Loading Workflow Objects

Last updated on 2024-12-13 | Edit this page

Overview

Questions

  • Where does the workflow happen?
  • How can we inspect the objects built by the workflow?

Objectives

  • Explain where targets runs the workflow and why
  • Be able to load objects built by the workflow into your R session

Where does the workflow happen?

So we just finished running our first workflow. Now you probably want to look at its output. But, if we just call the name of the object (for example, penguins_data), we get an error.

R 

penguins_data

ERROR 

Error: object 'penguins_data' not found

Where are the results of our workflow?

We don’t see the workflow results because targets runs the workflow in a separate R session that we can’t interact with. This is for reproducibility—the objects built by the workflow should only depend on the code in your project, not any commands you may have interactively given to R.

Fortunately, targets has two functions that can be used to load objects built by the workflow into our current session, tar_load() and tar_read(). Let’s see how these work.

tar_load()

tar_load() loads an object built by the workflow into the current session. Its first argument is the name of the object you want to load. Let’s use this to load penguins_data and get an overview of the data with summary().

R 

tar_load(penguins_data)
summary(penguins_data)

OUTPUT 

   species          bill_length_mm  bill_depth_mm
 Length:342         Min.   :32.10   Min.   :13.10
 Class :character   1st Qu.:39.23   1st Qu.:15.60
 Mode  :character   Median :44.45   Median :17.30
                    Mean   :43.92   Mean   :17.15
                    3rd Qu.:48.50   3rd Qu.:18.70
                    Max.   :59.60   Max.   :21.50

Note that tar_load() is used for its side-effect—loading the desired object into the current R session. It doesn’t actually return a value.

tar_read()

tar_read() is similar to tar_load() in that it is used to retrieve objects built by the workflow, but unlike tar_load(), it returns them directly as output.

Let’s try it with penguins_csv_file.

R 

tar_read(penguins_csv_file)

OUTPUT 

[1] "/home/runner/.local/share/renv/cache/v5/linux-ubuntu-jammy/R-4.4/x86_64-pc-linux-gnu/palmerpenguins/0.1.1/6c6861efbc13c1d543749e9c7be4a592/palmerpenguins/extdata/penguins_raw.csv"

We immediately see the contents of penguins_csv_file. But it has not been loaded into the environment. If you try to run penguins_csv_file now, you will get an error:

R 

penguins_csv_file

ERROR 

Error: object 'penguins_csv_file' not found

When to use which function

tar_load() tends to be more useful when you want to load objects and do things with them. tar_read() is more useful when you just want to immediately inspect an object.

The targets cache

If you close your R session, then re-start it and use tar_load() or tar_read(), you will notice that it can still load the workflow objects. In other words, the workflow output is saved across R sessions. How is this possible?

You may have noticed a new folder has appeared in your project, called _targets. This is the targets cache. It contains all of the workflow output; that is how we can load the targets built by the workflow even after quitting then restarting R.

You should not edit the contents of the cache by hand (with one exception). Doing so would make your analysis non-reproducible.

The one exception to this rule is a special subfolder called _targets/user. This folder does not exist by default. You can create it if you want, and put whatever you want inside.

Generally, _targets/user is a good place to store files that are not code, like data and output.

Note that if you don’t have anything in _targets/user that you need to keep around, it is possible to “reset” your workflow by simply deleting the entire _targets folder. Of course, this means you will need to run everything over again, so don’t do this lightly!

Key Points

  • targets workflows are run in a separate, non-interactive R session
  • tar_load() loads a workflow object into the current R session
  • tar_read() reads a workflow object and returns its value
  • The _targets folder is the cache and generally should not be edited by hand