v0.3.0.1

• Reconcile shiny dependency at run-time, not build time.

v0.3

• add_guide_axis() and add_guide_legend() have been replaced by
  add_axis() and add_legend(). Also, the interface for add_legend() has
  been simplified.
• Added hide_axis() and hide_legend() functions.
• When marks with a band() prop are added, the appropriate scale is
  automatically set to have points = FALSE. (#128)
• Continuous scales have a multiplicative expansion factor added by default,
  with the expand parameter of scale functions.
• Relative x and y scales for positioning of graphical elements can be added
  with add_relative_scales().
• Added support for strokeDash property.
• Added support for controlling width and height of image marks.
• prop() objects have been modified so that they always record which scale
  they use.
• Removed qvis(): now the default behaviour of ggvis() is to add
  layer_guess() if there are no layers on the plot already.
• add_dscale() has been replaced with scale_quantitative(),
  scale_nominal(), scale_ordinal(), and similar.
• Reactive expressions can be used for scale domains. This allows the scale
  domain to change dynamically.
• Axis and legend properties are fixed. (#90)
• Histograms allow stacking.
• Dynamic plots now with with by_group. (#71)
• Gear icon displays properly in Windows. (#159)
• layer_bars() are now symmetrical about the x tick positions.
• New singular() and corresponding scale_singular() make it easier to
  draw plots where x or y are constant (and hence uninteresting), such as
  for a 1d dot plot (#127).
• compute_histogram() gains pad argument to control whether empty bins
  on either side of the data extents are added. This is useful for frequency
  polygons and to ensure that histograms don't jam up against the axes.

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.

ggvis version 0.1

First alpha release of ggvis