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.
I highlighted a few of the cause-of-death trends. In particular, COVID (which, obviously, is biased more towards the old), and external causes of death: homicide, suicide, and accidents (which includes drug overdoses and motor vehicle accidents).
There are basically too many things going on in this graph, so there aren’t a lot of good choices for either me or the SOA. What I did was to pick four of the data series to highlight with data labels, as noted above (and I also slapped one data label on dementia for the oldest age group, just because). I am in the middle of a series going through how that external causes of death changed in 2020 — in particular, accidents and homicides went up, and really affected mortality for adults under age 45, plus male teens.
Yeah, check out heart disease and cancer (bottom of the graph). Ain’t old age great?
Some of Lego’s basic colors, like black and white, seem to maintain their representation across the years. However, other classics like red, blue, and yellow decreased in the mid-2000s, opening up space for a wider variety of colors and shades. The last few decades came with an explosion of the number of colors, and also the creative possibilities.
Today’s graphic from Paul Schmelzing, visiting scholar at the Bank of England (BOE), shows how global real interest rates have experienced an average annual decline of -0.0196% (-1.96 basis points) throughout the past eight centuries.
The Evidence on Falling Rates
Collecting data from across 78% of total advanced economy GDP over the time frame, Schmelzing shows that real rates* have witnessed a negative historical slope spanning back to the 1300s.
Displayed across the graph is a series of personal nominal loans made to sovereign establishments, along with their nominal loan rates. Some from the 14th century, for example, had nominal rates of 35%. By contrast, key nominal loan rates had fallen to 6% by the mid 1800s.
Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.
Author(s): Steven L. Franconeri, Lace M. Padilla, Priti Shah, Jeffrey M. Zacks, Jessica Hullman
This is the website for the book “Fundamentals of Data Visualization,” published by O’Reilly Media, Inc. The website contains the complete author manuscript before final copy-editing and other quality control. If you would like to order an official hardcopy or ebook, you can do so at various resellers, including Amazon,Barnes and Noble,Google Play, or Powells.
The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.
The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz. If you notice typos or other issues, feel free to open an issue on GitHub or submit a pull request. If you do the latter, in your commit message, please add the sentence “I assign the copyright of this contribution to Claus O. Wilke,” so that I can maintain the option of publishing this book in other forms.
Two weeks after the Omicron variant was identified, hospitals are bracing for a covid-19 tsunami. In South Africa, where it has displaced Delta, cases are rising faster than in earlier waves. Each person with Omicron may infect 3-3.5 others. Delta’s most recent rate in the country was 0.8.
Where it stands: The U.S. is now averaging roughly 120,000 new COVID cases per day, a 26% increase over the past two weeks.
Average cases briefly dipped below 100,000 as the summer’s Delta wave receded, but the virus has rebounded quickly. New infections were climbing even before Thanksgiving, and holiday travel likely is accelerating the virus’ spread even further.
Deathsare also on the rise, after tapering off in the fall.
The virus is now killing about 1,300 Americans per day, on average. That’s a 14% increase over the past two weeks.
At this rate, the U.S. will pass 800,000 total deaths — roughly equivalent to the population of the Charleston, South Carolina, metro area — before Christmas.
Well, as far as I can tell, the only advantage of box plots is that they show quartile ranges. The obvious next question, of course, is how often it’s necessary to show quartile ranges in order to say what you need to say about the data. In my experience, it’s not nearly as often as a lot of chart creators seem to think. Most of the time, you’re pointing out that distributions are higher or lower than one another, more concentrated or more dispersed than one another, have outliers, etc. None of these types of insights require showing quartile ranges and all can be communicated using simpler chart types. Some types of insights might require showing medians, but those can be easily added to simpler chart types:
Women in finance in the U.K. still make significantly less than men. While the gender pay gap at financial firms in the country narrowed slightly last year, overall the industry continues to have the biggest disparity.
Men working in finance and insurance made 25% more than women last year, down from 28% in 2019, a Bloomberg News analysis of government data shows. The pay gap is especially wide in investment banking, where some of the highest-paid employees work.
It is the fourth straight year that finance has led the industry rankings, showing that executives are finding it difficult to shrink the gap. Mining and quarrying had the second-biggest pay gap at 23% as the commodity boom boosted the income of workers, who are largely male.