Data visualization is a bridge between raw data and actionable insights. While R offers numerous tools for creating beautiful plots, the leap from static graphics to interactive visualizations can dramatically improve understanding, storytelling, and engagement. Among R’s visualization libraries, Plotly stands out for its ability to transform ordinary plots into rich, interactive, and shareable experiences without requiring extensive knowledge of JavaScript, HTML, or CSS.
In this guide, we’ll explore Plotly in R — from its fundamentals to advanced interactive visualizations — providing practical examples you can implement immediately.
Why Plotly?
While popular libraries like ggplot2 excel at creating static charts, they have limitations when it comes to interactivity. ggvis addresses some of this but can be less performant and harder to customize for complex dashboards. This is where Plotly shines.
Key advantages of Plotly:
Enables interactive visualizations such as hover info, zooming, and filtering.
Works seamlessly with R, Python, and other programming languages.
Integrates with ggplot2, allowing you to convert static plots into interactive charts.
Supports cloud deployment via Plotly Chart Studio for easy sharing.
Simple syntax reduces coding complexity, making interactive visuals accessible to non-programmers.
Limitations to be aware of:
Community (free) version plots are publicly accessible.
API usage may have daily limits for online hosting.
Large datasets can sometimes affect performance; careful aggregation or filtering may be needed.
Getting Started with Plotly in R
Begin by installing and loading the Plotly package:
install.packages("plotly")
library(plotly)
Plotly works well alongside dplyr and ggplot2, so we often load them too:
library(dplyr)
library(ggplot2)
The basic function for creating a Plotly plot is:
plot_ly(x, y, type, mode, color, size)
Where:
x, y → axis values
type → type of chart (scatter, bar, histogram, etc.)
mode → representation mode (lines, markers, or lines+markers)
color → color mapping for categorical variables
size → scales marker sizes according to a numeric variable
Creating Scatterplots
Let’s start with a scatterplot using the built-in iris dataset:
data(iris)
head(iris)
The dataset contains four continuous variables (Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) and one categorical variable (Species). To create a basic scatterplot:
scatter <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length, type = 'scatter', mode = 'markers')
layout(scatter, title = "Sepal vs Petal Length", xaxis = list(title = "Sepal Length"), yaxis = list(title = "Petal Length"))
Enhancing with Color and Size
We can differentiate species using color and scale marker size by Sepal.Length:
scatter <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length, type = 'scatter', mode = 'markers',
color = ~Species, size = ~Sepal.Length)
layout(scatter, title = "Interactive Scatterplot by Species", xaxis = list(title = "Sepal Length"),
yaxis = list(title = "Petal Length"))
Interactivity features include:
Hovering over points to see details
Zooming and panning
Selecting points with box or lasso tools
Exporting plots as PNG
Line Charts and Time Series
Plotly excels at time series and line charts. Consider the airquality dataset:
data(airquality)
plot_ly(airquality, x = ~Day, y = ~Solar.R, type = 'scatter', mode = 'lines+markers') %>%
layout(title = "Daily Solar Radiation", xaxis = list(title = "Day"), yaxis = list(title = "Solar.R"))
This chart provides interactive exploration of trends, enabling analysts to zoom into specific days or inspect individual points easily.
Histograms and Bar Charts
Histogram Example
plot_ly(iris, x = ~Sepal.Length, type = 'histogram') %>%
layout(title = "Distribution of Sepal Length", xaxis = list(title = "Sepal Length"), yaxis = list(title = "Count"))
Bar Chart Example
plot_ly(iris, x = ~Species, y = ~Sepal.Length, type = 'bar') %>%
layout(title = "Average Sepal Length by Species", xaxis = list(title = "Species"), yaxis = list(title = "Sepal Length"))
Stacked Bar Charts
For grouped data:
Animals <- c("Giraffes", "Orangutans", "Monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
zoo_data <- data.frame(Animals, SF_Zoo, LA_Zoo)
plot_ly(zoo_data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo') %>%
add_trace(y = ~LA_Zoo, name = 'LA Zoo') %>%
layout(title = "Zoo Animal Counts", yaxis = list(title = "Count"), barmode = 'stack')
Combining Line and Scatter Plots
Plotly allows layering multiple traces in a single chart. For example:
set.seed(123)
data <- data.frame(x = 1:100, trace1 = rnorm(100, 0, 1), trace2 = rnorm(100, -5, 1))
plot_ly(data, x = ~x, y = ~trace1, type = 'scatter', mode = 'lines+markers', name = 'Trace 1') %>%
add_trace(y = ~trace2, mode = 'markers', name = 'Trace 2') %>%
layout(title = "Combined Line and Scatter Plot", xaxis = list(title = "X"), yaxis = list(title = "Values"))
This is perfect for comparing multiple time series or data patterns on the same visualization.
Box Plots
Box plots summarize distributions effectively:
data(mtcars)
plot_ly(mtcars, y = ~hp, type = 'box') %>%
layout(title = "Horsepower Distribution in mtcars", yaxis = list(title = "HP"))
Interactive box plots allow users to explore outliers and quartiles dynamically.
Heat Maps
Heat maps are great for visualizing matrix-style data. Using volcano:
data(volcano)
plot_ly(z = ~volcano, type = 'heatmap') %>%
layout(title = "Volcano Elevation Heatmap")
Hovering over cells reveals exact values, making them highly interactive for pattern analysis.
3D Scatter Plots
Plotly supports 3D visualizations, enhancing multidimensional data analysis:
plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length,
type = 'scatter3d', mode = 'markers', color = ~Species, size = ~Petal.Width) %>%
layout(title = "3D Scatterplot of Iris Dataset")
Users can rotate, zoom, and inspect points interactively, gaining insights not visible in 2D plots.
Integrating ggplot2 with Plotly
A powerful feature of Plotly is the ability to convert ggplot2 plots into interactive charts using ggplotly():
p <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point(size = 3) +
theme_minimal()
ggplotly(p)
This leverages ggplot2’s familiar grammar while adding interactivity like hover info, zooming, and dynamic legends.
Advanced Customizations
Plotly also supports:
Hover templates to customize tooltips
Subplots for multi-panel dashboards
Annotations and shapes for highlighting insights
Themes and layouts to match branding or presentation styles
Example: Adding custom hover text:
plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species,
text = ~paste("Species:", Species, "
Sepal:", Sepal.Length)) %>%
layout(title = "Custom Hover Text Example")
Conclusion
Plotly transforms R visualizations from static images into interactive, engaging, and shareable insights. From scatterplots and line charts to 3D scatter plots and heatmaps, Plotly empowers analysts to explore data dynamically and tell compelling stories. Its compatibility with ggplot2 makes it flexible for traditional users while providing advanced interactive features for dashboards and reports.
Whether you are building dashboards for internal teams, executive presentations, or public-facing insights, Plotly elevates your data from numbers to narratives.
Time to start experimenting — create your first interactive plot today and see your data come alive!
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