• VVSOR

    Data Visualization Workshop

    Welcome!

  • Topics we will cover

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    The superpowers and limitations of our visual brain

    We will discuss the things we are good ànd less good at when it comes to visual information processing

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    The three visualization lenses

    These three lenses give you a framework for looking at and assessing the quality of a visualization. That, in turn, will help you create effective, clear, and attractive visualizations yourself!

  • Workshop slides

    Slides can be downloaded below:

  • What chart type to use?

    There is lots of help for that! Pick one that suits your needs best from the options below:

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    How to create this specific chart with software/tool X or Y?

    So you know what you want to make and also which tool you want to do that with.... but you're not really sure how. Chartmaker may be of help: it links you to blogs, formats etc, per tool and type of visualisation (but note that some of the links got a bit outdated...)

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    How to create relatively non-standard visualisations, quickly?

    In Rawgraphs you can upload your data, and visualize it in ways that are just outside of the 'standard Excel' realm, like Sankey plots, streamgraphs, etc.

     

    The output of Rawgraphs is not 'polished' or 'final', but you can export it as a SVG file, which you can then finalise in Adobe Illustrator or using the online drawing/design platform Figma (see next recommendation).

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    How do I make the last 'tweaks' to my visualisation?

    Figma is a free, online design program for people who do not have Adobe Illustrator, but still want to polish their 'rough' SVG files (for example Rawgraphs' output, see previous point). You do need to register to create an account.

  • Topics which came up in the workshop

     

    After the workshop, Onno asked a good question on 'reproducibility of visuals': building a visualisation pipeline, through which you can then send all your data to create visuals that are reproducible. However, expecially in R, it can be very difficult to do the final tweaking of a plot in the code directly (instead of post-processing in Adobe Illustrator or Figma.) I personally really appreciate the work and efforts of Cedric Scherer in doing just that: coding everything in R directly. He really pushes the boundary of that in my opinion: https://www.cedricscherer.com And the ggplot tutorial of Cedric Scherer is a great online course: https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/

  • Extra information, references, tips, etc.

    Background information and recommendations

    Inspiration - visualizing a single data set in many different ways

    Inspiration - people who turn data into art

    Inspiration - people who work on the academic side of datavis

    Great Design is not one single big insight. It is rather a million little steps in (sort of) the right direction...

    Book recommendations

    • Edward Tufte - The Visual Display of Quantitative Information - The 'founding' book of data visualization. Must read for scientists. 
    • Cole Nusbaumer Knafflic - Storytelling with data - Data vis from a BI perspective: how to effectively communicate with impact using stand-alone data visuals?
    • Alberto Cairo - The Functional Art - Data vis from an infographics & journalism perspective: how to capture complex information in understandable visuals for broader audiences.

    Scientific articles on data vis

    Conferences and talks

    • C'est-la-viz - A new datavis conference, in Utrecht, organised by Graphic Hunters - https://cest-la-viz.nl
    • Outlier - Relatively new online and live conference, organised by the Data Visualization Society - https://outlierconf.com
    • OpenVis - Unfortunately this one has stopped after a few editions. Very much worth to check out presentations on their website or YouTube.
    • Infographic Congres - Live conference in The Netherlands, a bit more focussed on journalism/infographics - https://www.infographicscongres.eu
    • SciVizNYC - Focussed on scientific visualisations - YouTube talks can still be found, for example: Mesa Schumacher's talk on a framework for structuring infographics/illustrative content: https://youtu.be/KIBnrU3f0ZA?si=wYO4Z2umLZA7FtUI&t=1220
    • Steve Franconeri @ OpenVis 2018 - Very interesting presentation on visual cognition: how does our brain process visual information, what is it good/bad at?

    Alternatives to grouped bar charts

    Podcasts

    Color help!

    Read about it:

     

    Create palettes yourself:

    • Viz palette - A great tool to create and edit color palettes that you then immediately see used in various types of charts - https://projects.susielu.com/viz-palette
    • Color picker - A nice tool to create three different types of color palettes: visually equidistant palettes, divergent palettes, and single hue palettes - https://www.learnui.design/tools/data-color-picker.html
    • Cooler.co - A very good and accessible tool to explore and create color palettes. You can also derive color palettes from images with this tool, and check for grayscale readability, colorblindness, etc. https://coolors.co

     

    Colorblindness and color contrast checkers:

    A bit more technical options:

    • Chroma.js Color Palette Helper - Creating a color palette that is lightness corrected overall - https://gka.github.io/palettes/
    • Colorpicker tool - This tool is a great way to get to know colorspace and play with different options (I recommend playing around with all the buttons and sliders on this page, and see what happens) The beauty of this tool is that the resulting color palettes are lightness corrected (meaning that when converted to grayscale, you still have a 'correct' ordering based on the lightness) - http://tristen.ca/hcl-picker/
    • IwantHue - Creating a good looking color palettes mostly for categorical, distinct data sets - http://medialab.github.io/iwanthue/

  • Survey

    Do let me know what was most valuable to you

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    About me

    Sara Maria Sprinkhuizen

    I am a physicist who fell in love with MRI scanners, which launched my path into academia and health care. After finalizing my PhD in MRI physics (Utrecht University, the Netherlands) I moved to Boston for a post-doc (Harvard Medical School, USA). Over the past years I have also actively pursued my personal interest in data visualizations, often amazed at all the knowledge on that topic that is not yet found within academia. With my health care, data analytics, and data visualization experience, I now provide human centered and tailor-made data support for health care organizations and scientists.

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  • Contact

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