background preloader

What Makes A Good Data Visualization?

What Makes A Good Data Visualization?
Hi there. I’m David McCandless, creator of this site and author of two infographic mega-tomes, Information is Beautiful (2009) and Knowledge is Beautiful (2014). I’ve created a lot of data and information visualizations. Around 540 over six years. (An eye-bleeding, marriage-crumpling average of 1.7 per week). There are 196 in my new book, Knowledge is Beautiful (out now). This graphic visualises the four elements I think are necessary for a successful “good” visualization. i.e. one that works. All four elements in his graphic seem essential. See how, interestingly, if you combine information & function & visual form without story, you get “boring”. Similarly, if you combine visuals, information & story without considering functionality and your goal, you get something useless. These elements form the backbone of my process and also what I teach in my dataviz workshops. I’m not really a follow-this-system type of person. Thanks, David

Related:  visual thinkingData VisualisationTutorialsVisualization

Snake oil? Scientific evidence for health supplements Updated September 2015 with a revitalising boost of fresh data. New entries include melatonin, proving travellers were right about its effect on sleep; and good evidence for Vitamin D for flu, bones and long life. Evidence for valerian as a cure for anxiety has dropped, as has any likelihood that cranberry juice has impact on urinary infections. Thanks to visitor suggestions we’ve added entries for supplements that may in some cases be harmful, including Vitamin A, which has been linked to birth defects.

One Dataset, Visualized 25 Ways “Let the data speak.” It’s a common saying for chart design. The premise — strip out the bits that don’t help patterns in your data emerge — is fine, but people often misinterpret the mantra to mean that they should make a stripped down chart and let the data take it from there. You have to guide the conversation though. You must help the data focus and get to the point.

The MicrobeScope - Infectious Diseases in Context For your reference and to satisfy a curiosity of mine around the contagiousness of microbes and pathogens, especially Ebola. To give a universal metric for infectiousness contagiousness, this graphic uses the average ‘basic reproduction number‘ (also rate or ratio), the number of additional cases generated by a single case of the disease. It’s a statistical measure of how likely an infectious disease might spread through a population – if nothing is done to contain the outbreak. These numbers are scattered around in the literature so quite a lot of mining was necessary to surface them. Correction 20th Oct 2014 – Relabelled the horizontal axis “contagiousness” (how spreadable a disease is through a population) rather than “infectiousness” (how communicable a disease is person-to-person).

IBM Design Language Visualizing data is central to this key moment in time, when the borders between big and impersonal, and small and intimate data will blur as we’ve never seen before. The greater the quantity and kinds of data collected, the more we need to experiment with how to make it unique. Instead of starting from standards, begin from a blank page and experiment with a custom visualization. Even if you come back to the basics, small details from your process and play can enhance basic charts to reveal more about topics users are interested in.

Introducing Vega-Lite Provide sensible defaults, but allow customization. Vega-Lite’s compiler automatically chooses default properties of a visualization based on a set of carefully designed rules. However, one can specify additional properties to customize the visualization. For example, the stacked bar chart on the left has a custom color palette. Vega-Lite uses a concise syntax, enabling rapid creation of visualizations without unduly restricting subsequent customization. A Snapshot of Current Trends in Visualization Guest Editors’ Introduction • Theresa-Marie Rhyne and Min Chen • February 2017 Read the Guest Editors’ Introduction inSpanish | Chinese Translations by Osvaldo Perez and Tiejun Huang Listen to the Guest Editors' Introduction English (Steve Woods): Spanish (Martin Omana):

What I Use to Visualize Data “What tool should I learn? What’s the best?” I hesitate to answer, because I use what works best for me, which isn’t necessarily the best for someone else or the “best” overall. Data visualization: A view of every Points of View column : Methagora We’ve organized all the Points of View columns on data visualization published in Nature Methods and provide this as a guide to accessing this trove of practical advice on visualizing scientific data. As of July 30, 2013 Nature Methods has published 35 Points of View columns written by Bang Wong, Martin Krzywinski and their co-authors: Nils Gehlenborg, Cydney Nielsen, Noam Shoresh, Rikke Schmidt Kjærgaard, Erica Savig and Alberto Cairo. As we prepare to launch a new column in our September issue we felt this would be a good time to collect and organize links to all the Points of View articles together in one place to make it easier to navigate this wonderful resource that the authors have provided us.

Visualization Education Mailbag It's around that time of year when more people than usual ask for advice about degrees in statistics, career paths in visualization, and how to get started with something that looks awesome. The high of graduation from high school, undergrad, and grad school has settled, and it's time to think about the future. Maybe summer brought more idle time at work to imagine what else you could do every day. I know the feeling. I'll try to answer the more common questions. However, keep in mind that I'm nowhere near the best person to ask about these things. A Complete Tutorial to learn Data Science in R from Scratch Introduction R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s.

How to design a better table In 2009 I did a survey of the use of data displays in a number of leading journals and found that, in all of the sciences that I surveyed, the dominant form of data display was the table.1 This is despite the century-old warning of the brothers Farquhar that: “Getting information from a table is like extracting sunbeams from a cucumber.”2 Figure 1 shows the results for the New England Journal of Medicine, which are essentially identical to those from the Journal of the American Medical Association and all of the other journals I looked at. Figure 1.

Machine Learning for Dummies: Part 1 – Chatbot’s Life I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don’t like the maths either. Math is an abstract way of describing things. Virus trading cards This week I made a set of virus trading cards! Viruses are surprisingly symmetrical, and I love them because they remind me of a biological version of snowflakes. Each trading card shows you the structure of the viral capsid - the protein shell protecting the genetic material inside a virus. To make the 3D animations I used UCSF Chimera, a free molecular modeling program. When scientists discover a new protein structure they upload it to the worldwide Protein Data Bank. Each entry is assigned a unique ID number, which you can use to call up the structure in programs like Chimera or PyMol.