facet bold titles in ggplotly

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Summary

A user reported that when converting a ggplot2 object with bold facet strip text to a ggplotly object using the plotly library, the bold styling is lost. The root cause is that ggplot2 and plotly use different rendering engines and styling models. ggplot2 applies element_text(face = "bold") to the plot theme, but the ggplotly() converter does not fully translate this specific CSS-like property into the plotly.js layout schema. To resolve this, users must manually update the ggplotly object’s layout properties using layout() to apply the specific font weight expected by the web renderer.

Root Cause

The technical root cause is a rendering engine mismatch and incomplete property mapping during the conversion process.

  • ggplot2 Theme System: ggplot2 uses a grid graphics system where text properties like face = "bold" are defined as part of the plot’s theme object. This is a static definition for an image-based output.
  • ggplotly Conversion: The ggplotly() function parses the ggplot object and attempts to map its components to plotly.js JSON schema. However, the conversion logic for theme(strip.text = element_text(face = "bold")) does not consistently map to the layout object’s annotations or axis title font properties.
  • CSS vs. Integer Mismatch: plotly.js (a web-based library) often expects font weights as integer values (e.g., 700 for bold) or string literals (“bold”), but the converter may leave the font weight as the default (usually normal or 400), effectively ignoring the ggplot instruction.

Why This Happens in Real Systems

This scenario is a classic abstraction layer leak.

  • Divergent Evolution: ggplot2 is a static visualization library based on the “Grammar of Graphics,” optimized for print and PDF. plotly is a dynamic, interactive web visualization library. While ggplotly provides a convenient bridge, it cannot map 100% of the source library’s features perfectly.
  • Theming Complexity: ggplot2 themes are highly customizable. Supporting every possible combination of element_text properties in the plotly conversion engine requires significant maintenance. Niche or specific properties (like font face) are often the first to break or be omitted to ensure the core data visualization remains intact.
  • Interactivity Trade-offs: To maintain interactivity (tooltips, zooming), the plot must be rebuilt in the browser using plotly.js. Reconstructing the exact visual style of a static R graphic requires explicit instructions in the plotly object that the converter sometimes misses.

Real-World Impact

  • Visual Inconsistency: In professional reports or dashboards (e.g., Shiny apps), mixed font weights break visual hierarchy. Users rely on bolding to distinguish categories; losing this reduces readability.
  • Polishing Friction: Engineers waste time debugging why a standard ggplot2 theme argument isn’t working, often searching for a “magic parameter” rather than realizing the renderer needs a manual override.
  • Dashboard Degradation: If a pipeline relies on ggplotly for interactive web deployment, the loss of theme fidelity forces a choice between abandoning interactivity or writing extensive patch code to restore visual styling.

Example or Code

The following code reproduces the issue and demonstrates the manual fix required to restore bold text.

library(plotly)
library(tidyverse)

# 1. Create the base ggplot with bold strip text
p %
  pivot_longer(cols = -Species) %>%
  group_by(Species, name) %>%
  reframe(total = sum(value)) %>%
  ggplot(aes(x = Species, y = total)) +
  geom_col() +
  facet_wrap(~name) +
  theme(strip.text = element_text(face = "bold"))

# 2. Convert to ggplotly (The bold style is lost here)
pg <- ggplotly(p)

# 3. The Fix: Manually update the layout to enforce bold font
# We iterate through the layout annotations (which include facet titles)
# and set the font weight explicitly.
for (i in seq_along(pg$x$layout$annotations)) {
  pg$x$layout$annotations[[i]]$font$weight <- "bold"
}

# View the result
pg

How Senior Engineers Fix It

Senior engineers understand that connectors are rarely perfect. They address this by treating the conversion output as an object to be patched, not a finished product.

  • Programmatic Patching: Instead of clicking through a GUI, they write a wrapper function (like the for loop above) that inspects the resulting plotly object and enforces corporate style guides (fonts, colors, weights) programmatically.
  • Using plotly Native Syntax: They bypass the ggplot2 theme settings for specific elements that don’t convert well and apply the styling directly via layout() or style() calls on the ggplotly object.
  • Validation Checks: In a production pipeline, they add assertions to check if the final object contains the expected styling attributes (e.g., checking that font.weight is not NULL or 100).

Why Juniors Miss It

Juniors often assume that libraries in the same ecosystem (R/tidyverse) will talk to each other flawlessly.

  • Expectation of Parity: They believe that if theme(strip.text = ...) works in ggplot, it must work in ggplotly. They often try to solve it by adding more ggplot arguments (e.g., face = "bold.italic") rather than looking at the intermediate object.
  • Lack of Object Inspection: Juniors rarely inspect the structure of the pg$x$layout$annotations list. They see a “black box” rather than a list structure that can be manipulated.
  • Documentation Blindness: They look for a specific ggplotly argument to handle bold text (which doesn’t exist robustly) rather than understanding that the fix requires modifying the underlying plotly object structure.