In gvarunkumar/plotly: Create Interactive Web Graphics via 'plotly.js'

knitr::opts_chunk$set(
  message = FALSE, 
  warning = FALSE, 
  fig.width = 7, 
  fig.height = 3
)

plotly is an R package for making interactive graphics via the open source JavaScript graphing library plotly.js. It provides two main ways to create a plotly visualization: ggplotly() and plot_ly(). Both of these functions output an htmlwidget object, which allows plots to work seamlessly and consistently across various contexts (e.g., R Markdown documents, shiny apps, inside RStudio, or any other R command prompt). For IPython/Jupyter notebook users, there is also an embed_notebook() function to embed plots as iframes pointing to a local HTML file. For plot.ly subscribers, there is a plotly_POST() function for sending local graphs to your account, and a get_figure() function for downloading publicly hosted plot.ly figure(s).

Translate ggplot2 to plotly with `ggplotly()`

The ggplotly() function translates ggplot2 graphics to a plotly equivalent, for example:

library(plotly)
p <- ggplot(txhousing, aes(x = date, y = median, group = city)) +
  geom_line(alpha = 0.3) +
  geom_line(data = subset(txhousing, city == "Houston"), color = "red")
ggplotly(p)

If you know ggplot2, ggplotly() is great since you can add some interactivity (specifically, idenfication + zoom & pan) to your plots for free. Also,

The ggplotly() function tries its best to replicate what you see exactly in the static ggplot2 graph.

The output of a ggplotly() function is a plotly object.

The `plot_ly()` interface

The plot_ly() function draws inspiration from ggplot2's implementation of the grammar of graphics, but provides a more flexible and direct interface to plotly.js. The interface is also functional, and designed to work with dplyr, so visualization can be described as a sequence of data manipulations and visual components via the pipe operator (%>%) from the magrittr package.

txhousing %>%
  group_by(city) %>%
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(alpha = 0.3, color = I("black"), name = "Texan Cities") %>%
  filter(city == "Houston") %>%
  add_lines(color = I("red"), name = "Houston")