Reproducible Reports with Quarto

Last updated on 2024-09-24 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • How can we create reproducible reports?

Objectives

  • Be able to generate a report using targets

Episode summary: Show how to write reports with Quarto

Copy-paste vs. dynamic documents

Typically, you will want to communicate the results of a data analysis to a broader audience.

You may have done this before by copying and pasting statistics, plots, and other results into a text document or presentation. This may be fine if you only ever do the analysis once. But that is rarely the case—it is much more likely that you will tweak parts of the analysis or add new data and re-run your pipeline. With the copy-paste method, you’d have to remember what results changed and manually make sure everything is up-to-date. This is a perilous exercise!

Fortunately, targets provides functions for keeping a document in sync with pipeline results, so you can avoid such pitfalls. The main tool we will use to generate documents is Quarto. Quarto can be used separately from targets (and is a large topic on its own), but it also happens to be an excellent way to dynamically generate reports with targets.

Quarto allows you to insert the results of R code directly into your documents so that there is no danger of copy-and-paste mistakes. Furthermore, it can generate output from the same underlying script in multiple formats including PDF, HTML, and Microsoft Word.

Installing Quarto

As of v2022.07.1, RStudio comes with Quarto, so you don’t need to install it separately. If you can’t run Quarto from RStudio, we recommend installing the latest version of RStudio.

About Quarto files

.qmd or .Qmd is the extension for Quarto files, and stands for “Quarto markdown”. Quarto files invert the normal way of writing code and comments: in a typical R script, all text is assumed to be R code, unless you preface it with a # to show that it is a comment. In Quarto, all text is assumed to be prose, and you use special notation to indicate which lines are R code to be evaluated. Once the code is evaluated, the results get inserted into a final, rendered document, which could be one of various formats.

Quarto workflow
Quarto workflow

We don’t have the time to go into the details of Quarto during this lesson, but recommend the “Introduction to Reproducible Publications with RStudio” incubator (in-development) lesson for more on this topic.

Dynamic documents like Quarto (or Rmarkdown, the predecessor to Quarto) can actually be used to manage data analysis pipelines. But that is not recommended because it doesn’t scale well and lacks the sophisticated dependency tracking offered by targets.

Our suggested approach is to conduct the vast majority of data analysis (in other words, the “heavy lifting”) in the targets pipeline, then use the Quarto document to summarize and plot the results.

Report on bill size in penguins

Continuing our penguin bill size analysis, let’s write a report evaluating each model.

To save time, the report is already available at https://github.com/joelnitta/penguins-targets.

Copy the raw code from here and save it as a new file penguin_report.qmd in your project folder (you may also be able to right click in your browser and select “Save As”).

Then, add one more target to the pipeline using the tar_quarto() function like this:

R 

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

tar_plan(
  # Load raw data
  tar_file_read(
    penguins_data_raw,
    path_to_file("penguins_raw.csv"),
    read_csv(!!.x, show_col_types = FALSE)
  ),
  # Clean data
  penguins_data = clean_penguin_data(penguins_data_raw),
  # Build models
  models = list(
    combined_model = lm(
      bill_depth_mm ~ bill_length_mm, data = penguins_data),
    species_model = lm(
      bill_depth_mm ~ bill_length_mm + species, data = penguins_data),
    interaction_model = lm(
      bill_depth_mm ~ bill_length_mm * species, data = penguins_data)
  ),
  # Get model summaries
  tar_target(
    model_summaries,
    glance_with_mod_name(models),
    pattern = map(models)
  ),
  # Get model predictions
  tar_target(
    model_predictions,
    augment_with_mod_name(models),
    pattern = map(models)
  ),
  # Generate report
  tar_quarto(
    penguin_report,
    path = "penguin_report.qmd",
    quiet = FALSE,
    packages = c("targets", "tidyverse")
  )
)

The function to generate the report is tar_quarto(), from the tarchetypes package.

As you can see, the “heavy” analysis of running the models is done in the workflow, then there is a single call to render the report at the end with tar_quarto().

How does targets know when to render the report?

It is not immediately apparent just from this how targets knows to generate the report at the end of the workflow (recall that build order is not determined by the order of how targets are written in the workflow, but rather by their dependencies). penguin_report does not appear to depend on any of the other targets, since they do not show up in the tar_quarto() call.

How does this work?

The answer lies inside the penguin_report.qmd file. Let’s look at the start of the file:

MARKDOWN 

---
title: "Simpson's Paradox in Palmer Penguins"
format:
  html:
    toc: true
execute: 
  echo: false
---

```{r}
#| label: load
#| message: false
targets::tar_load(penguin_models_augmented)
targets::tar_load(penguin_models_summary)

library(tidyverse)
```

This is an example analysis of penguins on the Palmer Archipelago in Antarctica.

The lines in between --- and --- at the very beginning are called the “YAML header”, and contain directions about how to render the document.

The R code to be executed is specified by the lines between ```{r} and ```. This is called a “code chunk”, since it is a portion of code interspersed within prose text.

Take a closer look at the R code chunk. Notice the two calls to targets::tar_load(). Do you remember what that function does? It loads the targets built during the workflow.

Now things should make a bit more sense: targets knows that the report depends on the targets built during the workflow, penguin_models_augmented and penguin_models_summary, because they are loaded in the report with tar_load().

Generating dynamic content

The call to tar_load() at the start of penguin_report.qmd is really the key to generating an up-to-date report—once those are loaded from the workflow, we know that they are in sync with the data, and can use them to produce “polished” text and plots.

Challenge: Spot the dynamic contents

Read through penguin_report.qmd and try to find instances where the targets built during the workflow (penguin_models_augmented and penguin_models_summary) are used to dynamically produce text and plots.

  • In the code chunk labeled results-stats, statistics from the models like P-value and adjusted R squared are extracted, then inserted into the text with in-line code like `r mod_stats$combined$r.squared`.

  • There are two figures, one for the combined model and one for the separate model (code chunks labeled fig-combined-plot and fig-separate-plot, respectively). These are built using the points predicted from the model in penguin_models_augmented.

You should also interactively run the code in penguin_report.qmd to better understand what is going on, starting with tar_load(). In fact, that is how this report was written: the code was run in an interactive session, and saved to the report as it was gradually tweaked to obtain the desired results.

The best way to learn this approach to generating reports is to try it yourself.

So your final Challenge is to construct a targets workflow using your own data and generate a report. Good luck!

Key Points

  • tarchetypes::tar_quarto() is used to render Quarto documents
  • You should load targets within the Quarto document using tar_load() and tar_read()
  • It is recommended to do heavy computations in the main targets workflow, and lighter formatting and plot generation in the Quarto document