Object Constancy The chart shows multiple slices of a dataset, transitioning smoothly when the age bracket changes. The x-axis rescales to accommodate the change in maximum value, while bars reshuffle along the y-axis to preserve sorted order. Graphical elements enter and exit: Hawaii enters the top ten for the 65 and older age brackets, but fades out in younger ones. The axis ticks change suitably, from whole percentages to fifths. Old values fade-out while the new values fade-in, both translating to preserve a valid display across the transition. Animated transitions are pretty, but they also serve a purpose: they make it easier to follow the data. #Key Functions To achieve object constancy with D3.js, specify a key function as the second argument to selection.data. A suitable key function for this data returns the State property, a FIPS code: function key(d) { return d.State; } When you join the top-ten states to the bars, three selections are returned: #When Constancy Matters
Mike Bostock Please find my recent work on Observable. Past Work April 28, 2017A Better Way to Code December 9, 2016Command-Line Cartography March 9, 2016What Makes Software Good? December 28, 2015Introducing d3-scale December 3, 2015Introducing d3-shape November 23, 2015Let’s Make a (D3) Plugin December 27, 2014Mapping Every Path to the N.F.L. December 20, 2014How Each Team Can Make the N.F.L. November 5, 2014The Most Detailed Maps You’ll See From the Midterm Elections November 3, 2014How To Scroll September 19, 2014‘Stop-and-Frisk’ Is All but Gone From New York July 26, 2014Mapping the Spread of Drought Across the U.S. June 26, 2014Visualizing Algorithms June 15, 2014Three Little Circles May 22, 2014Is It Better to Rent or Buy? May 6, 2014Let’s Make a Bubble Map April 22, 2014Who Will Win The Senate? March 7, 2014Let’s Make a Block Feburary 14, 2014Front Row to Fashion Week February 14, 2014Sochi 2014: Interactive Stories January 11, 2014Taking the Battle to the States November 5, 2013Let’s Make a Bar Chart #Examples
Stephen Boak — Easy as (a) Pie Through my work at Boundary I’ve started playing with the D3 visualization library, trying to build some interesting visualizations for our real-time network monitoring dashboard. I’ve really been enjoying D3 and Mike Bostock, the creator, has been extremely helpful to me and lots of others in the d3-js Google Group. Since the library is new, I hope the series of experiments I post here can be helpful in spreading some knowledge and awareness of the library. What You’re Building My first experiment is an animated pie chart meant to visualize a breakdown of incoming network traffic by port. Here is the completed pie chart: I’ve also posted the complete source code on JSFiddle. Building the Visualization For the sake of brevity I’ll skip over some of the details, but feel free to contact me with any questions. There are a couple of D3 helper functions that I use. The second is a function to return a color value from an ordinal scale of 20 preset colors: Improvements
D3.js Zooming and panning a collapsible tree diagram Creating Animations and Transitions With D3 For the ambitious, this is a great intro to creating your own animations and transitions in d3.js. If you get in over your head, drop by the Visually Marketplace to hire one of our talented developers. In interactive visualisation, there is the word reactive. Well, maybe not literally, but close enough. The fact is that reactivity, or the propension of a visualisation to respond to user actions, can really help engage the user in a visualisation, and help them understand its results. So I’ll go ahead and state that animation, if done right, can make any interactive data visualization better. How is that? When coupled with interaction, it’s a very useful way to give feedback to the user. That said, animation can definitely ruin your visualization, too. Animation is very prominent. So we’ve seen how animation is helpful in data visualization. The principle If you know how to draw in d3, you almost know how to animate. Animations are called “transitions” in d3 for a reason. How this is done
Working with data in protovis For the past year or so I have been dabbling with protovis. I don’t have a heavy CS background but protovis is supposedly easy to pick up for people like me, who are vaguely aware that computers can make calculations but who need to check the manual for the most mundane programming instructions. I found was while it’s reasonnably easy to modify the most basic examples to make stuff happen, it is much harder to understand or adapt the more complex ones, let alone to create a fairly complex visualization. The stumbling block for me was the use of the method data. So I started documenting my struggle with data, first for my own use, and eventually realized I could share what I learned. I split this tutorial in 5 parts. And as a bonus, I have also deconstructed several interesting (but not immediately accessible) examples from the gallery: To make the best use of this material, it would be helpful to know a bit about protovis. The protovis documentation.
Creating a brush region programatically For auto-redraw such that the focus of a 1D brush matches the graphed domain, you need to do as follows: (1) Apply the brush scale to the graphed domain (i.e., the brush extent) --> store in var brushExtent; (units: pixels) (2) Within the brush element (class='brush'), select the <rect> with class='extent'. (3) If the brush is horizontal (i.e., for the x-axis), set the 'x' attribute to the first value in brushExtent. This moves the start position of the focus <rect> to match the graphed domain. (4) Next, set the 'width' attribute of the extent to brushExtent[1] - brushExtent[0]. (*) The extent should now programmatically match the graphed domain. I use this procedure as part of a resize function. - mostra testo citato - On Tuesday, March 26, 2013 9:52:38 PM UTC, andrew payne wrote: Hi Mike,I'm trying to programmatically set a brush region at the initialization stage. Setting the brush extent programmatically does not trigger automatic redraw and does not dispatch any events.
Histogram Histogram This chart shows a histogram of a Bates distribution. The data is randomly generated. See also this histogram of a log-normal distribution of time durations. index.html# Jim Vallandingham Sentence Drawings Stefanie Posavec is known for her manually curated and hand drawn visualizations that illuminate variations of structure and form in great works of literature. In this remix, automation is used in place of thoughtful analysis to recreate a selection of her First Chapters, in which a layout technique she calls "sentence drawings" is used to display the first chapters of classic books. Implemented in D3. Automatic Sentence Drawings Track Tag Love An experiment that allows users to explore their most-listened to songs, and the tags associated with these songs. User names are faked - but the rest of the data is real. Visualization includes a force-directed layout with tag nodes suspended in a circular orientation. Song nodes are drawn towards the tags they are associated with. Track Tag Love Dallas Neighborhood Comparison Tool This piece provides both scatter plot and interactive map, allowing the user to zoom in on one of over 500 neighborhoods in the metro area. Feltronifier