How to Choose Variable to Display in Tooltip When Using ggplotly in R
Interactive visualizations are a powerful way to explore and present data and plotly
are one of R's most popular libraries for creating these interactive plots. When combined plotly
with ggplot2
using ggplotly
, you can convert static ggplot2
plots into interactive ones. One key feature ggplotly
is the ability to display tooltips, which provide additional information when hovering over data points. This article will guide you on choosing and customizing the variable displayed in tooltips using ggplotly
R Programming Language.
Understanding ggplotly
and Tooltips
ggplotly
is a function of the plotly
package that converts a ggplot2
plot into an interactive plotly
plot. In an interactive plot, tooltips are small boxes that appear when you hover over a data point, displaying information about that point. By default, ggplotly
will display information about all the aesthetics mapped in the ggplot2
plot, such as x
, y
, and any additional aesthetics color
or size
. However, you might want to customize the tooltip to display specific variables, whether or not they are mapped to aesthetics.
Basic Usage of ggplotly
with Tooltips
Let's start with a simple example of a ggplot2
scatter plot and see how ggplotly
handles tooltips by default.
library(ggplot2)
library(plotly)
# Create a simple scatter plot
p <- ggplot(mtcars, aes(x = wt, y = mpg, color = cyl)) +
geom_point()
# Convert to an interactive plotly plot
p_plotly <- ggplotly(p)
# Display the plot
p_plotly
Output:

In this example, the tooltip displays the x
, y
, and color
variables (wt
, mpg
, and cyl
). While this is useful, you might want to include additional variables or exclude some of the default ones.
Customizing Tooltips in ggplotly
To customize the tooltips in ggplotly
, you can use the tooltip
argument within the ggplotly()
function. The tooltip
argument allows you to specify which variables you want to display.
# Create a scatter plot with custom text labels for tooltips
p_custom_label <- ggplot(mtcars, aes(x = wt, y = mpg, color = cyl)) +
geom_point(aes(text = paste("MPG:", mpg, "<br>HP:", hp))) +
geom_text(aes(label = paste("HP:", hp)), hjust = -0.1, vjust = 1.5)
# Convert to an interactive plotly plot
p_plotly_custom_label <- ggplotly(p_custom_label, tooltip = "text")
# Display the plot
p_plotly_custom_label
Output:

In this example, we use geom_point()
with the text
aesthetic to create custom labels that include both mpg
and hp
. These labels appear in the tooltip when hovering over data points.
Practical Considerations
- Tooltip Content Length: Keep tooltips concise. Too much information can make the tooltip cluttered and hard to read.
- Interactivity: Remember that
ggplotly
may slow down if your plot has many data points or complex customizations. Consider simplifying tooltips in such cases. - Cross-platform Consistency: Tooltips may render differently across platforms or browsers. Always test your interactive plots in the environment where they will be viewed.
Conclusion
Customizing the variables displayed in tooltips when using ggplotly
in R can greatly enhance the interactivity and usefulness of your plots. By selecting specific variables, adding unmapped variables, and even creating custom text labels, you can tailor the tooltip content to meet the needs of your analysis or presentation. Understanding how to manipulate these aspects of ggplotly
allows you to create more informative and engaging visualizations.