Data Visualization and D3.js Newsletter Issue 141 Weekly Data Visualization and D3.js Newsletter

Hi Friends -

Welcome to issue 141 of the Weekly Newsletter.

Onwards to this week's links...


  • A Visual Introduction To Machine Learning
    In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions...Keep scrolling....Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco...
  • Difference Charts With D3.js: Science Versus Style
    A difference chart is a variation on a bivariate area chart. This is a line chart that includes two lines that are interlinked by filling the space between the lines...All that remains is for us to work out how d3.js can help us out by doing the job programmatically...

Data Visualization Reading and Videos

  • Visualization Of Large Datasets With tabplot
    The tableplot is a visualization method that is used to explore and analyse large datasets. Tableplots are used to explore the relationships between the variables, to discover strange data patterns, and to check the occurrence and selectivity of missing values...The tableplot is a powerful visualization method to explore and analyse large multivariate datasets. In this vignette, the implementation of tableplots in R is described, and illustrated with the diamonds dataset from the ggplot2 package.
  • Making Static/Interactive Voronoi Map Layers In ggplot/leaflet
    Despite having shown various ways to overcome D3 cartographic envy, there are always more examples that can cause the green monster to rear it’s ugly head...Take the Voronoi Arc Map example...We can overlay a Voronoi tessalation on top of a map in R as well thanks to the deldir package (which has been around since the “S” days!)...
  • A Compiler Infrastructure For Data Visualization
    There are many data visualization tools out there. Yet, I believe we're still lacking a robust, scalable, and cross-platform visualization toolkit that can handle today's massive datasets...These are all the reasons why we've started the VisPy project more than two years ago. We wanted to design a high-performance visualization library in Python that would handle massive datasets well, and where 2D and 3D visualization would both be first-class citizens. The main idea of VisPy is to transparently leverage the massively parallel graphics card through the OpenGL library for data visualization purposes...
  • Good Examples Of Fiscal Data Visualisation?
    I'm currently collecting examples of fiscal data visualisations for a research project. The project is looking at how different kinds of visualisations and visual elements are used to narrate public finances - whether by journalists, civil society organisations or public sector bodies...I've started out with an initial list of 165 different projects...The list so far is available here...
  • Align Against A Common Baseline
    The first article in my Feedly was by FiveThirtyEight and the graph that appeared with it caused me to click for details. Here's the graph that caught my attention...I think it can be made better by adhering to one recommendation I find myself often voicing to workshop participants: Think about what you want your audience to be able to easily compare. Put those things as physically close together as you can and align them along a common baseline....

D3.js Reading and Videos

  • D3-Labeler - D3 Plug-in For Automatic Label Placement Using Simulated Annealing
    A D3 plug-in for automatic label placement using simulated annealing that easily incorporates into existing D3 code, with syntax mirroring other D3 layouts...To automatically place labels, users declare a labeler (simulated annealing) layout, input label and anchor positions, the figure boundaries, and the number of Monte Carlo sweeps for simulated annealing. The general pattern is as follows...
  • "Building Bl.ocks" Kickstarter By Ian Johnson (@enjalot) Update
    Well, here we are at the end of the first week and we've hit both stretch goals at 205% funding! The momentum has been infectious and I've been coding furiously. We are a lot closer to a functional prototype than I thought we'd be at this point. I'm happy to report that the backend code from Tributary was very easy to port and I can effectively fork or edit gists! (I just pushed an update to the README for running it yourself if you'd like to try!)...

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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