project::uses( filename ) or project::original( filename ). The project metadata, somewhat similar to the drake_config() object, is the set of dependencies (which file is being created by which script, and which files down the pipeline use it) and statuses (file dates, sizes and hash sums). So when project::build() is invoked, project loads the master script, checks which of the slave scripts have had their source code changed, or have had their input data dependencies changed, and reruns only the modified parts (or new scripts freshly added to the master).

2 GB each should work, but it will take time to serialize all that data. In make(verbose = 6), you will get messages that compare execution time to overall processing time. If you find that drake's cache takes too long, feel free to work around it with file_in() and file_out(). I intend to continue work on improving cache speed.

Yes, @krlmlr has contributed great teaching materials, as well as some of the best ideas in drake's API and high-performance computing functionality. You can thank him for conceiving of file_in(), file_out(), and knitr_in().

@krlmlr I thought that Stata thingy worked fine for my purposes. (Mind you, this is not an official Stata command, but a really obscure third-party package... as obscure as library(drake).) At the very least, I was able to combine what that package offered with other tricks and tools to work for me. I think the distinction from darn is that Stata scripts cannot properly process the project package functionality, which is only available when the package is being built. At that stage, everything is under the control of project, and it knows which directory it is now in, and what script it is currently running. So the dependencies cannot be updated by running isolated scripts, which seems possible with darn. There are minor issues when timestamps are printed by default in the text output files by some of the external programs, etc., so you need to know what the workarounds are. But generally it fits well within Stata procedural language concept.

Drake's presence in the wide array of charity has been a point of contention for some. Giving a lot, some people have pointed out, is always great, but giving while boasting about giving can change the narrative. And in some cases, Drake himself seems to be part of the gift, with shots of him sneaking up and bringing a fan to immediate tears presented just as lovingly as when he hands the same shocked fan a wad of money. On Ebro in the Morning, DJ Peter Rosenberg called the video into question explicitly, arguing that Drake's charity seemed to serve him a little too much, which didn't line up with their shared tenets of Judaism. On Twitter, fans held similar arguments, suggesting that filming the acts of good will diminished them. In all cases one fact remains true: no one really knows God's plan, not even Drake.

Positive integer, optional.max_expand is the maximum number of targets to generate in eachmap(), split(), or cross() transform.Useful if you have a massive plan and you want totest and visualize a strategic subset of targetsbefore scaling up.Note: the max_expand argument of drake_plan() andtransform_plan() is for static branching only.The dynamic branching max_expandis an argument of make() and drake_config(). e24fc04721