This year, I’ve been focusing on making the nuances of the coronavirus pandemic more understandable for Guardian readers. Since joining the Guardian, this has meant digging through the noise to find data-driven stories that reveal new points about the pandemic.
Aside from our live-tracking pages, I have also uncovered stories on how 2020 summer relaxations were leading 10 countries to face rising cases, how densely-packed and deprived BAME communities were hardest-hit, how modelling showed that the ‘real’ English infection rate could be as high as 20%, and how the winter wave has driven record demand on the NHS.
Collectively these data-driven news stories have received hundreds of thousands of views. While our trackers continually serve to cover the bases of the pandemic’s progression, these pieces of data journalism aim to take the story further: they aim to reveal how coronavirus is impacting specific communities, or how government policy has led to coronavirus spreading in a certain way. These require a more bespoke analysis on a specific issue compared to the trackers, but are just as important to cover.
Such pieces use data to unveil problems or injustices that need addressing. They raise awareness of these issues, and therefore try and hold power to account. The Guardian’s reader community responded well to these stories, with the pieces receiving positive comments, with many positive comments, shares and engagement times of over a minute.
The data analysis components of these pieces were mostly conducted in R (mostly dplyr, tidyr) and Google Sheets. The static charts were built in R (ggplot) and final stylistic edits and annotations made in Adobe Illustrator. The interactive charts – in both the international piece on relaxed countries seeing coronavirus increases, and the England modelled ‘real’ infection rates – were built in d3.
Description of portfolio:
Finding data on the pandemic has been challenging, with different countries and authorities recording different metrics in slightly different ways. Authorities have also made certain metrics hard to access. This often made data analysis a harder job than usual, meaning that even more care had to be taken when at the checking and verification stage of a project. This was especially true for the “data shows 10 countries risking coronavirus second wave as lockdown relaxed” story.
Also, each of these pieces were collaborative projects, involving work with both other journalists on the Visuals or Data teams, or wider afield with reporters on the news desk. As so many people have come to learn, working from home presents extra challenges when working on collaborative projects. I joined the Guardian during this period of remote working, and so have never actually met some of the people I’ve worked with on these projects in person. Having to arrange calls instead of simply looking over a colleague’s shoulder, for example, made some of these stories more challenging. However, this challenge has been reduced from the start due to the fact that my colleagues have been so understanding, patient and helpful. The challenge has also been reducing as I become more adapted to remote working, and the process becomes more embedded and efficient in the newsroom.
I think the coronavirus pandemic and the data-led reporting around it has shown how data and visual journalism needs to be at the forefront of the newsroom. Every day, most national and international publications are reporting on newly-published statistics that not only help people understand how the pandemic is progressing, but also help authorities in combating the disease. These numbers are not easy to understand without explanation and context, which is where our job comes in.
Visualisation can help report these numbers in a balanced but engaging way. We can create time series charts to show the trajectory of death rates, or build a map that shows the geographic context of a particular area or countries’ latest case numbers. Without this data journalism, reporting on the the coronavirus pandemic – the most important story of the last year – would be inferior, lacking context and potentially misleading as communities either placed too much or too little emphasis on individual numbers.
The work of data analysis and data visualisation has never been more important.