Dynamic Visualization Libraries

ggplot2 is easily the most popular library for producing data visualizations in R. That said, ggplot2 is used to produce static visualizations: unchanging “pictures” of plots. Static plots are great for for explanatory visualizations: visualizations that are used to communicate some information—or more commonly, an argument about that information. All of the above visualizations have been ways for us to explain and demonstrate an argument about the data (e.g., the relationship between car engines and fuel efficiency).

Data visualizations can also be highly effective for exploratory analysis, in which the visualization is used as a way to ask and answer questions about the data (rather than to convey an answer or argument). While it is perfectly feasible to do such exploration on a static visualization, many explorations can be better served with interactive visualizations in which the user can select and change the view and presentation of that data in order to understand it.

While ggplot2 does not directly support interactive visualizations, there are a number of additional R libraries that provide this functionality, including:

• ggvis is a library that uses the Grammar of Graphics (similar to ggplot), but for interactive visualizations.
• plotly is a open-source library for developing interactive visualizations. It provides a number of “standard” interactions (pop-up labels, drag to pan, select to zoom, etc) automatically. Moreover, it is possible to take a ggplot2 plot and wrap it in Plotly in order to make it interactive. Plotly has many examples to learn from, though a less effective set of documentation.
• htmlwidgets provides a way to utilize a number of JavaScript interactive visualization libraries. JavaScript is the programming language used to create interactive websites (HTML files), and so is highly specialized for creating interactive experiences.