Getting Started with Plotly in R

06 Jun, 2021
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.
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”)
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 librarylibrary(plotly) # create plotly visualisationp <- plot_ly(iris, x = ~Sepal.Width,                   y = ~Sepal.Length) # adding markersadd_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 librarylibrary(plotly) # create plotly visualisationp <- plot_ly(iris, x = ~Sepal.Width,                    y = ~Sepal.Length) # structure of plotly using# plotly_buildstr(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 librarylibrary(plotly) # create plotly visualisationp <- 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 librarylibrary(plotly) # create plotly visualisationp <- plot_ly(iris, x = ~Sepal.Width,                    y = ~Sepal.Length) # adding trace (lines) to plotly# visualisationadd_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 librarylibrary(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 librarylibrary(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 librarylibrary(plotly) # create plotly visualisationp <- plot_ly(iris, x = ~Sepal.Width,                    y = ~Sepal.Length) # importing plotly visualisation# as image filesPng <- 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 graphicsSvg <- plotly_IMAGE(p, format = "svg",                     out_file = "plotly-test-image.svg") # importing plotly visualisation as# pdf filePdf <- 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 librarylibrary(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 librarylibrary(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. 
 
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