Installing and Loading Plotly in R

Data visualization today is both an art and a science. As organizations adopt data-driven decision-making, the ability to present insights clearly—and interactively—becomes incredibly valuable. R offers a variety of visualization libraries, with ggplot2, ggvis, and lattice being long-standing favorites. But when it comes to interactive graphics, static plots often fall short in performance, flexibility, and user experience.
This is where Plotly shines.
Plotly allows you to create rich, interactive, web-ready visualizations without writing a single line of JavaScript or CSS. From simple scatterplots to complex 3D charts, Plotly gives analysts, data scientists, and BI developers the power to build dashboards that feel modern, dynamic, and intuitive.
In this article, we’ll explore the fundamentals of Plotly in R, understand how it works, and walk through practical examples including scatterplots, time-series charts, histograms, box plots, heatmaps, and 3D visualizations.

What is Plotly?
Plotly is a high-level data visualization library built on top of D3.js, HTML, and CSS, designed to create interactive, web-based graphics. The Plotly ecosystem includes:
Plotly.js – JavaScript library for client-side rendering
Plotly for R/Python – language wrappers
Chart Studio – a no-code visualization editor
Dash – a framework for building interactive data apps
Plotly for R uses HTML widgets under the hood, allowing visualizations to be embedded in:
R Markdown reports
Shiny dashboards
Web applications
Jupyter notebooks
Static HTML files

Advantages of Plotly
Plotly comes with several strengths that make it highly appealing:

1. Build D3-level visualizations without D3 You get the power of interactive DOM-based charts without needing to learn JavaScript.
2. Multi-language support Plotly works across Python, R, MATLAB, and JavaScript—making it multi-disciplinary.
3. Easy hosting & sharing Charts can be exported, embedded, or hosted via Plotly’s cloud.
4. Chart Studio for non-coders Plotly’s drag-and-drop interface allows beginners to create visualizations without writing code.
5. Integration with ggplot2 You can convert ggplot2 graphics into interactive Plotly versions using ggplotly().

Limitations of Plotly
Like any tool, Plotly has constraints:
Community version charts are public by default
Daily API call limits for cloud-hosted plots
Rendering very large datasets can be slow
Browser-based rendering may not scale well for millions of points
Still, for most business and research use cases, Plotly provides ample power.

Installing and Loading Plotly in R
install.packages("plotly")
library(plotly)

You’ll also use dplyr for data inspection:
library(dplyr)

Getting Started With Plotly Syntax
The core function is:
plot_ly(x, y, type, mode, color, size)

Where:
x, y – data for the axes
type – chart type ("scatter", "bar", "histogram", etc.)
mode – how points/lines should appear ("markers", "lines"…)
color/size – aesthetic mappings for interactivity

Scatterplots in Plotly
Scatterplots are among the most common interactive visuals.
We’ll use the iris dataset.
attach(iris)
glimpse(iris)

Basic scatterplot
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'))

Adding color by species
sca <- plot_ly(x = ~Sepal.Length, y = ~Petal.Length,
type = 'scatter', color = ~Species)

Adding size aesthetic
sca <- plot_ly(x = ~Sepal.Length, y = ~Petal.Length,
type = 'scatter', color = ~Species, size = ~Sepal.Length)

Interactivity now includes:
Zoom & pan
Hover tooltips
Lasso selection
Save as PNG

Line Charts & Time Series in Plotly
Using the airquality dataset:
attach(airquality)
glimpse(airquality)

Basic time-series
ti <- plot_ly(y = ~Solar.R, type = 'scatter', mode = 'lines')
layout(ti, title = 'Time Series', yaxis = list(title = 'Solar Reading'))

Add markers
ti <- plot_ly(y = ~Solar.R, type = 'scatter', mode = 'lines+markers')

This creates a dynamic time-series visualization.

Histograms
hist <- plot_ly(x = ~Sepal.Length, type = 'histogram')
layout(hist, title = 'Histogram',
xaxis = list(title = 'Sepal Length'),
yaxis = list(title = 'Count'))

Interactive histograms allow users to highlight ranges and inspect distribution.

Bar Plots & Stacked Bars
Simple bar plot
bar <- plot_ly(x = ~Sepal.Length, type = 'bar')
layout(bar, title = 'Bar Plot')

But more useful is a stacked bar chart:
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)

p <- plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo') %>%
add_trace(y = ~LA_Zoo, name = 'LA Zoo')

layout(p, yaxis = list(title = 'Count'), barmode = 'stack')

Combining Line & Scatter Plots
Plotly supports multiple traces in one chart.
trace_1 <- rnorm(100, mean = 0)
trace_2 <- rnorm(100, mean = -5)
x <- c(1:100)
data <- data.frame(x, trace_1, trace_2)

plot_ly(data, x = ~x, y = ~trace_1, type = 'scatter',
mode = 'lines+markers', name = 'Trace 1') %>%
add_trace(y = ~trace_2, mode = 'markers', name = 'Trace 2')

Great for comparing trends vs. noise.

Box Plots
Using mtcars:
attach(mtcars)
box <- plot_ly(y = ~hp, type = 'box')
layout(box, title = 'Horsepower Distribution', yaxis = list(title = 'HP'))

Interactive box plots allow tooltip inspection of quartiles, outliers, and medians.

Heatmaps
Heatmaps work exceptionally well for matrix-like data.
data(volcano)
plot_ly(z = ~volcano, type = 'heatmap')

Hovering over each cell reveals intensity values.

3D Scatter Plots
One of Plotly’s most visually appealing capabilities:
plot_ly(x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length,
type="scatter3d", mode = 'markers',
size = ~Petal.Width, color = ~Species)

You can rotate, zoom, and examine clusters from any angle.

Conclusion
Plotly unlocks a level of interactivity that static visualization tools cannot match. Whether you're building dashboards, exploring data visually, or presenting insights to a non-technical audience, Plotly brings clarity and engagement to your visual storytelling.
In this article, we explored:
How Plotly works in R
Basic and advanced chart types
Combining multiple traces
3D & heatmap visualizations
When to choose Plotly over ggplot2
In modern analytics workflows, Plotly often complements ggplot2—ggplot excels at static design, while Plotly brings those visuals to life interactively. Together, they offer a powerful toolkit for R users.
So go ahead—experiment, explore, and build your next interactive story with Plotly.

At Perceptive Analytics, we help businesses make smarter decisions with data. Our experienced Microsoft Power BI consultants design dashboards, automate reporting, and build scalable BI solutions tailored to each organization’s needs. As a trusted Power BI consulting company, we bring deep expertise in data modeling, visualization, and analytics strategy—empowering teams with real-time insights that drive better outcomes.