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ggvis

Status

ggvis is currently dormant. We fundamentally believe in the ideas that underlie ggvis: reactive programming is the right foundation for interactive visualisation. However, we are not currently working on ggvis because we do not see it as the most pressing issue for the R community as you can only use interactive graphics once you've successfuly tackled the rest of the data analysis process.

We hope to come back to ggvis in the future; in the meantime you might want to try out plotly or creating inteactive graphics with shiny.

Introduction

The goal of ggvis is to make it easy to describe interactive web graphics in R. It combines:

a grammar of graphics from ggplot2,
reactive programming from shiny, and
data transformation pipelines from dplyr.

ggvis graphics are rendered with vega, so you can generate both raster graphics with HTML5 canvas and vector graphics with svg. ggvis is less flexible than raw d3 or vega, but is much more succinct and is tailored to the needs of exploratory data analysis.

If you find a bug, please file a minimal reproducible example at https://github.com/rstudio/ggvis/issues. If you're not sure if something is a bug, you'd like to discuss new features or have any other questions about ggvis, please join us on the mailing list: https://groups.google.com/group/ggvis.

Installation

Install the latest release version from CRAN with:

install.packages("ggvis")

Install the latest development version with:

# install.packages("devtools")
devtools::install_github("rstudio/ggvis")

Getting started

You construct a visualisation by piping pieces together with %>%. The pipeline starts with a data set, flows into ggvis() to specify default visual properties, then layers on some visual elements:

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

The vignettes, available from https://ggvis.rstudio.com/, provide many more details. Start with the introduction, then work your way through the more advanced topics. Also check out the various demos in the demo/ directory. See the basics in demo/scatterplot.r then check out the the coolest demos, demo/interactive.r and demo/tourr.r.

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Version

Install

install.packages('ggvis')

Monthly Downloads

3,605

Version

0.4.9

License

GPL-2 | file LICENSE

Maintainer

Last Published

February 5th, 2024

Functions in ggvis (0.4.9)

Automatically group data by grouping variables

default_options

Default options

Create an axis_props object for controlling axis properties.

compute_tabulate

Count data at each location of a categorical variable

ggvisControlOutput

Create a ggvis control output element in UI

compute_model_prediction

Create a model of a data set and compute predictions.

Generate sequence of fixed size intervals covering range.

Stack overlapping data.

Visualise a data set with a ggvis graphic.

Create a new interactive "input" object.

Print out the structure of a ggvis object in a friendly format

Handle mouse actions on marks.

Handle brush events on a visualisation.

Export a PNG or SVG from a ggvis object

Handlers and interactive inputs for plot sizing.

Create an interactive checkbox.

Create a broker object

Send a message to ggvis running on client

ggvisOutputElements

Create HTML elements for ggvis output

Determine if an object is a broker object

Determine if an ggvis is dynamic (i.e. needs to be run in a shiny app)

Display data with bars (a barchart).

Display data with a boxplot.

Create an interactive text or numeric input box.

Interactive inputs bound to arrow keys.

Create an axis_props object for controlling legend properties.

add_guide_legend

Defunct function for adding a legend

Create an interactive slider.

Cocaine seizures in the US.

Align positions using length.

Create interactive control to select one (or more options) from a list.

Layer lines on a plot.

layer_model_predictions

Overlay model predictions or a smooth curve.

Create a linked brush object.

Define padding.

Create a new "mark" object.

Get data from a ggvis object

View in a ggvis plot in the browser.

knit_print.ggvis

Knit print method for ggvis plots.

Create a property.

is.scaled_value

Tests whether an object is a scaled_value object

Manage a list of properties.

Compute the "resolution" of a data vector.

Dplyr verbs for ggvis.

Add a numeric scale to a ggvis object.

Add a ordinal, nominal, or logical scale to a ggvis object.

sidebarBottomPage

Create a page with a sidebar

Tests whether an object is an axis_props object

layer_densities

Transformation: density estimate

Add a date-time scale to a ggvis object.

Determine if range of vector is close to zero, with a specified tolerance

Tools to save and view static specs.

Create a new layering function.

Create new prop object

vega_data_parser

Determine the vega data type for a vector

Set options for a ggvis plot

Determine the "type" of a vector

Create a subvisualisation.

scaletype_to_vega_scaletype

Given the type of a ggvis scale, get the name of its corresponding vega scale

Waggle back and forth between two numbers

Explain details of an object

Create a scaled_value object

Print out the vega plot specification

Send a message to the client to show or hide a tooltip

Add a scale to a ggvis plot

Create a new ggvis_scale object.

Guess the right type of layer based on current properties.

Divide data into groups.

layer_histograms

Display binned data

Property domain.

is.legend_props

Tests whether an object is a legend_props object

Is an object a ggvis object?

propname_to_scale

Convert the name of a property to the name of its default scale.

set_scale_label

Set the label for a scale

Connect a ggvis graphic to a shiny app.

Add a vega legend specification to a ggvis plot

Add visual properties to a visualisation

Add arbitrary scales to ggvis.

Add a vega axis specification to a ggvis plot

add_relative_scales

Add x_rel and y_rel scales

Add tooltips to a plot.

Defunct function for adding an axis

Add dataset to a visualisation

Bin data along a continuous variable

compute_boxplot

Calculate boxplot values

Coerce an ggvis object to a vega list.

Count data at each location

compute_density

Compute density of data.