Quarto Julia Plotly: Your Ultimate Debugging Guide 2025

# Check your Quarto version
quarto --version

# Check your Julia version
julia --version

using Pkg
Pkg.status(["IJulia", "PlotlyJS"])

quarto check jupyter

---
title: "My Awesome Report"
author: "Me"
format: html
julia:
  project: "/path/to/your/project"
---

In a Julia REPL or a Jupyter Notebook, if the last line of a cell is an object (like a plot), it gets displayed automatically. Quarto, in its quest for reproducibility and control, is stricter. It will only display what you explicitly tell it to.

The problem code:

using PlotlyJS
p = plot(rand(10), Layout(title="My Plot"))
# Nothing is returned, so Quarto shows nothing.

The solution: Make the plot object the last expression in the chunk. This makes it the return value, which Quarto then knows how to handle.

using PlotlyJS
p = plot(rand(10), Layout(title="My Plot"))
p # This line makes all the difference

If your code block is more complex, with logic after the plot is created, the `display()` function is your best friend. It's a forceful, unambiguous command: "Render this thing, right here, right now."

using PlotlyJS
p = plot(rand(10), Layout(title="My Plot"))
display(p) # Force the display
println("Plot has been generated.")

Chunk Options Matter

Quarto's code chunk options provide fine-grained control, but they can also be a source of confusion. For interactive plots like those from Plotly, the key option is often results: asis. This tells Quarto: "The output of this code is raw HTML/JS. Don't try to format it; just pass it straight through to the final document."

{julia}
#| results: asis
#| echo: false

using PlotlyJS
plot(rand(20, 3))

When you're troubleshooting a missing plot, try adding results: asis. If the plot appears, you know the issue was with how Quarto was trying to wrap the HTML output from PlotlyJS.jl.

Deciphering Cryptic Error Messages

When you do get an error, it can feel overwhelming. Let's break down where to look and what to look for.

In the Render Log: `MethodError`

When you run quarto render, your terminal becomes a firehose of information. Don't ignore it! A `MethodError: no method matching...` is a classic Julia error that often appears here. In a Plotly context, this usually means your data isn't the shape or type the plotting function expects. For example, trying to create a scatter plot with a 2D array instead of two 1D vectors, or passing data with `missing` values to a function that doesn't handle them.

Your weapon: Before your `plot()` call, inspect your data! Use `println(typeof(x_data))`, `println(size(y_data))`, and `println(any(ismissing, my_data))` to see what you're *really* passing to the function.

In the Browser: JavaScript Console Errors

This is the most underutilized debugging tool. Remember: PlotlyJS.jl in Julia generates JavaScript code that runs in your browser. If the Julia part works but the JavaScript part fails, the error won't be in your terminal; it'll be in your browser.

Render your document, open it in Chrome or Firefox, and press F12 to open Developer Tools. Click on the "Console" tab. Do you see any red text? Errors like `Uncaught TypeError: Cannot read properties of undefined` or `Invalid JSON` are goldmines. They tell you that the data structure Julia sent to the browser's Plotly.js library was malformed. This often points to an issue with a complex `Layout` configuration or an unusual data type that didn't serialize to JSON correctly.

Your Go-To Debugging Workflow

Feeling overwhelmed? Don't be. Just follow this systematic process.

Step 1: Isolate

Create a brand new `debug.qmd` file. In it, put only the bare minimum: one Julia code chunk with the simplest possible plot.

---
title: "Debug"
format: html
---

{julia}
using PlotlyJS
plot(rand(5))

Does it render?

• Yes: Your environment is fine. The problem is with the specific data, layout, or complexity of your original document's code.
• No: The problem is foundational. Go back to the first section of this guide and re-check your environment, versions, and kernel connection.

Step 2: Simplify

Assuming the minimal plot worked, copy your problematic code into `debug.qmd`. Now, start stripping it down. Using a complex `Layout`? Remove it. Plotting five traces? Reduce it to one. Using custom data? Replace it with `rand()`. The goal is to find the exact line or piece of data that causes the failure. Once you find it, you know precisely where to focus your attention.

Step 3: Inspect

Just before the line that fails, print everything. Check data types, sizes, and a few sample values. Are your vectors the same length? Do you have `NaN`s or `missing`s where you don't expect them? This step reveals mismatched assumptions between what you *think* your data looks like and what it *actually* is.

Step 4: Use the Debug Flag

For the toughest problems, Quarto has a powerful, verbose logging mode. It keeps all the intermediate files that are usually deleted after a successful render.

quarto render my_document.qmd --debug

This will create a folder full of logs and scripts. It's dense, but by searching for your code chunk and the word "error," you can often find a much more detailed traceback from Julia that was suppressed in the standard render view.

---

Debugging the Quarto-Julia-Plotly stack is a skill, and like any skill, it improves with practice. It moves from a frantic, random process to a calm, systematic one. Start with the environment, ensure you're explicitly displaying your plots, learn to love the browser console, and always be ready to isolate the problem. The reward—seamless, beautiful, interactive reports powered by one of the most exciting toolchains in data science—is more than worth the effort.