Ggvis
ggvis is an R package for creating interactive data visualizations and graphics in a web browser. It builds on the popular ggplot2 package but allows users to add interactivity, make visualizations reusable, and embed them in web pages.
r
ggplot2
interactive
data-visualization
graphics
web-browser
ggvis: Create Interactive Data Visualizations
ggvis is an R package for creating interactive data visualizations and graphics in a web browser. It builds on the popular ggplot2 package but allows users to add interactivity, make visualizations reusable, and embed them in web pages.
What is Ggvis?
ggvis is an R package developed by RStudio for building interactive data visualizations. It allows users to create rich graphics and plots that can be embedded in web pages and applications. Some key features of ggvis include:
- Builds on the popular ggplot2 graphics package in R, so it is easy for ggplot2 users to start using ggvis
- Interactivity - ggvis visualizations allow the end user to interact with the graphic by panning, zooming, hovering, filtering, and more
- Reusable components - ggvis graphics can be packaged into reusable building blocks that allow modular construction of complex visualizations
- Integration with Shiny - ggvis works seamlessly with Shiny web applications for R
- Exporting and embedding - visualizations can be exported as Vega-Lite JSON or embedded directly into web pages as iframes or via custom bindings
In summary, ggvis allows R users to create D3-style interactive web graphics easily by building on their existing knowledge of ggplot2. It enables powerful data exploration and presentation from within R.
Ggvis Features
Features
- Grammar of Graphics-based visualization using the ggplot2 API
- Interactivity through linking graphical elements to data
- Built on top of Shiny for reactive programming
- Can embed plots in R Markdown documents and Shiny apps
- Supports faceting, zooming, panning, etc.
- Exporting plots to SVG and PNG format
Pricing
- Open Source
Pros
Leverages ggplot2 syntax for easy plotting
Interactivity enables exploration of data
Tight integration with Shiny apps
Can create standalone visualizations to embed in web pages
Cons
Limited adoption compared to static ggplot2
Interactivity requires knowledge of reactivity in Shiny
Less customizable than D3.js for web-based graphics
Official Links
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