The ggplot2 package offers a powerful graphics language for creating elegant and complex plots.
For more information, see http://ggplot2.org/
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.
For a regular R environment, you need to install the
If you are using a Conda environment, you can also choose instead to install in the Conda section the
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
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