One of the best, cheapest ways to get better at visualizing and communicating your data is blogs. The first five blogs I’ve listed here publish more regularly than some of the others I include at the end of the list. There are a few tools-specific blogs listed at the end as well (of which there are so many, it’s hard to know where to start). These are just the blogs that I regularly try to keep up with; there are many others that you might find useful as well.
Depict Data Studio. Ann Emery’s stuff is great, especially if you want to learn how to create better, more effective reports.
Datawrapper. Lisa Charlotte Muth is the primary blogger here and the content is always amazing. The content is not specific to the Datawrapper tool.
Flowing Data. Nathan Yau sends out a daily example of a data visualization. I’m also a member of Flowing Data ($100 for the year), which gives me access to additional written content plus tutorials in D3, R, and Excel (often written by others).
Nightingale (from the Data Visualization Society). Collaborative effort from a variety of folks in the data visualization field. If you’re interested in blogging about your data visualization journey but don’t want the hassle of hosting your own site, you might want to reach out to the editors here.
Storytelling with Data. Cole Nussbaumer Knaflic’s site is one of the tops in the field and the recent addition of the SWD “challenges” and “community” (see below) are great community-based additions to the platform.
The last visualization I tried was to really embrace the idea of time in the data. Instead of a map or bar chart or something else, I placed the state abbreviations around two clock faces. I know it sounds weird, but take a look at the final version.
I think this is a fun visualization, and it communicates more precisely the exact average starting times than the previous graphs. The two clocks could be combined to one, but I worry it’s not quite as clear, so I tried using the different colors to differentiate the two hours.
One last option is to add sparklines. Sparklines are small line charts that are typically used in data-rich tables, often at the end of a row or column. The purpose of sparklines is not necessarily to help the reader find specific values but instead to show general patterns and trends. Here, the sparklines show all five years of data, which allows us to omit three columns of numbers, lightening and simplifying the table. This approach lets us show the full time series in the sparklines while just showing the two endpoints in the table cells.
In the previous iteration of this site, I reserved a special page dedicated to collecting Data Visualization Style Guides. I’m republishing that collection here as a blog post with the rekindled hope that readers will add their own or their organization’s guides to the collection.
The original idea was developed at the Responsible Data Forum in New York City on January 11, 2016. It’s simply a list of data visualization style guides provided in no particular order. The idea is to build a collection of guides that layout style, formatting, and perhaps some other basic recommendations. These should not necessarily be documents that describe “best practices” or “dos and don’ts”.
My hope is that this post will serve as a repository for guides from around the world that others can use to develop their own guides and best practices. The list was originally published in January 2016, started small, and has grown to more than 15 documents. But I’m sure there is more, so please send me your suggestions and links using the comment box below, via the Contact form, or on Twitter.
Yesterday I gave a virtual lecture on data visualization at GMU. Here I’m posting the slides I used for that talk and including my discussion notes for the portion of the talk where I discussed guidelines for data visualization.
At the beginning of the talk I spoke a bit about data visualization guidelines. I framed this part of my talk around Jon Schwabish’s five guidelines from his new book Better Data Visualizations see (on Amazon) and here for a blog summary.
I then went over some charts I’ve used recently in talks I’ve given and discussed how I used (or didn’t use) the guidelines in that chart.
Over the course of four years as President, Donald Trump made more than 30,000 false or misleading claims, according to the Washington Post Fact Checker. It should be no surprise, then, that some of these took the form of data visualizations. Here are the top ten most misleading charts, graphs, maps, and tables from the Trump Administration over the past four years.
We are not born knowing instinctively how to read a bar chart or line chart or pie chart. Most of us learn those basic chart types in grade school. But there is a vast array of graphic types available that can effectively communicate your work to your audience.
To get you started, here are five graphs that perhaps you’ve never used before but that you should consider. They either do a better job showing certain types of data or they are more engaging and interesting than basic chart types.
This is a list of data visualization catalogs and collections. It is inspired by the lists published by Yuri Engelhardt and others. It is provided for your use and reference. • 6 Ways to Visualize Graphs | https://www.twosixlabs.com/6-ways-visualizegraphs • 9 Ways to Visualize Proportions | http://flowingdata.com/2009/11/25/9-waysto-visualize-proportions-a-guide/ • 45 Ways to Communicate Two Quantities | https://www.scribblelive.com/blog/2012/07/27/45-ways-to-communicatetwo-quantities/