Best Practices for targets Project Organization

Last updated on 2024-03-12 | Edit this page

Overview

Questions

  • What are best practices for organizing targets projects?
  • How does the organization of a targets workflow differ from a script-based analysis?

Objectives

  • Explain how to organize targets projects for maximal reproducibility
  • Understand how to use functions in the context of targets

A simpler way to write workflow plans

The default way to specify targets in the plan is with the tar_target() function. But this way of writing plans can be a bit verbose.

There is an alternative provided by the tarchetypes package, also written by the creator of targets, Will Landau.

Installtarchetypes

If you haven’t done so yet, install tarchetypes with install.packages("tarchetypes").

The purpose of the tarchetypes is to provide various shortcuts that make writing targets pipelines easier. We will introduce just one for now, tar_plan(). This is used in place of list() at the end of the _targets.R script. By using tar_plan(), instead of specifying targets with tar_target(), we can use a syntax like this: target_name = target_command.

Let’s edit the penguins workflow to use the tar_plan() syntax:

R 

library(targets)
library(tarchetypes)
library(palmerpenguins)
library(tidyverse)

clean_penguin_data <- function(penguins_data_raw) {
  penguins_data_raw |>
    select(
      species = Species,
      bill_length_mm = `Culmen Length (mm)`,
      bill_depth_mm = `Culmen Depth (mm)`
    ) |>
    remove_missing(na.rm = TRUE) |>
    # Split "species" apart on spaces, and only keep the first word
    separate(species, into = "species", extra = "drop")
}

tar_plan(
  penguins_csv_file = path_to_file("penguins_raw.csv"),
  penguins_data_raw = read_csv(penguins_csv_file, show_col_types = FALSE),
  penguins_data = clean_penguin_data(penguins_data_raw)
)

I think it is easier to read, do you?

Notice that tar_plan() does not mean you have to write all targets this way; you can still use the tar_target() format within tar_plan(). That is because =, while short and easy to read, does not provide all of the customization that targets is capable of. This doesn’t matter so much for now, but it will become important when you start to create more advanced targets workflows.

Organizing files and folders

So far, we have been doing everything with a single _targets.R file. This is OK for a small workflow, but does not work very well when the workflow gets bigger. There are better ways to organize your code.

First, let’s create a directory called R to store R code other than _targets.R (remember, _targets.R must be placed in the overall project directory, not in a subdirectory). Create a new R file in R/ called functions.R. This is where we will put our custom functions. Let’s go ahead and put clean_penguin_data() in there now and save it.

Similarly, let’s put the library() calls in their own script in R/ called packages.R (this isn’t the only way to do it though; see the “Managing Packages” episode for alternative approaches).

We will also need to modify our _targets.R script to call these scripts with source:

R 

source("R/packages.R")
source("R/functions.R")

tar_plan(
  penguins_csv_file = path_to_file("penguins_raw.csv"),
  penguins_data_raw = read_csv(penguins_csv_file, show_col_types = FALSE),
  penguins_data = clean_penguin_data(penguins_data_raw)
)

Now _targets.R is much more streamlined: it is focused just on the workflow and immediately tells us what happens in each step.

Finally, let’s make some directories for storing data and output—files that are not code. Create a new directory inside the targets cache called user: _targets/user. Within user, create two more directories, data and results. (If you use version control, you will probably want to ignore the _targets directory).

A word about functions

We mentioned custom functions earlier in the lesson, but this is an important topic that deserves further clarification. If you are used to analyzing data in R with a series of scripts instead of a single workflow like targets, you may not write many functions (using the function() function).

This is a major difference from targets. It would be quite difficult to write an efficient targets pipeline without the use of custom functions, because each target you build has to be the output of a single command.

We don’t have time in this curriculum to cover how to write functions in R, but the Software Carpentry lesson is recommended for reviewing this topic.

Another major difference is that each target must have a unique name. You may be used to writing code that looks like this:

R 

# Store a person's height in cm, then convert to inches
height <- 160
height <- height / 2.54

You would get an error if you tried to run the equivalent targets pipeline:

R 

tar_plan(
    height = 160,
    height = height / 2.54
)

ERROR 

Error:
! Error running targets::tar_make()
  Error messages: targets::tar_meta(fields = error, complete_only = TRUE)
  Debugging guide: https://books.ropensci.org/targets/debugging.html
  How to ask for help: https://books.ropensci.org/targets/help.html
  Last error: duplicated target names: height

A major part of working with targets pipelines is writing custom functions that are the right size. They should not be so small that each is just a single line of code; this would make your pipeline difficult to understand and be too difficult to maintain. On the other hand, they should not be so big that each has large numbers of inputs and is thus overly sensitive to changes.

Striking this balance is more of art than science, and only comes with practice. I find a good rule of thumb is no more than three inputs per target.

Key Points

  • Put code in the R/ folder
  • Put functions in R/functions.R
  • Specify packages in R/packages.R
  • Put other miscellaneous files in _targets/user
  • Writing functions is a key skill for targets pipelines