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Animated interactive ggplots

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Faizan Uddin Fahad Khan edited this page Apr 1, 2019 · 12 revisions

Background

animint2 is an R package for making interactive animated data visualizations on the web, using ggplot syntax and two new keywords:

  • showSelected=variable means that only the subset of the data that corresponds to the selected value of variable will be shown.
  • clickSelects=variable means that clicking a plot element will change the currently selected value of variable.

Toby Dylan Hocking initiated the project in 2013, and Susan VanderPlas (2013), Carson Sievert (2014), Tony Tsai (2015), Kevin Ferris (2015), Faizan Khan (2016-2017), and Vivek Kumar (2018) have provided important contributions during previous GSOC projects.

The animint2 manual is the definitive reference on how to design data visualizations using animint2.

Related work

Standard R graphics are based on the pen and paper model, which makes animations and interactivity difficult to accomplish. Some existing packages that provide interactivity and/or animation are

  • Non-interactive animations can be accomplished with the animation package (animint provides interactions other than moving forward/back in time).
  • Some interactions with non-animated linked plots can be done with the qtbase, qtpaint, and cranvas packages (animint provides animation and showSelected).
  • Linked plots in the web are possible using SVGAnnotation or gridSVG but using these to create such a visualization requires knowledge of Javascript (animint designers write only R/ggplot2 code).
  • The svgmaps package defines interactivity (hrefs, tooltips) in R code using igeoms, and exports SVG plots using gridSVG, but does not support showing/hiding data subsets (animint does).
  • The ggvis package defines a grammar of interactive graphics that relies on shiny’s reactivity model for most of its interactive capabilities (animint does not need a shiny server).
  • Vega can be used for describing plots in Javascript, but does not implement clickSelects/showSelected (animint does).
  • RIGHT and DC implement interactive plots for some specific plot types (animint uses the multi-layered grammar of graphics so is not limited to pre-defined plot types).

For even more related work see the Graphics and Web technologies task views on CRAN, and Visualization design resources from the UBC InfoVis Group.

GSOC coding project: new animint2 features

The goal of this GSOC project is to implement new features for animint2 in order to make it possible to do more kinds of interactive data visualization, and to more easily maintain the code.

An ideal student project will also plan to write some tests and documentation (vignette, web page, blog). Some important items from the TODO list:

  • object-oriented implementation of geoms – currently they are implemented with a lot of conditional code (if geom is point then, else if geom is rect then, etc). Move that code to a new method (e.g. animint_compile) in the geom object definition, so the code is easier to understand and maintain.
  • Upgrade to d3.js version 4, which makes data join easier. The new merge function allows combining the ENTER and UPDATE selections, which reduces duplication and can make animint.js code easier to understand, as explained in this article.
  • New aesthetics that only make sense on the web/SVG (not in ggplot2), for example an aesthetic for stroke-opacity and fill-opacity.
  • Support for multiple clickSelects aesthetics per geom.
  • Currently, selected items in Animint are shown with a black border outline stroke for rectangles, and with 0.5 more alpha transparency for all other geoms. This should be configurable using new aesthetics such as selected.color, selected.alpha, etc.
  • introduce dependency on data.table, so that data visualizations with large data sets can be compiled faster. The example to benchmark would be the PeakSegJoint data viz.
  • New hoverSelects aesthetic which updates the selection just by hovering the mouse over an element, including tests that simulate mouseover/mouseout events.
  • Previously Faizan implemented updating of axes/legends after changing the currently displayed data subset. Currently the computations are done in the compiler but there are some limitations, so it would be preferable to move the computations to the renderer.
  • Compute stats based on the current subset, e.g. total homicides, maybe animint syntax like this.

Any other ideas for improving Animint are welcome as well!

Expected impact

Animint already provides useRs with some unique features for interactive data visualization. At the end of GSOC, the animint2 package will be easier to maintain, and have even more features, tests, and documentation.

Frequently Asked Questions

Does a student need to know both JavaScript and R?

YES. If you don’t know JavaScript then I suggest you read some tutorials, e.g. Mozilla JavaScript basics, W3Schools, mbostock’s blocks examples.

Mentors

Please get in touch with Toby Dylan Hocking <tdhock5@gmail.com> and Faizan Khan <faizan.khan.iitbhu@gmail.com> after completing at least one of the tests below.

Tests

Do one or several — doing more hard tests makes you more likely to be selected.

  • Easy: use animint2 to visualize some data from your domain of expertise, and upload your visualization to the web using animint2gist. Show an example of an error that you see when animint2 is loaded/attached at the same time as standard ggplot2.
  • Medium: translate an example of the animation package into an Animint. Do not do one of the examples that has already been ported. Post a link to your result on the Ports of animation examples page on the Animint wiki.
    • look at source code of one of the animation package functions e.g. grad.desc() for a demonstration of the gradient descent algorithm. Translate the for loops and plot() calls into code that generates data.frames. In the grad.desc() example, there should be one data.frame for the contour lines, one for the arrows, and one for the values of the objective/gradient at each iteration.
    • Use the data.frames to make some ggplots. In the grad.desc() example, there should be one ggplot with a geom_contour and a geom_path, and another ggplot with a geom_line that shows objective or gradient value versus iteration, and a geom_tallrect in the background for selecting the iteration number.
    • Make a list of ggplots and pass that to animint2dir. For the grad.desc() example the plot list should be something like list(contour=ggplot(), objective=ggplot(), time=list(variable=”iteration”, ms=2000)).
  • Hard: write a testthat unit test based on one of your Animint visualizations. Fork animint and add a renderer test (using animint2HTML) to tests/testthat/test-renderer3-YOUR-TEST.R, then send us a Pull Request. Upload a screencast to youtube that shows you executing your test from the R command line (make sure to show two windows, a remote-controlled browser window rendering the data viz, and the R script/terminal that executes the test code).

Solutions of tests

  • Students, please post a link to your test results here.

Name: Anshul Goel

Course: BS_MS Student in Economics (majorly Econometrics)

University: Indian Institute of Technology, Kanpur

email: anshul96go@gmail.com, ganshul@iitk.ac.in

Easy Test: Result Code

Medium Test: link

Hard Test: Supposedly, issue with test_init()

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