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ggraptR icon

ggraptR

ggraptR is an open source R package providing a GUI for visualization. It is based on principles of visualization analysis by Tamara Munzner, and also acts as a wrapper for functionality implemented in the grammar of graphics for R, ggplot2.

ggraptR screenshot 1

Cost / License

  • Free
  • Open Source

Platforms

  • Windows
  • Linux
  • Online
  • Self-Hosted
ggraptR screenshot 1
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Features

  1.  Data Mining
  2.  Graphical User Interface

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ggraptR information

  • Developed by

    Eugene Dubossarsky [aut, cph, cre], Julius Neggo [ctb] (Lead Developer), Howard Song [aut] (Developer), Susan Wang [ctb] (Project Manager), Jason Widjaja [ctb] (Vignette), Jose Magana [ctb] (Package Manager and Developer), Sergey Skripko [ctb] (Devel

  • Licensing

    Open Source and Free product.

  • Written in

  • Alternatives

    9 alternatives listed

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  • Supported Languages

    • English

GitHub repository

  •  77 Stars
  •  23 Forks
  •  11 Open Issues
  •   Updated  
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Our users have written 1 comments and reviews about ggraptR, and it has gotten 1 likes

ggraptR was added to AlternativeTo by dimlakgorkehgz on and this page was last updated .

Comments and Reviews

   
Top Positive Comment
dimlakgorkehgz
0

no more typing out complicated ggplots2 by hand

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What is ggraptR?

ggraptR is an open source R package providing a graphical user interface for data exploration and visualisation. It is based on principles of visualization analysis by Tamara Munzner, and also acts as a wrapper for functionality implemented in the grammar of graphics for R, ggplot2.

ggplot2 offers a wide array of marks and channels that constitute the building blocks for developing visual encodings, as well as a large selection of built-in visual idioms. However, users unfamiliar with the package may not have visibility of the options available to them, or find it challenging to express their envisioned design in their desired visual idiom.

ggraptR is designed to handle a spectrum of data visualisation needs ranging from visualising the raw values of individual variables through to fully aggregated, pivot-table style visualisations. Data volume and visual complexity is handled by the implementation of two approaches to handle visual complexity - faceting into multiple views, and reduction of items and attributes.