drakepkg

Cascadia R Conference 2019 Update: the slides from Tiernan Martin’s talk can be downloaded here: drakepkg-slides-cascadiarconf2019.pdf

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The goal of drakepkg is to demonstrate how a drake workflow can be organized as an R package.

Why do this? Because the package system in R provides a widely-adopted method of structuring, documenting, testing, and sharing R code. While most R packages are general purpose, this approach applies the same framework to a specific workflow (or set of workflows). It increases the reproducibility of a complex workflow without requiring users to recreate the workflow’s environment with a container image (although that approach is compatible with drakepkg - see januz/drakepkg).

The drakepkg package is experimental in nature and currently requires some inconvenient steps (see the drake manual - 7.4 Workflows as R packages); please use caution when applying this approach to your own work.

Installation

You can install the released version of drakepkg from its Github repository with:

devtools::install_github("tiernanmartin/drakepkg")

Usage

The following table shows how each feature of a drake workflow is made accessible within an R package:

drake	R Package
plans, commands	functions (R/*.R)
targets	stored in the cache (.drake/)
input files, output files	internal data (inst/intdata/*), external data (inst/extdata/*), images and documents (inst/documents/*)

The package comes with two example drake plans, both of which are loosely based on the main example included in the drake package:

1. An introductory plan: drakepkg::get_example_plan_simple()
2. A plan that involves downloading external data: drakepkg::get_example_plan_external()

The first plan looks like this:

library(drake)
get_example_plan_simple()
#> # A tibble: 5 x 2
#>   target     command                                                       
#>   <chr>      <expr>                                                        
#> 1 raw_data   readxl::read_excel(file_in("intdata/iris-internal.xlsx"))    ~
#> 2 ready_data dplyr::mutate(raw_data, Species = forcats::fct_inorder(Specie~
#> 3 hist       create_plot(ready_data)                                      ~
#> 4 fit        lm(Sepal.Width ~ Petal.Width + Species, ready_data)          ~
#> 5 report     write_html_report(hist, fit, knitr_in("documents/report-simpl~

Several commands used in the plan (e.g,create_plot(), write_report_simple()) are included as part of the drakepkg R package and so is the plan itself; the documentation for each of these functions can be accessed using R’s help() function (for example, help(get_example_plan_simple)).

Once you have installed and loaded drakepkg, you can reproduce the introductory plan’s workflow by performing the following steps:

1. Copy the package’s directories and source code files into your working directory with the copy_drakepkg_files() function
2. View the plan (get_example_plan_simple()) and then make it (make(get_example_plan_simple()))
3. Access the plan’s targets using drake functions like readd() or loadd()
4. View the html documents created by the workflow in the documents/ directory

# Step 1: copy the source code files into the working directory

copy_drakepkg_files()

# Step 2A: view the example plan

get_example_plan_simple()
#> # A tibble: 5 x 2
#>   target     command                                                       
#>   <chr>      <expr>                                                        
#> 1 raw_data   readxl::read_excel(file_in("intdata/iris-internal.xlsx"))    ~
#> 2 ready_data dplyr::mutate(raw_data, Species = forcats::fct_inorder(Specie~
#> 3 hist       create_plot(ready_data)                                      ~
#> 4 fit        lm(Sepal.Width ~ Petal.Width + Species, ready_data)          ~
#> 5 report     write_html_report(hist, fit, knitr_in("documents/report-simpl~

# Step 2B: make the example plan

make(get_example_plan_simple())
#> All targets are already up to date.

# Step 3: examine the plan's targets

readd(fit)
#> 
#> Call:
#> lm(formula = Sepal.Width ~ Petal.Width + Species, data = ready_data)
#> 
#> Coefficients:
#>       (Intercept)        Petal.Width  Speciesversicolor  
#>             3.236              0.781             -1.501  
#>  Speciesvirginica  
#>            -1.844

readd(hist)

This example and others are available in the package vignette (vignette('drakepkg')).