Category: Data visualization

How I Created a Data Visualization With Zero Coding Skills, Thanks to ChatGPT

Mary Pat Campbell

Link: https://nightingaledvs.com/data-visualization-using-chatgpt-to-code/

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Data visualization is cool but at the same time it’s bit daunting that I need to know lots of tech stacks to actually implement it.

I totally agree that even when I was studying data visualization, I spent a pretty substantial amount of time learning how to code, handle web hosting, work with Python, SQL, and more, all while absorbing knowledge on information visualization.

Thankfully, we no longer need to deep dive into technical gatekeepers in this field. This doesn’t mean that technical knowledge is not valuable, but rather that we no longer need to be intimidated by technology because AI can spoon-feed us knowledge and do the heavy lifting for us. Are you excited? Let’s get started!

I’m going to build the data visualization that one of my students posted on weekly write-up homework.

Author(s): Soonk Paik

Publication Date: 4 April 2023

Publication Site: Nightingale

The Banking Sector Turmoil in Charts

Mary Pat Campbell

Link: https://www.wsj.com/articles/the-banking-sector-turmoil-in-charts-52bb6095?mod=e2twg

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It has been a wild ride for banks. Silicon Valley Bank, which catered to venture capitalists and startups, collapsed March 10 after a run on deposits that was preceded by a plunging share price and a money-losing bond sale as the bank tried to raise capital. Two days later, Signature Bank SBNY -22.87%decrease; red down pointing triangle was closed by federal regulators following a run. Then, First Republic Bank FRC 29.47%increase; green up pointing triangle, at risk of a run as its share price plummeted, was flooded with cash in an extraordinary action by some of the largest U.S. banks—but its shares resumed their plunge a day later. 

Here is how some banks ended up in the market’s crosshairs.

Author(s): Nate Rattner, Alana Pipe

Publication Date: 18 Mar 2023

Publication Site: WSJ

Data Vis Dispatch, January 31

Mary Pat Campbell

Link:https://blog.datawrapper.de/data-vis-dispatch-january-31-2023/

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Welcome back to the 79th edition of Data Vis Dispatch! Every week, we’ll be publishing a collection of the best small and large data visualizations we find, especially from news organizations — to celebrate data journalism, data visualization, simple charts, elaborate maps, and their creators.

Recurring topics this week include wintery weather, social inequality, and inflation.

Author(s):Veronika Halamková

Publication Date: 31 JAN 2023

Publication Site: Datawrapper

All of US: a dot density map of the United States

Mary Pat Campbell

Link: https://all-of-us.benschmidt.org/

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One point per person in the US for the 2010 and 2020 censuses, fully zoomable and interactive using WebGL and [Deepscatter](https://github.com/CreatingData/deepscatter). Since this uses WebGL individual point rendering and quadtiled data, it can be far more responsive than raster-based maps you may have seen in 2010. Plus, if you zoom all the way in in some views it has little person glyphs!

Author(s): Ben Schmidt

Publication Date: accessed 2 Dec 2022

Publication Site: Ben Schmidt’s personal site

The most common restaurant cuisine in every state, and a chain-restaurant mystery

Mary Pat Campbell

Link: https://www.washingtonpost.com/business/2022/09/29/chain-restaurant-capitals/

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The places that drive the most tend to have the same high share of chain restaurants regardless of whether they voted for Trump or Biden. As car commuting decreases, chain restaurants decrease at roughly the same rate, no matter which candidate most residents supported.

If the link between cars and chains transcends partisanship, why does it look like Trump counties have more chain restaurants? It’s at least in part because he won more of the places with the most car commuters!

About 83 percent of workers commute by car nationally, but only 80 percent of folks in Biden counties do so, compared with 90 percent of workers in Trump counties. The share of car commuters ranges from 55 percent in the deep-blue New York City metro area to 96 percent around bright red Decatur, Ala.

Author(s): Andrew Van Dam

Publication Date: 1 Oct 2022

Publication Site: WaPo

Where Do People Pay the Most in Property Taxes?

Mary Pat Campbell

Link: https://taxfoundation.org/property-taxes-by-state-county-2022/

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Median property taxes paid vary widely across (and within) the 50 states. The lowest bills in the country are in six counties or county equivalents with median property taxes of less than $200 a year:

  • Northwest Arctic Borough and the Kusivlak Census Area (Alaska)*
  • Avoyelles, East Carroll, and Madison (Louisiana)
  • Choctaw (Alabama)

(*Significant parts of Alaska have no property taxes, though most of these areas have such small populations that they are excluded from federal surveys.)

The next-lowest median property tax of $201 is found in Allen Parish, near the middle of Louisiana, followed by $218 in McDowell County, West Virginia, in the southernmost part of the state.

The eight counties with the highest median property tax payments all have bills exceeding $10,000:

  • Bergen, Essex, and Union (New Jersey)
  • Nassau, New York, Rockland, and Westchester (New York)
  • Falls Church (Virginia)

All but Falls Church are near New York City, as is the next highest, Passaic County, New Jersey ($9,999). 

Author(s): Janelle Fritts

Publication Date: 13 Sept 2022

Publication Site: Tax Foundation

What to consider when using text in data visualizations

Mary Pat Campbell

Link: https://blog.datawrapper.de/text-in-data-visualizations/

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Text is maybe the most underrated element in any data visualization. There’s a lot of text in any chart or map — titles, descriptions, notes, sources, bylines, logos, annotations, labels, color keys, tooltips, axis labels — but often, it’s an afterthought in the design process. This article explains how to use text to make your visualizations easier to read and nicer to look at.

Show information where readers need it
01 Label directly
02 Repeat the units your data is measured in
03 Remind people what they’re looking at in tooltips
04 Move the axis ticks where they’re needed
05 Emphasize and explain with annotations

Design for readability
06 Use a font that’s easy to read
07 Lead the eye with font sizes, styles, and colors
08 Limit the number of font sizes in your visualization
09 Don’t center-align your text
10 Don’t make your readers turn their heads
11 Use a text outline

Phrase for readability
12 Use straightforward phrasings
13 Be conversational first and precise later
14 Choose a suitable number format

Author(s): Lisa Charlotte Muth

Publication Date: 28 Sept 2022

Publication Site: Datawrapper

Reasons to Visualise the Same Data, Differently

Mary Pat Campbell

Link: https://nightingaledvs.com/reasons-to-visualise-the-same-data-differently/

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To quote Andy Kirk, “we can look at data, but we cannot really see it. To see data, we need to represent it in a different, visual form.” So, in an attempt to make data more accessible, you may create more visual representations – dots, lines, shapes, and colours. These building blocks combine to create all sorts of charts and pictures helping readers understand numbers. 

Although the purpose of visualising data is clear (and universal), the reasons can be different. The reason you visualise data, will help you determine the appropriate visual.

Author(s): Kat Greenbrook

Publication Date: 8 Sept 2022

Publication Site: Nightingale

Dataviz Horror Story: How I Crashed the Top Exec’s Email

Mary Pat Campbell

Link: https://nightingaledvs.com/dataviz-horror-story-how-i-crashed-the-top-execs-email/

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In my case, the graphs I made looked just fine—it’s just that I didn’t understand how copy/pasting graphs between Excel and Word worked (at the time). This was in the mid-2000s, when memory wasn’t quite so plentiful, so many corporate email accounts had memory quotas. If you hit that quota, you would be locked out of your email account. You had to call IT and actually talk to a person! 

I was a lowly entry-level person at a financial services company and had done some Monte Carlo modeling involving 1,000,000 scenarios. We were developing a new mutual fund project, based on changing allocations over time as people moved towards retirement, and the company wanted me to model outcomes for different allocation trajectories.  After a “full” model run of one million scenarios, I made diagnostic graphs showing the distribution of key metrics (such as the annual accumulation of the fund, how many times the fund decreased while the owner was in retirement, and whether – and when – the money in the fund ran out)  so that we could analyze different potential fund strategies. The graphs themselves were fairly simple. 

Author(s): Mary Pat Campbell

Publication Date: 31 Aug 2022

Publication Site: Nightingale

Data visualization lessons: Jitter charts, screwups, and visionaries

Mary Pat Campbell

Link: https://marypatcampbell.substack.com/p/data-visualization-lessons-jitter

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Jitter charts are my new favorite tool for displaying how distributions change over time.

I used them to great effect in my recent post One Bad Year? Comparing the Long-Term Public Pension Fund Returns Against Assumptions.

I’m often looking at distributions, and wanting to communicate something about how those distributions change over time, or how distributions compare. Often, I have to simply pick out key percentiles in those distributions, or key aspects, such as mean and standard deviation.

But why not graph all the points in one’s sample directly, if one has them?

That’s where jitter charts can help.

Author(s): Mary Pat Campbell

Publication Date: 16 Sep 2022

Publication Site: STUMP at substack

Excess mortality in England

Mary Pat Campbell

Link: https://app.powerbi.com/view?r=eyJrIjoiYmUwNmFhMjYtNGZhYS00NDk2LWFlMTAtOTg0OGNhNmFiNGM0IiwidCI6ImVlNGUxNDk5LTRhMzUtNGIyZS1hZDQ3LTVmM2NmOWRlODY2NiIsImMiOjh9

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The numbers of expected deaths are estimated using statistical models and based on previous 5 years’ (2015 to 2019) mortality rates. Weekly monitoring of excess mortality from all causes throughout the COVID-19 pandemic provides an objective and comparable measure of the scale of the pandemic [reference 1]. Measuring excess mortality from all causes, instead of focusing solely on mortality from COVID-19, overcomes the issues of variation in testing and differential coding of cause of death between individuals and over time [reference 1].


In the weekly reports, estimates of excess deaths are presented by week of registration at national and subnational level, for subgroups of the population (age groups, sex, deprivation groups, ethnic groups) and by cause of death and place of death.

Author(s): Office for Health Improvement and Disparities

Publication Date: accessed 10 Aug 2022

Publication Site: Public PowerBI dashboard

What can go wrong? Exploring racial equity dataviz and deficit thinking, with Pieta Blakely.

Mary Pat Campbell

Link: https://3iap.com/what-can-go-wrong-racial-equity-data-visualization-deficit-thinking-VV8acXLQQnWvvg4NLP9LTA/

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For anti-racist dataviz, our most effective tool is context. The way that data is framed can make a very real impact on how it’s interpreted. For example, this case study from the New York Times shows two different framings of the same economic data and how, depending on where the author starts the X-Axis, it can tell 2 very different — but both accurate — stories about the subject.

As Pieta previously highlighted, dataviz in spaces that address race / ethnicity are sensitive to “deficit framing.” That is, when it’s presented in a way that over-emphasizes differences between groups (while hiding the diversity of outcomes within groups), it promotes deficit thinking (see below) and can reinforce stereotypes about the (often minoritized) groups in focus.

In a follow up study, Eli and Cindy Xiong (of UMass’ HCI-VIS Lab) confirmed Pieta’s arguments, showing that even “neutral” data visualizations of outcome disparities can lead to deficit thinking (and therefore stereotyping) and that the way visualizations are designed can significantly impact these harmful tendencies.

Author(s): Eli Holder, Pieta Blakely

Publication Date: 2 Aug 2022

Publication Site: 3iap