Educational Statistics and Research Methods (ESRM) Program*
University of Arkansas
Published
March 19, 2025
Overview
Introduction to Plotly
Advanced Plot Types
Customization Techniques
Interactivity Features
Integration with Other Tools
Performance Optimization
Introduction to Plotly
Plotly is an open-source JavaScript library that enables the creation of interactive, publication-quality plots in R. It supports various chart types, including line plots, scatter plots, bar charts, histograms, and more. Interactive plots enhance data exploration and presentation by allowing users to engage directly with visualizations.
The plotly package is built on top of the htmlwidgets framework, which allows R users to create interactive web-based visualizations using JavaScript libraries.
In Figure 1, ggplotly() can translate a ggplot2 object into a plotly object.
p <-ggplot(diamonds, aes(x=log(carat), y=log(price))) +geom_hex(bins =100)ggplotly(p)
Figure 1
add_ as layers and |> as connection
Plotly implements geoms in a similar fashion as ggplot2, functions that start in add_ add a layer to the plot (e.g.: add_lines, add_bars), making it easy to combine series into a single chart, as in Figure 2.
plot_ly(mtcars, x=~disp) |>add_markers(y=~mpg, text =rownames(mtcars)) |>add_lines(y=~fitted(loess(mpg ~ disp)))
Figure 2: Plotly with add_ and |> as connection
Advanced Plot Types
3D Scatter and Surface Plots
Visualizing three-dimensional data can provide deeper insights into complex datasets.
library(plotly)# Sample datax <-rnorm(100)y <-rnorm(100)z <-rnorm(100)# 3D Scatter Plotfig <-plot_ly(x =~x, y =~y, z =~z, type ='scatter3d', mode ='markers')fig
Heatmaps and Contour Plots
Heatmaps and contour plots are effective for representing data density and gradients.
Utilizing pre-defined themes and creating custom templates ensures consistent styling across multiple plots.
# Sample datax <-c('A', 'B', 'C')y <-c(10, 15, 13)# Manually setting dark theme colorsfig <-plot_ly(x =~x, y =~y, type ='bar', marker =list(color ='lightblue')) |>layout(title =list(text ='Custom Themed Bar Chart', font =list(color ='white')),plot_bgcolor ='black',paper_bgcolor ='black',font =list(color ='white'))fig
Annotations and Shapes
Adding informative annotations and geometric shapes can highlight significant data points or regions.
# Sample datax <-c(1, 2, 3, 4)y <-c(10, 11, 12, 13)# Line Plot with Annotationfig <-plot_ly(x =~x, y =~y, type ='scatter', mode ='lines') |>layout(title ='Annotated Line Plot',annotations =list(x =2, y =11, text ="Important Point", showarrow =TRUE ))fig
Interactivity Features
Hover and Click Events
Interactive features such as hover and click events can display additional information or trigger specific actions upon user interaction.
# Sample datax <-c('A', 'B', 'C')y <-c(10, 15, 13)# Bar Chart with Hover Infofig <-plot_ly(x =~x, y =~y, type ='bar', text =~paste('Value: ', y),hoverinfo ='text')fig
Sliders and Dropdowns
Incorporating interactive controls like sliders and dropdowns allows users to dynamically filter or modify the data displayed.
# Sample datax <-c(1, 2, 3, 4, 5)y1 <-c(1, 3, 5, 7, 9)y2 <-c(2, 4, 6, 8, 10)# Line Plot with Dropdownfig <-plot_ly() |>add_trace(x =~x, y =~y1, type ='scatter', mode ='lines', name ='Line 1') |>add_trace(x =~x, y =~y2, type ='scatter', mode ='lines', name ='Line 2') |>layout(title ='Line Plot with Dropdown',updatemenus =list(list(y =0.8,buttons =list(list(method ="restyle",args =list("visible", list(TRUE, FALSE)),label ="Line 1"),list(method ="restyle",args =list("visible", list(FALSE, TRUE)),label ="Line 2"),list(method ="restyle",args =list("visible", list(TRUE, TRUE)),label ="Both") ) ) ) )fig
Integration with Other Tools
Shiny Applications
Integrating Plotly plots within Shiny apps enables the development of interactive web applications.