Data visualization is often described as both an art and a science. While statistics provide the numbers and mathematics behind data, visualization gives life to these numbers by transforming them into meaningful patterns, trends, and insights. In the R programming ecosystem, visualization has always played a central role, with packages like ggplot2 setting the gold standard for static graphics. However, when it comes to interactive visualizations—charts that allow users to zoom, hover, filter, and explore—traditional static tools fall short.
This is where Plotly shines. Plotly enables analysts, data scientists, and even non-programmers to build rich, interactive, and publication-ready visualizations without requiring extensive knowledge of JavaScript, HTML, or CSS. Whether you’re creating scatter plots, time series charts, heatmaps, or 3D visualizations, Plotly integrates seamlessly into R workflows, offering simplicity and power.
In this article, we’ll explore Plotly in detail—its advantages, syntax, and use cases—while also walking through practical chart-building examples. We’ll also discuss how interactive visualizations enhance storytelling in industries like finance, healthcare, marketing, and research.
What is Plotly?
Plotly is an open-source data visualization library built on top of web technologies such as D3.js, HTML, and CSS. Originally created using the Django framework, it provides APIs for multiple languages, including Python, R, Julia, and JavaScript. For R users, Plotly offers a powerful way to integrate interactive plots directly into notebooks, Shiny dashboards, and even static reports (by exporting charts).
Key Features of Plotly:
Interactivity first: Charts are not just static images but dynamic components that allow zooming, hovering, panning, and filtering.
Language compatibility: Works with R, Python, MATLAB, and more.
Cloud sharing: Visualizations can be hosted and shared online using the Plotly API.
Low learning curve: Simple syntax similar to ggplot2, reducing the learning barrier.
Chart Studio: A drag-and-drop interface that allows even non-coders to build charts.
Integration with ggplot2: Users can start with familiar ggplot2 syntax and enhance it with interactivity.
Advantages of Plotly:
Build D3.js-powered visualizations without learning JavaScript.
Cross-platform and multi-language compatibility.
Easy cloud hosting for collaborative access.
User-friendly for non-coders through Chart Studio.
Compatible with Shiny apps for interactive dashboards.
Limitations of Plotly:
The free/community version makes charts public by default.
Daily API call limits can restrict very heavy usage.
Certain advanced features require a commercial license.
Despite these limitations, Plotly remains one of the most widely adopted tools for interactive visualization in R.
Getting Started with Plotly in R
Before diving into chart-building, install the package:
install.packages("plotly")
require(plotly)
When loaded, Plotly attaches functions that sometimes overlap with existing R packages (e.g., filter, layout). Keep this in mind while scripting.
The general syntax is:
plot_ly(x, y, type, mode, color, size)
Where:
x → values for the x-axis
y → values for the y-axis
type → type of plot (scatter, bar, box, histogram, etc.)
mode → specifies how data should be displayed (lines, markers, etc.)
color → differentiates groups by color
size → adjusts marker sizes based on variable values
Scatter Plots with Plotly
Scatter plots are the backbone of data visualization when comparing two continuous variables. Using the famous iris dataset, we can easily create interactive scatter plots.
require(dplyr)
attach(iris)
sca <- plot_ly(
x = ~Sepal.Length,
y = ~Petal.Length,
type = 'scatter',
color = ~Species
)
layout(sca,
title = 'Scatter Plot',
xaxis = list(title = 'Sepal length'),
yaxis = list(title = 'Petal length'))
With interactivity enabled, users can:
Zoom into dense areas
Hover to see exact values
Filter species by color
Export plots as PNG
By adding size = ~Sepal.Length, we make marker size proportional to sepal length—adding a third dimension of information.
Case Study: Biology Research
A botanist studying petal-sepal relationships can use interactive scatter plots to compare flower species. Instead of static plots, interactive plots allow deeper exploration, e.g., zooming into anomalies where certain flowers deviate from expected patterns.
Line Charts and Time Series
For sequential or temporal data, line charts are often the best choice. Using the airquality dataset, we can build a time-series visualization of solar radiation values.
attach(airquality)
ti <- plot_ly(y = ~Solar.R, type = 'scatter', mode = 'lines+markers')
layout(ti, title = 'Time Series', yaxis = list(title = 'Solar Reading'))
Adding markers highlights daily fluctuations.
Case Study: Climate Science
Climate researchers can use such time series charts to track ozone levels or solar radiation trends. Unlike static line charts, interactive versions allow them to zoom into specific months, compare anomalies, and export subsets of data.
Histograms
Histograms are used to study the frequency distribution of a variable.
hist <- plot_ly(x = ~Sepal.Length, type = 'histogram')
layout(hist, title = 'Histogram',
xaxis = list(title = 'Sepal length'),
yaxis = list(title = 'count'))
This simple code creates an interactive histogram where bins can be adjusted dynamically.
Case Study: Marketing Analytics
An e-commerce analyst might use interactive histograms to study purchase frequency distributions, allowing them to spot unusual spikes in order sizes.
Bar Charts
Plotly supports both simple and stacked bar charts. Using zoo data:
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')
Case Study: Business Dashboards
Retailers can use stacked bar charts to compare store performance across regions, enabling decision-makers to quickly see differences in sales volume.
Combined Line + Scatter Plots
Plotly allows overlaying multiple chart types. For example, combining time series lines with scatter points for deeper analysis.
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 = 'trace1') %>%
add_trace(y = ~trace_2, mode = 'markers', name = 'trace2')
Box Plots
Box plots are ideal for understanding data spread and outliers. Using the mtcars dataset:
box <- plot_ly(y = ~mtcars$hp, type = 'box')
layout(box, title = 'Box Plot', yaxis = list(title = 'Horse Power'))
Case Study: Healthcare
Hospitals can use box plots to compare patient recovery times across treatments, spotting outliers (very slow or very fast recoveries).
Heatmaps
Heatmaps are great for visualizing intensity across two dimensions. Using the volcano dataset:
plot_ly(z = ~volcano, type = 'heatmap')
Case Study: Retail
Retailers use heatmaps to visualize customer traffic across store layouts, identifying hotspots where customers spend more time.
3D Scatter Plots
One of Plotly’s coolest features is 3D scatter plots. Using the iris dataset:
plot_ly(
x = ~Sepal.Length,
y = ~Sepal.Width,
z = ~Petal.Length,
type = "scatter3d",
mode = 'markers',
size = ~Petal.Width,
color = ~Species
)
Case Study: Financial Markets
Stock analysts can use 3D scatter plots to visualize risk, return, and volatility of different portfolios in one interactive chart.
Why Use Plotly in Dashboards?
Interactive dashboards have become a cornerstone of modern decision-making. Plotly integrates seamlessly with Shiny, making it possible to build dynamic dashboards for:
Executives: Quick insights into KPIs.
Analysts: Drill-down capabilities on datasets.
Clients: Data storytelling with interactive visualizations.
Conclusion
Plotly is more than just a visualization library—it’s a storytelling tool. While ggplot2 remains the go-to for static plots, Plotly extends the R ecosystem into the world of interactivity, making data exploration far richer. From scatter plots and time series to heatmaps and 3D charts, Plotly gives analysts the ability to go beyond presentation into true exploration.
If you’re building dashboards, delivering insights, or simply exploring datasets, Plotly is an indispensable tool. Now is the time to experiment—take your data beyond static charts and unlock deeper insights through interactive visualizations.
This article was originally published on Perceptive Analytics.
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Top comments (1)
Nice guide! I’ve used Plotly a lot in my own work and the biggest win for me has always been how interactive visuals spark better conversations with non-technical teammates. A static chart can get ignored, but once people can hover or zoom, they start asking the right questions.