ggplot2 vs plotly: When to Use Each in R

Choosing between ggplot2 and plotly is one of the most common decisions R users face when building data visualizations. Both are powerful, but they serve different purposes and excel in different scenarios. This guide helps you pick the right tool for your project.

The Short Answer

Use ggplot2 for static publications. Use plotly for interactive web applications.

This is the simplest way to think about it, but the reality has more nuance. Both packages can create both static and interactive plots, but each has a primary strength that makes it the better choice for specific use cases.

ggplot2 has been the gold standard for static visualizations in R since 2005. plotly, which came to R from JavaScript, has become the go-to for interactive web-based visualizations. Understanding when to use each will save you time and produce better results.

What ggplot2 Does Well

ggplot2 follows the grammar of graphics—a layered approach where you build charts piece by piece. This declarative style makes it incredibly intuitive once you learn the syntax:

library(ggplot2)

ggplot(mpg, aes(displ, hwy, color = class)) +
  geom_point() +
  labs(title = "Fuel Efficiency by Engine Displacement",
       subtitle = "Each point is a car model",
       x = "Engine displacement (L)",
       y = "Highway miles per gallon") +
  theme_minimal()

When to Choose ggplot2

1. Academic papers and reports — Static images in PDF or PNG format
2. Publication-quality graphics — Fine-grained control over every visual element
3. Complex, multi-layered visualizations — Facets, annotations, custom themes
4. When you need the fastest rendering — ggplot2 is significantly faster for large datasets
5. Learning the grammar of graphics — The conceptual framework transfers to other tools

The strength of ggplot2 is its extensibility. Packages like gganimate for animations, patchwork for composing plots, and ggthemes for pre-built styles build on top of ggplot2 to handle almost any visualization need.

ggplot2 Limitations

The main limitation is interactivity. While you can make interactive ggplot2 visualizations using ggiraph or plotly::ggplotly(), the native output is static. This is perfect for print but problematic for web applications where users need to hover, zoom, and pan.

What plotly Does Well

plotly creates interactive HTML visualizations that work in web browsers, R Markdown documents, and Shiny applications. The same syntax that creates web graphics also works in R:

library(plotly)

plot_ly(mpg, x = ~displ, y = ~hwy, color = ~class, 
        type = "scatter", mode = "markers") %>%
  layout(title = "Fuel Efficiency by Engine Displacement",
         xaxis = list(title = "Engine displacement (L)"),
         yaxis = list(title = "Highway miles per gallon"))

When to Choose plotly

1. Web applications — Shiny apps, dashboards, interactive reports
2. Exploratory data analysis — Hover tooltips save clicks
3. Sharing visualizations online — Anyone can interact without R
4. 3D visualizations — plotly handles 3D scatter and surface plots natively
5. When stakeholders need to explore data — Interactive zooming and panning

The interactivity is the killer feature. When you need users to hover over points to see exact values, filter data by clicking legend items, or zoom into dense regions, plotly delivers this out of the box.

plotly Limitations

Performance can be an issue with very large datasets. Each interactive element adds JavaScript overhead, and rendering thousands of points in the browser takes time. Customizing every visual element is also more complex than in ggplot2—the trade-off for interactivity is sometimes less precise control.

Converting Between Them

Here is the secret most R users do not know: you do not have to choose one. The ggplotly() function converts ggplot2 visualizations to interactive plotly with a single function call:

library(plotly)

p <- ggplot(mpg, aes(displ, hwy, color = class)) +
  geom_point() +
  theme_minimal()

ggplotly(p)

This gives you the best of both worlds—use ggplot2’s intuitive grammar to build the visualization, then convert to plotly for interactivity when needed. The trade-off is that not all ggplot2 features transfer perfectly, so test your converted plots carefully.

Performance Comparison

For static plots with large datasets, ggplot2 is noticeably faster. A 2025 benchmark from Jumping Rivers found that plotly’s plot_ly() can be slower than ggplotly(), which itself adds conversion overhead. If rendering speed matters for your pipeline, stick with pure ggplot2 for static outputs.

For interactive plots, the difference is less noticeable to users. Both render quickly for datasets under 10,000 points. Beyond that, consider downsampling or using interactive widgets that load data on demand.

What About the Grammar?

If you learn ggplot2’s grammar of graphics, you are building a transferable skill. The concepts—mapping data to visual aesthetics, layering geoms, specifying scales—appear in other tools. plotly has its own syntax that is specific to the plotly ecosystem.

This is worth considering if you work across multiple languages. The Python plotly library uses nearly identical syntax to R plotly. ggplot2 concepts also transfer to Python’s plotnine and Tableau’s visualization grammar.

Decision Framework

Use this quick reference to guide your choice:

Scenario	Recommendation
PDF report or academic paper	ggplot2
Shiny dashboard	plotly
Quick EDA in RStudio	Either works
Publication with precise styling	ggplot2
Sharing with non-R users	plotly
Animated visualizations	ggplot2 + gganimate
3D scatter plots	plotly
Thousands of points	ggplot2
Interactive tables + charts	plotly

See Also

• r-ggplot2-extensions — Extend ggplot2 with patchwork, ggrepel, and gganimate
• r-plotly-guide — Interactive visualizations with plotly in R
• data-viz-best-practices-r — Principles for effective data visualization