Data Visualization in R and Python

Part IIInteractive Graphics with Altair

With Altair, a Python-based graphical library, we enter into the realm of interactive graphics with graphics that take the form of HTML or JSON objects (other formats are available). We will still see some static graphics, similar to those presented in Part 1 of the book, because we need them as building blocks for interactive ones, however, the main interest now is not specifically on them but on the logic and mechanisms supporting the interactivity of those visual objects with actions performed by the observer. Hence, graphics become responsive to user’s choices, they dynamically adapt through user’s inputs, which may take different forms like mouse clicks and hovering, or gestures on the touchpad/touchscreen.

This novelty is not a small improvement over static graphics; instead, it is a true change of perspective. While static graphics still have roots in traditional schemas and diagrams, for some, those roots may look distant. Interactive graphics, being reactive and dynamic, cater to the web and offer a completely different user experience. It is no longer just a visual language for communicating information to passive observers, but an interactive association between visual objects and active observers involving actions and responses that should be imagined, designed, and developed since the beginning of a data visualization project. Interactivity is not just an add-on to static objects; it is the nature of these dynamic visual ...