Using ggplot2¶

The ggplot2 package offers a powerful graphics language for creating elegant and complex plots.

For more information, see http://ggplot2.org/

Installing ggplot2¶

The ggplot2 package is installed in the builtin R environment of DSS. If you are using a custom code environment, you’ll need to install it.

See how to install R packages

For a regular R environment, you need to install the ggplot2 package
If you are using a Conda environment, you can also choose instead to install in the Conda section the r-ggplot2 package.

Displaying charts in a Jupyter notebook¶

ggplot2 charts display naturally inthe Jupyter notebook.

For example, if “df” is a dataframe obtained with dkuReadDataset with columns “age” and “price”, you can make a scatter plot with a smoothing line with

library(ggplot2)

ggplot(df, aes(x = age))
        + geom_point( aes(y = price))
        + geom_smooth( aes(y = price))

Displaying charts on a dashboard¶

ggplot2 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

You can either save the last displayed plot:

# Display a plot
ggplot(df, aes(x=myXColumn, y=myYColumn)) + geom_point()

# Save it as an insight
dkuSaveGgplotInsight("my-ggplot2-plot")

Or save an explicit plot object:

# Prepare a plot object
gg <- ggplot(df, aes(x=myXColumn, y=myYColumn)) + geom_point()

# Save it as an insight
dkuSaveGgplotInsight("my-ggplot2-plot", gg)

From the Dashboard, you can then add a new “Static” insight, select the my-ggplot2-plot insight

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 dkuSaveGgplotInsight code can be:

In a DSS recipe (use a regular “Build” scenario step)
In a Jupyter notebook (use a “Export notebook” scenario step)

Using in Shiny¶

ggplot2 can be used directly in Shiny, inside of a renderPlot block.