Getting Started with Plotly in R

Plotly in R Programming Language allows to create 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 devtools:

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”)

Example: 

R

# import plotly library
library(plotly)

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

# adding markers
add_markers(p, color = ~Petal.Length,
               size = ~Petal.Length)
add_markers(p, color = ~Species)

Output: 

• 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)

Example: 

R

# import plotly library
library(plotly)

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

# structure of plotly using
# plotly_build
str(plotly_bulid(p, registerFrames = TRUE))

Output: 

No scatter mode specified:

  Setting the mode to markers

  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

List of 8

 $ x            :List of 10

  ..$ visdat     :List of 1

  .. ..$ 29f4345ac49:function ()  

  ..$ cur_data   : chr “29f4345ac49”

  ..$ attrs      :List of 1

  .. ..$ 29f4345ac49:List of 5

  .. .. ..$ x           :Class ‘formula’  language ~Sepal.Width

  .. .. .. .. ..- attr(*, “.Environment”)=<environment: R_GlobalEnv> 

  .. .. ..$ y           :Class ‘formula’  language ~Sepal.Length

  .. .. .. .. ..- attr(*, “.Environment”)=<environment: R_GlobalEnv> 

  .. .. ..$ alpha_stroke: num 1

  .. .. ..$ sizes       : num [1:2] 10 100

  .. .. ..$ spans       : num [1:2] 1 20

 ……..

• layout: Modify the layout of a plotly visualization

Syntax: layout(p, …, data = NULL)

Example:

R

# import plotly library
library(plotly)

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

Output:

• add_trace: Add trace(s) to a plotly visualization.

Syntax: add_trace(p, …, data = NULL, inherit = TRUE)

Example: 

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)

Example:

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)

Example:

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, …)

Example:

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_empty: Creates a complete empty plotly graph. It is a very useful function when used with subplot().

Syntax: plotly_empty(…)

Example:

R

# import plotly library
library(plotly)
plotly_example(iris)

Output: 

• plotly_example: It runs a plotly example(s). Provides a unified interface for running demos, shiny apps, and Rmd documents which are bundled with the package.

Syntax: plotly_example(type = c(“demo”, “shiny”, “rmd”), name, edit = TRUE, …)

Example:

R

# import plotly library
library(plotly)
plotly_example(type = c("demo", "shiny", "rmd"),
               name, edit = TRUE, ...)

Output:

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.