ggplotly Facet Titles: How to Make Them Bold?

• ⚙️ ggplotly() strips much of the ggplot styling due to translation from R to JavaScript.
• 💡 Inline HTML tags like <b> are parsed by Plotly to style facet titles.
• 🛠 Workarounds like subplot() and manual annotations allow complete control over facet appearance.
• 🚫 CSS and theme customizations don’t carry over when converting ggplot to ggplotly.
• 🔄 Reusable R functions help automate HTML tagging of ggplotly facet labels.

Making interactive charts with ggplotly() is a good way to bring your static ggplot2 pictures to life. But, many R users get frustrated because styling gets lost, especially when they try to make facet titles bold. This guide shows you practical steps to fix this problem and make your visual stories better with stronger, bolder facet titles.

How Facets in ggplot2 Work

Facets in ggplot2 offer a simple way to see many relationships. They do this by breaking a plot into smaller parts based on different categories. These parts, or panels, are made with the facet_wrap() and facet_grid() functions. Each panel shows a piece of your data. This is a good way to find trends, compare categories, and tell richer data stories.

Here is a basic example:

p <- ggplot(data, aes(x, y)) +
  geom_point() +
  facet_wrap(~category)

When you make these types of faceted panels in a static ggplot object, you have full control over how the facet strip looks. This is the title at the top or side of each panel. You can make the strip text bold using theme() adjustments.

theme(strip.text = element_text(face = "bold"))

This works perfectly with static plots. But, when you change to an interactive Plotly graph using ggplotly(), these styles usually disappear.

Why ggplotly() Does Not Keep Facet Styling

ggplotly() works by changing your ggplot object into a Plotly graph. It does this through an internal process that matches ggplot's way of drawing graphics to Plotly's JSON system. This is a best-effort change, which means some features are partly changed or left out completely.

Here are the main reasons for lost styling:

• 🎯 HTML vs Grid: ggplot2 uses grid graphics in R, but Plotly shows things in HTML/JavaScript.
• 🧠 Different Drawing Systems: ggplot2 builds graphic parts (grobs) using R’s own drawing system. Plotly, however, expects an HTML structure.
• ❌ Theme Parts Not Supported: Plotly often does not understand custom ggplot themes, like element_text(), during the change.

Carson Sievert (2020) writes in Interactive Web-Based Data Visualization with R, Plotly, and Shiny that “ggplotly() tries to match ggplot2 objects one-to-one. But complex plots or themes often lose quality because of the limits of changing native R graphics to web-based JavaScript.”

So, ggplotly() is a useful link, but it is not perfect.

Plan: Bold ggplotly Facet Titles with HTML Tags

The best and most common way to fix this is to use inline HTML tags. Specifically, you use <b> for bold text inside the facet labels. Plotly knows and shows basic HTML tags inside text parts. This means you can add bold formatting right into your facet titles.

To do this, change your original data's factor labels before you make the plot:

df$group <- factor(df$group,
                   levels = c("A", "B"),
                   labels = c("<b>Group A</b>", "<b>Group B</b>"))

When you pass these labels to facet_wrap() or facet_grid(), Plotly will show them using HTML once ggplotly() changes them.

A Simple Working Example

Here is a complete example that shows how to use the HTML-label method:

library(ggplot2)
library(plotly)

# Sample data
df <- data.frame(
  category = rep(c("A", "B"), each = 10),
  x = rnorm(20),
  y = rnorm(20)
)

# Modify facet labels using inline HTML
df$category <- factor(df$category, labels = c("<b>Group A</b>", "<b>Group B</b>"))

# Static ggplot
p <- ggplot(df, aes(x, y)) +
  geom_point() +
  facet_wrap(~category)

# Convert to interactive plot
ggplotly(p)

Once plotted, the ggplotly() object’s facet strips will have bold labels. This is different from the usual behavior, which removes the bold styling.

How Plotly Reads HTML for Labels

Plotly’s drawing system uses HTML5 to show labels, tooltips, and notes. Static R plots usually create image files, but Plotly treats text parts as HTML strings. If a string includes HTML tags like <b>, it will show up that way. Other tags it supports include:

• <b> (bold)
• <i> (italic)
• <br> (line break)
• <span> (some limits on what you can do)

⚠️ Important things to know:

• Only basic inline tags work.
• Complex parts (like <div> or outside style sheets) will not work.
• Saving to static images using orca() or webshot() might remove or flatten styling.

This HTML reading lets you not only bold ggplotly labels but also improve tooltips and legends.

More Control: Use subplot() and Annotations

For users who need to avoid ggplot entirely or make custom layouts with detailed notes, plotly::subplot() offers another option. This gives full control over how things look, but it takes more work up front.

Example:

library(plotly)

fig1 <- plot_ly(data = subset(df, category == "<b>Group A</b>"),
                x = ~x, y = ~y, type = 'scatter', mode = 'markers',
                name = "Group A")

fig2 <- plot_ly(data = subset(df, category == "<b>Group B</b>"),
                x = ~x, y = ~y, type = 'scatter', mode = 'markers',
                name = "Group B")

subplot(fig1, fig2, nrows = 1, margin = 0.05) %>%
  layout(annotations = list(
    list(text = "<b>Group A</b>", x = 0.2, y = 1.05, showarrow = FALSE, xref='paper', yref='paper'),
    list(text = "<b>Group B</b>", x = 0.8, y = 1.05, showarrow = FALSE, xref='paper', yref='paper')
  ))

As you can see, each plot is added by hand, and the facet strip is made with notes.

Pros:

• Complete control over placement
• Custom font, color, and direction

Cons:

• More work; you have to handle axes and shared legends
• Code gets harder to manage for many facets

Looking at the Plot Structure with JSON

Another tool you can use is plotly_json(). This shows you how your Plotly object is built in JSON format. From here, you can check and change notes, font styles, and layout parts with great care.

library(plotly)
plotly_json(ggplotly(p))

Once you find what you want to change, use layout() to change these parts:

ggplotly(p) %>%
  layout(
    annotations = list(
      font = list(family = "Arial", size = 14, color = "black")
    )
  )

layout() does not directly change facet strips, but it gives you deep control over any text on top. This includes fake strip titles you create by hand.

Quick Helpers: Functions for HTML Formatting

Avoid writing the same code over and over by making helper functions:

make_facet_bold <- function(factor_var) {
  factor(paste0("<b>", factor_var, "</b>"))
}

How to use it:

df$category <- make_facet_bold(df$category)

This method helps automate formatting for many charts in Shiny dashboards or reports with parameters.

Other Ways to Use Bold Formatting in ggplotly

Inline HTML tags can make more than just ggplotly facet strip titles better. Here are other places to use them:

• Bold x and y-axis titles with labs(x = "<b>X-axis</b>")
• Stronger titles in ggtitle("<b>My Plot</b>")
• Styled legend items (works partly)
• Tooltips with <b>Important</b>: Value = 45

These changes add order and focus to your visuals, which makes them easier to understand.

Use Cases: Where This Helps Most

Faceted charts are very useful in areas like:

• 📊 Business dashboards that group key numbers or sales figures
• 🧬 Gene studies where each gene gets its own panel
• 💡 A/B test analysis for interface design
• 📚 Education tools that break content into chapters or units

In these dashboards, bolded facet titles help users quickly see and move between panels.

Making Plots with plot_ly() From Scratch (Advanced)

If you need more than what ggplotly() can do—like full control over spacing, font display, or interactivity—it might be worth building directly in plot_ly().

plot_ly(data = df, x = ~x, y = ~y, color = ~category,
        type = 'scatter', mode = 'markers')

This needs more specific code and clear control. But, it means you avoid the limits of ggplotly() when it comes to styling.

Pros and Cons of the HTML Workaround

Final Thoughts

Faceting is a key strength of ggplot2. And when you add interactivity with ggplotly(), it leads to active, useful dashboards. But by default, you lose important custom styles, especially for facet titles.

By putting HTML like <b> into factor labels, you can bring back and even add to those styles. This makes your chart’s message stronger. Also, using subplot(), layout annotations, or fully custom plot_ly() calls gives even more options for important presentations.

With these HTML tips, layout functions, and helper methods, you can now fully control your ggplotly facet titles. You can make sure your facet strips stand out and every label adds meaning to your story.

References

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. CRC Press.
https://plotly-r.com/ggplotly.html