Getting Started with Plotly in R

creationPlotly in R Programming Language allows the creation of interactive web graphics from ‘ggplot2’ graphs and a custom interface to the JavaScript library ‘plotly.js’ inspired by the grammar of graphics.

Installation

To use a package in R programming one must have to install the package first. This task can be done using the command install.packages(“packagename”). To install the whole plotly package type this:

install.packages(“plotly”)

Or install the latest development version (on GitHub) via dev tools:

devtools::install_github(“ropensci/plotly”)

Important Functions

plot_ly: It basically initiates a plotly visualization. This function maps R objects to plotly.js, an (MIT licensed) web-based interactive charting library. It provides abstractions for doing common things and sets some different defaults to make the interface feel more ‘R-like’ (i.e., closer to plot() and ggplot2::qplot()).

Syntax:

plot_ly(data = data.frame(), …, type = NULL, name, color, colors = NULL, alpha = NULL, stroke, strokes = NULL, alpha_stroke = 1, size, sizes = c(10, 00), span, spans = c(1, 20), symbol, symbols = NULL, linetype, linetypes = NULL, split, frame, width = NULL, height = NULL, source = “A”)

Scatter Plot with Colors plotly in R

R

# Load necessary libraries
library(plotly)
library(dplyr)

# Load Iris dataset
data(iris)

#Scatter Plot with Colors
plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, color = ~Species, 
        type = "scatter", mode = "markers", 
        marker = list(size = 10, opacity = 0.8)) %>%
  layout(title = "Scatter Plot of Sepal Length vs. Sepal Width",
         xaxis = list(title = "Sepal Length"),
         yaxis = list(title = "Sepal Width"))

Output:

Scatter Plot with Plotly in R

plotly_build: This generic function creates the list object sent to plotly.js for rendering. Using this function can be useful for overriding defaults or for debugging rendering errors.

Syntax: plotly_build(p, registerFrames = TRUE)

Box Plot with Plotly in R

R

#Box Plot
plot_ly(iris, x = ~Species, y = ~Petal.Length, 
        type = "box", boxpoints = "all", jitter = 0.3,
        pointpos = -1.8, boxmean = "sd") %>%
  layout(title = "Box Plot of Petal Length by Species",
         xaxis = list(title = "Species"),
         yaxis = list(title = "Petal Length"))

Output:

Box Plot with Plotly in R

3D Scatter Plot with Plotly in R

R

# 3D Scatter Plot
plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, z = ~Petal.Length,
        color = ~Species, type = "scatter3d", mode = "markers", 
        marker = list(size = 8, opacity = 0.8)) %>%
  layout(title = "3D Scatter Plot of Sepal Length, Sepal Width, and Petal Length",
         scene = list(xaxis = list(title = "Sepal Length"),
                      yaxis = list(title = "Sepal Width"),
                      zaxis = list(title = "Petal Length")))

Output:

3D Scatter Plot with Plotly in R

Heatmap Plot with Plotly in R

R

#Heatmap
plot_ly(z = ~cor(iris[, 1:4]), type = "heatmap", 
        colorscale = "Viridis", showscale = FALSE) %>%
  layout(title = "Correlation Heatmap of Iris Features",
         xaxis = list(ticktext = colnames(iris[, 1:4]),
                      tickvals = seq(0.5, 4.5, by = 1),
                      title = "Features"),
         yaxis = list(ticktext = colnames(iris[, 1:4]),
                      tickvals = seq(0.5, 4.5, by = 1),
                      title = "Features"))

Output:

Heatmap Plot with Plotly in R

Adding trace (lines) to plotly in R

R

# import plotly library
library(plotly)

# create plotly visualisation
p <- plot_ly(iris, x = ~Sepal.Width, 
                    y = ~Sepal.Length) 

# adding trace (lines) to plotly
# visualisation
add_trace(p, type = "scatter", 
          mode = "markers+lines")

Output: 

animation_opts: Provides animation configuration options. Animations can be created by either using the frame argument in plot_ly() or frame ggplot2 aesthetic in ggplotly(). By default, animations populate a play button and slider component for controlling the state of the animation (to pause an animation, click on a relevant location on the slider bar). Both the play button and slider component transition between frames according to rules specified by animation_opts().

Syntax:

animation_opts(p, frame = 500, transition = frame, easing = “linear”, redraw = TRUE, mode = “immediate”)

animation_slider(p, hide = FALSE, …)

animation_button(p, …, label)

R

# import plotly library
library(plotly)


plot_ly(mtcars, x = ~wt, y = ~mpg, 
        frame = ~cyl) %>%
animation_opts(transition = 0)

Output:

add_data: Add data to a plotly visualization.

Syntax: add_data(p, data = NULL)

R

# import plotly library
library(plotly)


plot_ly() %>% add_data(economics) %>% 
        add_trace(x = ~date, y = ~pce)

Output:

plotly_IMAGE: Creates a static image for plotly visualization. The images endpoint turns a plot (which may be given in multiple forms) into an image of the desired format.

Syntax:

plotly_IMAGE(x, width = 1000, height = 500, format = “png”, scale = 1, out_file, …)

R

# import plotly library
library(plotly)

# create plotly visualisation
p <- plot_ly(iris, x = ~Sepal.Width,
                    y = ~Sepal.Length)

# importing plotly visualisation 
# as image files
Png <- plotly_IMAGE(p, 
                    out_file = "plotly-test-image.png")
Jpeg <- plotly_IMAGE(p, format = "jpeg",
                     out_file = "plotly-test-image.jpeg")

# importing plotly visualisation
# as vector graphics
Svg <- plotly_IMAGE(p, format = "svg",  
                    out_file = "plotly-test-image.svg")

# importing plotly visualisation as
# pdf file
Pdf <- plotly_IMAGE(p, format = "pdf", 
                    out_file = "plotly-test-image.pdf")

Output:

Plotly in R

Conclusion

We can leverage the plotly R package to create a variety of interactive graphics. Two main ways of creating a plotly object: either by transforming a ggplot2 object (via ggplotly()) into a plotly object or by directly initializing a plotly object with plot_ly()/plot_geo()/plot_mapbox(). Both approaches have somewhat complementary strengths and weaknesses, so it can pay off to learn both approaches.