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Did you know that both ggvis and plotly can guess which geometry you are looking for? Based on the basic aesthetics defined, they make a guess and adopt certain geometry. They look at how many variables of what kind (discrete or continuous) were inputted, and for some combinations they are able to make a guess.For the nearest example they would have guessed points geometry.

Figures breed by both packages will be displayed by the Viewer tab if you're using RStudio (They are interactive! Try hoovering the mouse over a plotly figure). Figures can be exported as web pages. Other than that, they can be exported as PNG, JPEG, and BMP, therefore losing the interactive property. 

This recipe aimed to demonstrate how to construct plots using ggplot2 primitives, and build similar graphs using other packages. A question you should always ask yourself is if the geometry adopted goes along with the data used. In other words, if the graphic tells the story that you are willing to.

The recipes's goal was to introduce you to the graphical primitives of ggplot2 and draw simple graphics by using only primitives. Additional goal was to draw related graphics using the ggvis and plotly packages.

The next chapters pe deeper; each one shall tackle some families of graphics, highlighting nuts and bolts in the way to building high quality plots. As the book advances, so does the complexity involved. At some point, we are going to be plotting interactive globes, 3D surfaces and developing web applications. I find it pretty sicking cool, hope you enjoy it.

Chapter 2, Plotting Two Continuous Variables, takes care of scatterplots. It's a very popular kind of plot, and very useful too, but there is a big problem: over-plotting. Following chapter will not only teach how to craft scatterplots, but also teach how to deal with such problem and how to improve scatters by deploying marginal plots. Let it rip!