Data visualization in R has come a long way. While tools like ggplot2 have become the gold standard for creating elegant static visualizations, modern analytics increasingly demands interactivity—zooming, filtering, selecting points, tooltips, 3D interactions, animated transitions, and more.
As datasets grow and stakeholders expect dashboards rather than one-off graphs, the need for interactive visualization libraries becomes clear.
This is where Plotly shines.
Plotly gives R users the power of interactive, browser-based visualizations without requiring knowledge of JavaScript, CSS, or D3.js. The syntax is intuitive, compatible with ggplot2, and powerful enough to build dashboards, research apps, or web-based analytics tools.
This updated guide walks you through:
✅ What Plotly is and how it works
✅ Pros and cons
✅ How to install & use Plotly in R
✅ Step-by-step examples of interactive charts
✅ Advanced visualizations like heatmaps and 3D plots
What is Plotly?
Plotly is a high-level, declarative charting library built on top of modern web technologies like:
D3.js (for rendering visual elements)
HTML5 Canvas
CSS
WebGL (for 3D and high-performance rendering)
The visualizations are interactive and can be displayed in:
RStudio viewer pane
Shiny apps
HTML reports
Web dashboards
Plotly Cloud accounts
Plotly supports multiple languages including Python, R, JavaScript, Julia, and MATLAB, making it a universal visualization framework.
Advantages of Plotly
No JavaScript or D3.js required
Interactive charts by default
Compatible with ggplot2 (via ggplotly())
Works seamlessly with Shiny dashboards
Cloud hosting available for sharing visualizations
A wide gallery of chart types: heatmaps, 3D plots, maps, animations, etc.
Simple, consistent syntax
Limitations of Plotly
Community cloud version makes some charts publicly accessible
Daily or request-based API limits (if using Plotly Cloud)
For extremely large datasets, performance can drop
3D plots can be resource-heavy in older browsers
Plotly in R: Installation and Setup
install.packages("plotly")
library(plotly)
You'll also see Plotly load ggplot2 because of ggplot compatibility.
Plotly Syntax Overview
General syntax:
plot_ly(x, y, type, mode, color, size)
Common arguments:
x, y: Aesthetic mappings
type: "scatter", "bar", "box", "histogram", "heatmap"
mode: "markers", "lines", "lines+markers"
color: Variable for grouping
size: Size of markers
- Scatter Plot with the Iris Dataset Start with loading the dataset: library(dplyr) attach(iris) head(iris) glimpse(iris)
Basic scatter plot
sca <- plot_ly(
x = ~Sepal.Length,
y = ~Petal.Length,
type = 'scatter'
)
layout(sca,
title = 'Scatter Plot',
xaxis = list(title = 'Sepal Length'),
yaxis = list(title = 'Petal Length')
)
Add color for categories
plot_ly(
x = ~Sepal.Length,
y = ~Petal.Length,
type = 'scatter',
color = ~Species
) %>%
layout(
title = "Sepal vs Petal Length",
xaxis = list(title = "Sepal Length"),
yaxis = list(title = "Petal Length")
)
Add marker size
plot_ly(
x = ~Sepal.Length,
y = ~Petal.Length,
type = 'scatter',
color = ~Species,
size = ~Sepal.Length
)
Interactivity now includes:
zooming
panning
lasso selection
saving as PNG
hover tooltips
- Line Charts & Time Series Use the built-in airquality dataset: attach(airquality) glimpse(airquality)
Basic time series
plot_ly(
y = ~Solar.R,
type = 'scatter',
mode = 'lines'
) %>%
layout(
title = 'Time Series: Solar Radiation',
yaxis = list(title = 'Solar.R')
)
Add markers
plot_ly(
y = ~Solar.R,
type = 'scatter',
mode = 'lines+markers'
)
Histograms
plot_ly(
x = ~Sepal.Length,
type = 'histogram'
) %>%
layout(
title = "Histogram of Sepal Length",
xaxis = list(title = "Sepal Length"),
yaxis = list(title = "Count")
)Bar Plots
Simple bar plot
plot_ly(
x = ~Species,
type = 'bar'
)
Stacked bar plot
Animals <- c("giraffes", "orangutans", "monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(Animals, SF_Zoo, LA_Zoo)
plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo') %>%
add_trace(y = ~LA_Zoo, name = 'LA Zoo') %>%
layout(barmode = 'stack')
- Combining Line + Scatter trace_1 <- rnorm(100) trace_2 <- rnorm(100, mean = -5) x <- 1:100 data <- data.frame(x, trace_1, trace_2)
plot_ly(data, x = ~x, y = ~trace_1, mode = 'lines+markers', name = 'Trace 1') %>%
add_trace(y = ~trace_2, mode = 'markers', name = 'Trace 2')
Box Plots
Using mtcars:
attach(mtcars)
plot_ly(
y = ~hp,
type = 'box'
) %>%
layout(
title = "Horsepower Distribution",
yaxis = list(title = "HP")
)Heat Maps
Using the volcano dataset:
data(volcano)
plot_ly(
z = ~volcano,
type = 'heatmap'
)3D Scatter Plots (One of Plotly’s Strengths)
This interactive 3D chart uses WebGL rendering:
plot_ly(
x = ~Sepal.Length,
y = ~Sepal.Width,
z = ~Petal.Length,
type = "scatter3d",
mode = 'markers',
size = ~Petal.Width,
color = ~Species
)
You can rotate, zoom, drag, and inspect the 3D structure of the iris dataset — something static plots cannot offer.
Conclusion
Plotly brings a powerful interactive layer to R visualizations. While ggplot2 is excellent for static graphics, Plotly excels when:
you need dashboards
users must explore data interactively
you want 3D, animations, or high-engagement visuals
you're building Shiny apps or HTML reports
Plotly’s intuitive syntax, wide chart support, and rich interactivity make it one of the most valuable tools in a modern R workflow.
Now that you’ve seen how to create scatter plots, time series, histograms, bar charts, box plots, heatmaps, and 3D graphs — you’re ready to build your own interactive analytics stories.
At Perceptive Analytics, we help organizations harness data to make smarter, faster business decisions. Our data analytics services enable teams to uncover insights, optimize performance, and drive measurable outcomes across their operations. As an experienced Power BI Consultants, we also design dashboards, automate reporting, and build scalable BI solutions that empower leaders with real-time visibility. With deep expertise across advanced analytics and business intelligence, we turn data into a strategic advantage.
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