The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
dplyr::rbind_all() to dplyr::bind_rows(), becuase rbind_all is being removed from dplyr 0.8.4. (#482)Switched from stripped-down build of jQuery UI to a full build. (#410)
Fixed problems for R CMD check in R 3.3.0.
Remove vignettes due to R CMD check hanging.
ggvis plots can now resize their width to the containing div, with set_options(width = "auto"). Height can be set automatically as well, but it will only work properly if the containing div has a fixed height, due to the way that web browsers do vertical layout. (#316, #374, #381)
compatible with dplyr 0.4.2
input_slider has been updated to work with Shiny 0.11.
The parse spec and update events now happen in the correct order. This fixed an issue with plots flashing. (#351)
Pointer events are now allowed in tooltips (#349)
Updated to Vega 1.4.3 and D3 3.5.2.
Startup messages are now shown only one in ten times. (#302)
Added new dplyr verbs: distinct, rename, slice, and transmute. (#299)
ggvis now gives a warning when key prop values are not unique. (#295)
Boxplots are now supported, with layer_boxplots() and compute_boxplot().
Much better support for data objects with zero rows.
Added support for displaying ggvis plots in dynamic UI in Shiny apps. (#165)
compute_bin() uses width instead of binwidth, and boundary instead of origin. (#268)
compute_bin() now defaults to pad = FALSE
compute_model_predictions() always returns a result, even if there’s an error (#102).
filter() is no longer imported and re-exported from dplyr. This means that to use filter() with ggvis object you’ll need to make sure to load dplyr first.
compute_smooth() supports more complex formulas. (#209)
compute_bin() and compute_count() now preserve date and time properties. (#235)
export_png() and export_svg() now work. This requires node.js, and vega must be installed via npm.
Legend hiding is fixed. (#218)
count_vector() preserves the order of factor levels. (#223)
compute_bin() now ignores NA’s. (#148)
layer_bars() now uses correctly uses fill prop when it is passed to the function, and not inherited. (#201)
compute_count() drops unused factor levels. (#201)
compute_bin() and compute_stack() no longer give warnings and errors for zero-row data frames. (#211)
Range calculation for zero-length vectors now returns NULL instead of throwing an error.
Objects imported from the magritter and dplyr packages are now properly re-exported.
Using “.” in column names now works. (#246)
Un-exported :=, to avoid possible conflict with data.table.
Updated to Vega 1.4.2. (#193 and #217)
Switched from RJSONIO to jsonlite.
Switched to the new non-standard argument evaluation strategy from dplyr 0.3, using the new lazyeval package.
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.
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/.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.