ggvis 0.2

The main change is that ggvis now uses a functional approach to building plots. Instead of doing:

ggvis(mtcars, props(~wt, ~mpg)) + layer_point()

You now do:

layer_points(ggvis(mtcars, ~wt, ~mpg))

This is a bit clunky, but we streamline it by using the pipe operator (%>%, from magrittr):

mtcars %>%
  ggvis(~wt, ~mpg) %>%
  layer_points()

We think that this change will make it a little easier to create plots, and just as importantly, it's made the internals of ggvis much much simpler (so now we actually understand how it works!). As part of these changes:

• We now have a better idea of how layers should work. These are the "magic"
  bits of ggvis - they can inspect the current state of the plot, the data and
  the visual properties and decide what to do. For an example, take a look at
  layer_guess() which implements the most important parts of qvis(),
  guessing which type of layer to use to display the data.
• ggvis() and all layer functions now take props directly - you no longer
  need to use props() in everyday work.
• You can seamlessly use data transformations from dplyr: that means that
  you use group_by() to define grouping in the plot, and you can use
  filter(), summarise(), mutate() and arrange() both inside and
  outside of visualisations. See ggvis?dplyr for more examples.
• Data transformations are now handled by compute_*() functions. These
  are S3 generics with methods for data frames, grouped data frames and
  ggvis objects. This means that any transformation done by ggvis for
  a visualisation (e.g. smoothing) can also be done on ordinary datasets so
  you can see exactly what variables are being created.
• It is possible to extract all the data objects, including those that are
  created by a transformation function, with the get_data() function. This
  makes it easier to inspect and understand what's happening to your data.
• The explain() function shows the structure of the ggvis object in a
  somewhat-readable format.
• New handle_click(), handle_hover(), handle_resize() and
  handle_brush() allow you to connect callbacks to important ggvis events.
  A fully reactive interface will follow in the future.
• The process of embedding ggvis plots in shiny apps has been overhauled and
  simplified. See details in ggvis?shiny and sample apples in demos/apps/.
• A new built-in dataset: cocaine, recording cocaine seizures in the US in 2007.
  We plan to transition our dummy examples that use mtcars to something
  more useful/informative over time.