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

1. 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

1. 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”) ) 1.

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’)

1. 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”) ) 1.

Heat Maps Using the volcano dataset: data(volcano) plot_ly( z = ~volcano, type = ‘heatmap’ ) 1.

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.

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