Interactive data visualization

In statistics, interactive data visualization enables direct actions on a plot to change elements and link between multiple plots.^[1]

Overview

Interactive data visualization has been a pursuit of statisticians since the late 1960s. Examples of the developments can be found on the ASA video lending library.^[2]

Common interactions

• Brushing: works by using the mouse to control a paintbrush, directly changing the color or glyph of elements of a plot. The paintbrush is sometimes a pointer and sometimes works by drawing an outline of sorts around points; the outline is sometimes irregularly shaped, like a lasso. Brushing is most commonly used when multiple plots are visible and some linking mechanism exists between the plots. There are several different conceptual models for brushing and a number of common linking mechanisms. Brushing scatterplots can be a transient operation, in which points in the active plot only retain their new characteristics while they are enclosed or intersected by the brush, or it can be a persistent operation, so that points retain their new appearance after the brush has been moved away. Transient brushing is usually chosen for linked brushing, as we have just described.
• Painting: Persistent brushing is useful when we want to group the points into clusters and then proceed to use other operations, such as the tour, to compare the groups. It is becoming common terminology to call the persistent operation painting,
• Identification: which could also be called labeling or label brushing,is another plot manipulation that can be linked. Bringing the cursor near a point or edge in a scatterplot, or a bar in a barchart, causes a label to appear that identifies the plot element. It is widely available in many interactive graphics, and is sometimes called mouseover.
• Scaling: maps the data onto the window, and changes in that mapping function help us learn different things from the same plot. Scaling is commonly used to zoom in on crowded regions of a scatterplot, and it can also be used to change the aspect ratio of a plot, to reveal different features of the data.
• Linking: connects elements selected in one plot with elements in another plot. The simplest kind of linking, one-to-one, where both plots show different projections of the same data, and a point in one plot corresponds to exactly one point in the other. When using area plots, brushing any part of an area has the same effect as brushing it all and is equivalent to selecting all cases in the corresponding category. Even when some plot elements represent more than one case, the underlying linking rule still links one case in one plot to the same case in other plots. Linking can also be by categorical variable, such as by a subject id, so that all data values corresponding to that subject are highlighted, in all the visible plots.

Commonly available software

• animint is an R package that takes ggplot2 graphics, converts them to JavaScript to provide interaction.
• AnyChart is a flexible, cross-platform and cross-browser JavaScript (HTML5) charting library to create interactive JS charts, maps, real-time stock charts, Gantt charts, and dashboards.
• cranvas is an R package built upon Qt libraries. It is designed to plot reasonably large amounts of data, and various different types of data.
• d3.js is lower level JavaScript routines for creating interactive graphics for data.
• Datadesk was software for statistical analysis that had both interactive graphics and modeling capabilities.
• GGobi is software for interactive graphics for multivariate real-valued data. It is written in C, and was actively developed between 1998 and 2001. There is an R package, rggobi, which provides some dual access to data structures from R.
• ggvis is an emerging R package for creating interactive graphics, building from a grammar of graphics. It generates plots written in JavaScript, and depends on the vega JavaScript libraries.
• Orange is a visual programming tool with widgets for broad interactive scientific data visualization, statistical data analysis, data mining, and machine learning.
• Google Chart provides interactive charts for browsers and mobile devices.
• Highsoft is providing tools to build interactive charts and diagrams for web and mobile projects.
• htmlwidgets is an emerging R package for building interactive web graphics, using JavaScript.
• iplots is an R package for creating interactive plots using RJava.
• MANET is software for interactive graphics for multivariate data, that can handle mixed data types. It is written in C++ using apply graphics libraries, and although it is not in general use, formed the inspiration for Mondrian.
• Mondrian is Java software for interactive graphics for multivariate data. It can handle a mix of variable types.
• plotly is for making data charts and dashboards online.
• rCharts is an R package to create, customize and publish interactive JavaScript visualizations.
• shiny is making it possible to create web apps very easily, that can contain interactive graphics, but mostly provides the GUI elements and reactivity to produce interactions.
• XLispStat was Lisp software for statistical analysis that had both interactive graphics and modeling capabilities. Other software packages for particular purposes was built on it: Vista for psychometric data, Arc for regression diagnostics.

See also

• Comparison of online interactive charts
• Comparison of JavaScript charting frameworks

References

1. Swayne, Deborah (1999). "Introduction to the special issue on interactive graphical data analysis: What is interaction?". Computational Statistics 14 (1): 1–6.
2. American Statistics Association, Statistical Graphics Section. "Video Lending Library".