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At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes - and other people may be able to learn from them, too. After a deep dive into our archive, I found several instructive examples. I grouped our crimes against data visualisation into three categories: charts that are (1) misleading, (2) confusing and (3) failing to make a point. For each, I suggest an improved version that requires a similar amount of space - an important consideration when drawing charts to be published in print.

Nice little interactive piece from the NYTimes - they've charted sets of data from the Bush presidency, can you accurately extend their line charts to show the change over the Obama years?

The histogram is one of my favorite basic chart types, because it lets you quickly see the shape and distribution of a dataset. However, a lot of people don't know what a histogram shows or how the chart works.

Is income inequality growing (in America)? This article discusses the issue, and uses a chart from the Financial Times (and Pew research data) to demonstrate the change since 1971. Great visualisation and great discussion of what it shows.

For the 11 million Australians with private hospital cover, premium rises are nothing new. The 3.95% average increase on April 1, 2018 will be the seventeenth consecutive year in which insurance premiums have been hiked up. Health insurance premiums have increased by an average of 5.35% per year since 2000, which is significantly more than wage growth, meaning that households are spending a larger share of their income on health care.

In our teens and early twenties, we're still figuring out what we want to be when we grow up. As we get older, we start to settle into a career. In between, we switch jobs in the search. Based on data from the Current Population Survey, this is when people make the switches and the jobs they switch to.The chart above shows the rate by age, relative to the total number of people who switched to each job. So you see a lot of switching in the early years, and then things seem to settle down at older ages. If someone takes a new job when they're older, it tends towards management or jobs that require more education.

examples of data journalism in audio / podcast form - including: Right To Remain Silent is one particularly good example, because it's about bad data: specifically. police who manipulated official statistics. You might also listen to Choosing Wrong, which includes a section about polling. Another favourite of mine is an audio story by The Economist about the prostitution industry, based on data scraped from sex trade websites: More bang for your buck (there are even worse puns in the charts). David Rhodes, a BBC data journalist, has a range of stories on his Audioboom account, including pieces on Radio 4, Radio 5 Live, and a piece discussing "Did Greece really not pay 89.5% of their taxes in 2010" from the excellent factchecking radio programme, More or Less.

Machines are besting humans in more and more tasks; thanks to technology, fewer Americans make more stuff in less time. But today economists debate not whether machines are changing the workplace and making us more efficient - they certainly are - but whether the result is a net loss of jobs. The figures above may look dire. But compare the number of manufacturing jobs and total jobs in the chart below. Since 1990, the total non-farm workforce has grown 33 percent, more than accounting for the manufacturing jobs lost.

By pulling this chart apart and then helping you put it back together, this story aims to help you understand: how quickly coronavirus is spreading in different countries; where Australia fits into the global picture; what we can learn from countries that appear to have curbed the rise of COVID-19; and what you can do to help keep Australians safe. But first, one concept that's vitally important to understanding a pandemic is exponential growth. This is a pattern viruses tend to initially follow, due to the way they're spread. The result is that what might seem like a small difference in the rate of growth can actually have enormous impacts on how many people are infected overall.

Wrapping your head around the scale of a global pandemic is not easy, and the volume of stats and data can be bewildering. What, for instance, are we to make of the fact Australia recorded just 109 new cases in its daily count for April 6? Given this figure peaked at around 400 new cases per day, does this mean the rate of infection is now tapering off? And what, apart from sadness, are we to make of more gruesome statistics, such as the 969 COVID-19 deaths reported in a single day in Italy on March 27? To help interpret and understand the mountains of COVID-19 data, we'll look at five commonly used methods, and explain the pros and cons of each.