Data Visualization and D3.js Newsletter Issue 165

DashingD3js.com Weekly Data Visualization and D3.js Newsletter

Hi Friends -


Welcome to issue 165 of the DashingD3js.com Weekly Newsletter.
 

Onwards to this week's links...
 

Featured
 

  • Six Approaches To Visualising Complexity
    As information designers, we often create visual representations of meaning to make hard-to-understand information more accessible to a wider audience...Particularly focusing on scientific visualisations, Harry Robin (1993) discusses six approaches that have been used ‘from preliterate to succeeding cultures’ to convey scientific phenomena...Observation, Induction, Methodology, Self-illustrating phenomena, Classification, Conceptualization...
 

Data Visualization Reading and Videos
 

  • How To Make Extreme Numbers Resonate
    Both colossally large and infinitesimally small numbers can be hard to fathom, because they’re so abstract. Visualization can make data at the extremes easier to grasp. The key to doing it well is finding the right scale and the right approach. Here are three examples of visuals that make huge numbers, tiny numbers, and moving numbers easier to grasp...
  • Minute By Minute Point Differentials Of 2015 NBA Games
    Since seeing Allison McCann and Mike Beuoy's Every NBA Team’s Chance Of Winning In Every Minute Across Every Game last year, I've been trying to think of ways of seeing an entire season of basketball at once. Beyond each team's average chance of winning over time, I was curious about what their distribution of chances looked like...the key trick of this chart - showing a changing distribution by stacking colored objects...
  • Causes of Death
    There are many ways to die. Cancer. Infection. Mental. External. The Centers for Disease Control and Prevention classifies the ways into 113 causes, which are grouped into 20 categories of disease and external causes. The CDC's Underlying Cause of Death database provides estimates for the number of people who die due to each of the causes...The chart below shows how cause of death varies across sex and race, based on mortality data from 2005 through 2014. Select a group to see the changes. Select causes to see them individually...
  • Interactive Skymap
    A free, open source map of the sky, adaptable (see form below) and zoomable/draggable with mouse/gestures. Done with the help of the d3.js visualization library and the GeoJSON data format. Try it out below, but make sure to maximize your browser window and click/tap 'Make full width' to enjoy it in its full glory!..
  • The 9 Best Data Visualization Examples From 2015
    The folks behind 2015’s best data visualizations have done the following things well: a) Used design and/or data science to make insights from a data set easily understood, b) Designed a visualization that tells the whole story by itself, meaning it does note need added context to be compelling, and c) Delivers the data in a way that surprises, startles, or is (totally subjectively) awesome aesthetically...Here are a nine of our favorites from 2015...
 

D3.js Reading and Videos
 

  • D3 Dispatch (The D3 Event System) Notes
    Learning d3 events this afternoon. These are the notes I'm taking while I'm learning it. I hope it helps...It's called d3.dispatch. d3.dispatch('eventname1','eventname2') returns an object that manages setting event handlers, and dispatching events. The event system isn't global (unlike in most frameworks, where the event system appears to be globally available)- it's contained entirely within the object...
  • Sporthorse Foal Registrations II
    An iteration on Sporthorse Foal Registrations by phoebebright...An example of using d3 + crossfilter together to make an svg map with linked bar charts...
  • EventDrops
    A time based / event series interactive visualization using d3.js. Use drag and zoom to navigate in time...
  • How To Make Tufte's Discrete Sparklines Using D3.js
    I've been working on a dashboard and found a data set that would look great represented as a discrete sparkline. This type of visualization is great for quickly showing trends at a glance in discrete variable datasets...

Hope that you had a great past week and that next week is even better!

Wishing you the best, 
Sebastian Gutierrez
@DashingD3js
www.dashingd3js.com

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