Data Visualization and D3.js Newsletter Issue 45 Weekly Data Visualization and D3.js Newsletter

Hi Friends -

Welcome to the forty-fifth issue of's Weekly Newsletter

Onwards to this week's links...


  • Why We Hate Infographics (And Why You Should)
    Infographic is not another name for Data Visualization. An infographic is a picture, it's made with a graphical design package like Illustrator, not with a data analysis package. It's usually embedding several sub-images, aiming at delivering a specific message. But a picture is worth a thousand words, right?
  • D3.js Screencasts To Make You Awesome
    Master D3 With Weekly Bite-Sized D3 Videos (26 Videos and counting); Fresh New Screencasts Every Monday; HD Quality Videos (1080p); Access To Full Catalog And New Weekly Additions Made For Beginner, Intermediate and Advanced D3.js Developers only $9 per month...

Data Visualization Reading and Videos

  • Visualization: Big Money In Tax Breaks
    The federal tax code includes hundreds of tax breaks designed to encourage certain activities that lawmakers deem beneficial to society. Tax deductions, credits, and exclusions are all different kinds of tax breaks.  This visualization, based on NPP’s tax break dataset, shows some of the most expensive tax breaks, who benefits, and how their costs have changed over time.
  • Looking Beyond Radar Charts
    Since I started working in coffee I’m very well aware of these charts.  This blog post is basically a collection of reason why I think one should avoid using radar charts.  When it comes to coffee, cocoa or any other food product, radar charts are omnipresent and the de facto standard for documenting sensorial profiles.  I think we can do better.  There are superior solutions and we should embrace them.
  • Five Steps to Storytelling with Data
    Data visualization has come a long way since its formative days as the basic pie chart invented over 200 years ago. So regardless of whether you’re bringing shape to data on health and wellbeing, shopping habits, or in editorial, Fjord has identified five core principals to follow when embarking on a data visualization challenge...
  • prettyplotlib: Painlessly Create Beautiful Matplotlib Plots
    A while back I wrote a few tutorials about how to work with Python’s plotting library, matplotlib, so that it behaves nicely and produces beautiful plots. Well, I got tired of tweaking every single figure individually so I wrote this library, prettyplotlib to have pretty default plots in Python’s matplotlib.
  • Easy Web Applications In R
    Shiny makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use. Let your users choose input parameters using friendly controls like sliders, drop-downs, and text fields. Easily incorporate any number of outputs like plots, tables, and summaries.

D3.js Reading and Videos

  • Using D3 To Capture User Input
    Using D3 for visualizing the output is quite straight forward. But then, we wanted to have some easy to use user input - we graded mood on a scale, but how to represent it best? Numbers from 1-x as they are often used didn’t seem very intuitive (is 1 best or 10 best?). After thinking about it for a while we had an idea of using a smiley as a slider - the smiley would smile if happy and look sad if dragged to a sad status...
  • Reusable Responsive Charts With D3.js
    Colin Gourlay / @collypops presentation - Using D3.js and d3.Chart to create visualizations that can be customized to use different features / styles and fit multiple viewports.

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

Wishing you the best, 
Sebastian Gutierrez

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