Using ggvis

ggvis is a data visualization package for R

For more information, see

Installing ggvis

Installing the ggvis package

The ggvis package is not installed by default. The recommended way to install it is to use a code environment

See how to install R packages

  • For a regular R environment, you need to install the ggvis package

  • If you are using a Conda environment, you can also choose instead to install in the Conda section the r-ggvis package.

Installing the frontend dependencies

To work, ggvis first needs some frontend libraries, that need to be preinstalled once.

To install the dependencies, open a R notebook and run



It is not possible to run this if your DSS instance has the User Isolation Framework enabled.

In that case, your DSS administrator needs to run this from a command-line ./bin/R prompt, after setting the DIP_HOME env variable to the location of the DSS data directory

Displaying charts in a Jupyter notebook

ggvis charts will not work properly if you only enter it in a Jupyter notebook

Instead, use the dkuDisplayGgvis method.

For example; to display the first example in the ggvis documentation:


    # Prepare the chart
    chart <- mtcars %>% ggvis(~wt, ~mpg) %>% layer_points()

# And display it

Displaying charts on a dashboard

googleVis charts generated using R code can be shared on a DSS dashboard using the “static insights” system.

Each chart can become a single insight in the dashboard.

To do so, create static insights

# Prepare the chart
chart <- mtcars %>% ggvis(~wt, ~mpg) %>% layer_points()

# Save it as an insight
dkuSaveGgvisInsight("my-ggvis-plot", chart)

From the Dashboard, you can then add a new “Static” insight, select the my-ggvis-plot insight

Plots can be donwloaded in SVG or PNG format

Refreshing charts on a dashboard

You can refresh the charts automatically on a dashboard by using a scenario to re-run the above piece of code.

This call to dkuSaveGgvisInsight code can be:

  • In a DSS recipe (use a regular “Build” scenario step)

  • In a Jupyter notebook (use a “Export notebook” scenario step)