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* update dplyr functions to use non-standard evaluation

* Typo: unquote rlang::sym

For some reason, all tests passed without catching this mistake, but I think I should have had a !! here.

* Same fix as previous commit (d2bc923)

* missed an add --> .add

* lapply(vars, sym)-->syms(vars)

Co-authored-by: Carson Sievert <>

* Revert imports.R

* Use group_by_add; dots_as_quosures

* as_quosure --> new_quosure

As is done here

Co-authored-by: Carson Sievert <>

* Remove redundant add arguments to group_by_add

* Proper registration plotly methods for dplyr generics

* ensure that crosstalk set attribute is preserved when applying dplyr transforms, closes #1825, closes #1799

* make highlight_key() an S3 generic that understands plotly objects

* review feedback

* clean-up docs

Co-authored-by: julianstanley <>
Co-authored-by: Julian Stanley <>

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An R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.


Install from CRAN:


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


Getting started

Web-based ggplot2 graphics

If you use ggplot2, ggplotly() converts your static plots to an interactive web-based version!

g <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  stat_density_2d(aes(fill = ..level..), geom = "polygon") + 
  xlim(1, 6) + ylim(40, 100)

By default, ggplotly() tries to replicate the static ggplot2 version exactly (before any interaction occurs), but sometimes you need greater control over the interactive behavior. The ggplotly() function itself has some convenient “high-level” arguments, such as dynamicTicks, which tells plotly.js to dynamically recompute axes, when appropriate. The style() function also comes in handy for modifying the underlying trace attributes (e.g. hoveron) used to generate the plot:

gg <- ggplotly(g, dynamicTicks = "y")
style(gg, hoveron = "points", hoverinfo = "x+y+text", hoverlabel = list(bgcolor = "white"))

Moreover, since ggplotly() returns a plotly object, you can apply essentially any function from the R package on that object. Some useful ones include layout() (for customizing the layout), add_traces() (and its higher-level add_*() siblings, for example add_polygons(), for adding new traces/data), subplot() (for combining multiple plotly objects), and plotly_json() (for inspecting the underlying JSON sent to plotly.js).

The ggplotly() function will also respect some “unofficial” ggplot2 aesthetics, namely text (for customizing the tooltip), frame (for creating animations), and ids (for ensuring sensible smooth transitions).

Using plotly without ggplot2

The plot_ly() function provides a more direct interface to plotly.js so you can leverage more specialized chart types (e.g., parallel coordinates or maps) or even some visualization that the ggplot2 API won’t ever support (e.g., surface, mesh, trisurf, etc).

plot_ly(z = ~volcano, type = "surface")

Learn more

To learn more about special features that the plotly R package provides (e.g., client-side linking, shiny integration, editing and generating static images, custom events in JavaScript, and more), see You may already be familiar with existing plotly documentation (e.g.,, which is essentially a language-agnostic how-to guide for learning plotly.js, whereas is meant to be more wholistic tutorial written by and for the R user. The package itself ships with a number of demos (list them by running demo(package = "plotly")) and shiny/rmarkdown examples (list them by running plotly_example("shiny") or plotly_example("rmd")). Carson also keeps numerous slide decks with useful examples and concepts.


Please read through our contributing guidelines. Included are directions for opening issues, asking questions, contributing changes to plotly, and our code of conduct.

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