CanvasXpress GGPlot2 Interface

Getting Started

For many R users, ggplot2 is the gold standard for creating elegant, publication-quality static plots. Its "grammar of graphics" provides a powerful and intuitive way to build complex visualizations layer by layer. But what if you could take those carefully crafted plots and make them fully interactive with just one extra line of code? The canvasXpress R package makes this possible.

This guide will show you how to enhance your existing ggplot2 workflow by converting your static plots into dynamic, web-ready charts with tooltips, zooming, and interactive legends.

Installation

If you haven't already, install both canvasXpress and ggplot2 from CRAN.

install.packages("canvasXpress")
install.packages("ggplot2")

Then, load both libraries to get started.

library(canvasXpress)
library(ggplot2)

How It Works: The canvasXpress Translator

The magic behind the conversion lies in the canvasXpress() function's built-in intelligence. When you pass a ggplot object to it, the function acts as a sophisticated translator. It automatically parses the structure of your plot, interpreting the various layers (like geom_point or geom_bar) and aesthetic mappings (such as aes(x=..., y=..., color=...)) you've defined. It then converts these elements into the specific JSON configuration required by the underlying CanvasXpress JavaScript engine, effectively rebuilding your static plot as a fully interactive HTML widget.

A Step-by-Step Example

Let's walk through the process with a simple example using the iris dataset.

1. Create Your ggplot2 Object

First, build a standard ggplot object just as you normally would. Here, we'll create a scatter plot of petal length versus petal width, colored by species.

# Create a standard ggplot2 object
my_gg_plot <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Species)) +
  geom_point(size = 3) +
  labs(title = "Iris Petal Length vs. Width") +
  theme_minimal()

2. The One-Liner Conversion

Now, simply pass the my_gg_plot object directly to the canvasXpress() function.

# Convert the ggplot object to an interactive CanvasXpress chart
canvasXpress(my_gg_plot)

Examples