Any visualization is useful only when you are able to share it. rCharts tries to make it really easy to share the visualizations you create. Let us first create a simple interactive scatterplot to illustrate the different sharing mechanisms built into rCharts

r1 <- rPlot(mpg ~ wt, data = mtcars, type = 'point')


You can save your chart using the save method. The additional parameters passed to the save method determine how the js/css assets of the javascript visualization library are served. You can now email your visualization or embed it in a blog post as an iframe.

# link js/css assets from an online cdn
r1$save('mychart1.html', cdn = TRUE)
# create standalone chart with all assets included directly in the html file
r1$save('mychart2.html', standalone = TRUE)


Sometimes, you may want to directly publish the visualization you created, without having to bother with the steps of saving it and then uploading it. rChart has you covered here, and provides a publish method that combines these two steps. It currently supports publishing to RPubs and Gist and I expect to add more providers over time.

# the host defaults to 'gist'
r1$publish("My Chart")
r1$publish("My Chart", host = 'rpubs')

Publishing a chart saves the html in a temporary file, uploads it to the specified host, and returns a link to where the chart can be viewed. There are many gist viewers out there, and rCharts uses a custom viewer, designed specifically for rCharts, and is a modified version of another excellent gist viewer Another popular gist viewer is, built by Mike Bostock, the creator of d3.js.

If you wish to simply update a visualization you have already created and shared, you can pass the gist/rpubs id to the publish method, and it will update instead of uploading it as a brand new chart.

r1$publish("My Chart", id = 9253202)

While using a provider like Gist that allows multiple files to be uploaded, you can use the extras argument to add additional files that you want to upload. This is especially useful, if you want to provide a or upload external assets like js/css/json files that are required for your chart to render.

r1$publish("My Chart", id = 9253202, extras = "")



Suppose you wish to embed a visualization created using rCharts in an Rmd document.


One way to do this would be to use the save method to save the chart, and then embed it as an iframe. rCharts saves you the steps by allowing you to use the show method and specify that you want the chart to be embedded as an iframe.

We need to set the chunk options comment = NA and results = "asis" so that the resulting html is rendered asis and not marked up (which is the default in knitr).

```{r results = "asis", comment = NA}
r1$show('iframe', cdn = TRUE)

If you have several charts in your Rmd document, you can set these options globally in a setup chunk. Make sure to set cache = F for this chunk so that it is always run.

```{r setup, cache = F}
options(rcharts.mode = 'iframe', rcharts.cdn = TRUE)
knitr::opts_chunk$set(results = "asis", comment = NA)

You can now rewrite the earlier sourcecode chunk simply as


I prefer this style when writing, since it allows a user to simply copy paste sourcecode from the html and run it in their R console.

IFrame Inline

The iframe mode requires users to upload the additional chart html files along with their document. This introduces additional steps, and in the case of some providers like Rpubs, is not even possible. Hence, rCharts provides an additional mode named iframesrc that embeds the chart as an inline iframe, which makes your document self contained.

```{r results = "asis", comment = NA}
r1$show('iframesrc', cdn  = TRUE)

This option has the advantage of keeping the html standalone, but isolating the chart from the html on the page, thereby avoiding css and js conflicts. However, this feature is not supported by IE and Opera.


A third option to embed an rCharts created visualization is to inline the chart directly. Note that you need to add include_assets = TRUE, only the first time you are creating a chart using a specific library.

```{r chart3}
r1$show('inline', include_assets = TRUE, cdn = TRUE)

This approach should work in all browsers, however, it is susceptible to css and js conflicts.

If you are using Slidify to author your Rmd, then you can specify the charting library as ext_widgets in the YAML front matter. Here is a minimal reproducible example.

Note how you did not have to specify include_assets = TRUE. This is because slidify uses the ext_widgets property to automatically pick up the required assets and include them in the header of the resulting html page.


It is easy to embed visualizations created using rCharts into a Shiny application. The main idea is to make use of the utility functions renderChart() and showOutput(). The shiny application created using the code below, can be seen here

 ## server.r
 shinyServer(function(input, output) {
   output$myChart <- renderChart({
     names(iris) = gsub("\\.", "", names(iris))
     p1 <- rPlot(input$x, input$y, data = iris, color = "Species",
       facet = "Species", type = 'point')
     p1$addParams(dom = 'myChart')

 ## ui.R
   headerPanel("rCharts: Interactive Charts from R using polychart.js"),

     selectInput(inputId = "x",
      label = "Choose X",
      choices = c('SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'),
      selected = "SepalLength"),
     selectInput(inputId = "y",
       label = "Choose Y",
       choices = c('SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'),
       selected = "SepalWidth")
     showOutput("myChart", "polycharts")