464: quantum of sollazzo

#464: quantum of sollazzo – 19 April 2022

The data newsletter by @puntofisso.

Hello, regular readers and welcome new ones :) This is Quantum of Sollazzo, the newsletter about all things data. I am Giuseppe Sollazzo, or @puntofisso. I’ve been sending this newsletter since 2012 to be a summary of all the articles with or about data that captured my attention over the previous week. The newsletter is and will always (well, for as long as I can keep going!) be free, but you’re welcome to become a friend via the links below.

This is a shorter Easter-break version. But I really wanted to share this week’s six-questions interview with a data guru. This week, I spoke with Colin Angus, the University of Sheffield researcher with a record-breaking number of viral chart-powered Twitter threads on the COVID-19 pandemic.

Last but not least, after a few years of not managing to catch up in person, Soph was again around London for her final week before moving to the States, and we grabbed a cup for some data journalism gossip. We’ll miss Soph this side of the pond, but you should all subscribe to her brilliant data journalism newsletter Fair Warning (yes, we have a sort of friendly rivalry!).

‘till next week,
Giuseppe @puntofisso

Six questions to...

Colin Angus

Colin is a Senior Research Fellow, at the School of Health and Related Research (ScHARR), The University of Sheffield.

What is your daily data work like and what tools do you use?

As a researcher my 'proper' work involves bringing together many datasets and building complex health economic models to estimate the impact of public health policies. But I also think descriptive epidemiology – just describing trends and patterns in health and related factors - is massively underrated. So alongside this research I have often done bits of analysis of various public datasets related to health. Since there is (sadly) little appetite from academic journals to publish this sort of descriptive analysis, I post graphs, code and commentary on Twitter. I do all this analysis in R, mostly because ggplot is amazing – powerful enough to create *almost* any graph you can imagine, but without being hellishly complicated.

Tell me about a data project that you're proud of...

I initially avoided COVID data, but then got lured in in April 2020 by one of my colleagues asking me a question about whether deprived areas had higher rates of COVID cases. Since then I've posted a *lot* of graphs and analysis (and R code) trying to make sense of what's going on, and in doing so learned a huge amount both technically about data processing and visualisation in R and also about what works well (and what doesn't) when trying to communicate data to the public. And hopefully along the way I've helped a few people to better understand the development of the pandemic.

...and a data project that someone else did and you're jealous of.

I'm not jealous in the sense that there's no way I could have made it, but the UK COVID dashboard really is an incredible thing. On a technical level, the amount of work required to pull such a huge amount of data from so many sources, process it and package it in a really accessible way so quickly absolutely boggles my mind. The fact that it also has an incredibly easy to use API so strange people like me can mess around with the data ourselves is just the icing on the cake. I *really* hope that we take some of that commitment to data accessibility into the future with other aspects of public health data.

If I say "dataset", you think of...

An opportunity to draw some new graphs and learn some new things.

Give someone new to data a tip or lesson you wish you'd learned earlier.

Share. Your. Code.

I made a decision one day to do this with everything I post online. It felt like a big leap of faith at the time, but I have no regrets at all.

It does feel a bit scary at first, particularly when you are just getting started with a new tool, topic or dataset, but I guarantee you that there will always be someone out there who is more confused than you and who will appreciate you sharing how you've done things. And remember, the ugly code that works is *much* better and more useful than the beautiful code that doesn't.

Data is or data are...

Is. Emphatically is. I usually love a bit of tedious grammatical pedantry, but 'Data are' just sounds wrong to me and I'm glad the world seems to be slowly coming round to my way of thinking.

Topical

This is the relationship between the percentage of foreign nationals…

“…and the score of Le Pen, Macron and Mélenchon in the 1rst round of the French presidential election last weekend in French parliamentary constituencies”.
Also look at this one which looks at the relationship with unemployment.

Video: Global Warming Broken Down by Latitude Zone (1880-2021)

“Global warming isn’t uniform around the planet. This visualization shows global temperature changes per latitude zone from 1880 to 2021, illustrating that the Arctic is warming much faster than other regions on Earth.” By NASA.
(h/t Soph Warnes)

How phone companies use our personal data

“In the past, some telephone companies have become known for their unscrupulous use of customers’ personal data. While things have improved in Europe, it is important to know what we are agreeing to when we sign a new contract.“
A look at T&Cs by Gianluca De Feo and Federico Caruso at the European Data Journalism Network.

Litterati

Litteraty is fundamentally a US-based company with a mission to put together a large, open (?), crowd-sourced dataset of litter.
(via Lee Provoost)

The Culture Wars Couldn’t Stop Ketanji Brown Jackson’s Confirmation

“…Supreme Court confirmations remain extremely contentious regardless of whether control of the court is at stake. And going forward, it’s unlikely that new Supreme Court nominees will make it onto the court unless the president’s party is also in charge of the Senate.“
Interestingly, most senators vote in strongly polarised ways.

Tools & Tutorials

Scrutinising airline efficiency by visualising public aviation data

“Visualising UK airline data to understand drivers of efficiency and highlight aviation’s environmental impact”. This is in the context of “ghost flights” flying during the pandemic in order to keep slots.

ML Notebooks

“A series of code examples for all sorts of machine learning tasks and applications.“

Diversity Data Builder

“This tool will help you create or update a CSV file following the Diversity Data Standard for Diversity Dashboards”, of which you can see an example about Yorkshire here.

Data thinking

Uncommon advice on becoming a data scientist in the public interest

“Civic data scientist Alex Engler shares his insights for those aspiring to work with data in the public sector.“

Dataviz, Data Analysis, & Interactive

We are pushing the planet over the edge

Simon Jockers of DataWrapper takes a look at “data from a depressing study about the overconsumption of natural resources.“

Visualizing air pollution across the world

“For this year’s World Health Day, we take a closer look at environmental influences on health”. This is from Flourish.studio.

AI

AI could help college baseball players reach the majors, but with little control over their biometric data

“Data providing insight into the performance of athletes’ bodies is being used to assess their value, but baseball players have few rights when it comes to who can access that information.“

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