Datawrapper Blog

(Disclaimer: I work at Datawrapper) For most projects, our free plan might suffice: https://blog.datawrapper.de/create-data-visualization-for-free/ It’s fully usable for commercial use as well. In terms of in-between projects, if comfortable with a bit of programming I would probably look at Vega Lite [0] or Observable Plot [1]. For GUI tools, there’s also the excellent RAW Graphs [2] to consider! [0] https://vega.github.io/vega-lite/ [1] https://observablehq.com/@observablehq/plot [2] https://rawgraphs.io/

For Germany this one is the fastest and most reliable: - https://interaktiv.morgenpost.de/corona-virus-karte-infektionen-deutsc… Second best is: - https://www.zeit.de/wissen/gesundheit/2020-03/coronavirus-deutschland-… Also good are these charts: - https://blog.datawrapper.de/coronaviruscharts/ But the ultimate list can be found at - https://travel.stackexchange.com/questions/154808/covid-19-virus-day-b…

> you have vivid memories of how quickly the AUD, or USD or any fiat currency really has been so immensely rapidly inflated in the last 20 years; hell just in the last 3 years I actually think the last 20 years have had remarkably little inflation in the grand scheme of things, compared to the last 100 years, at least in the UK. I first got interested in this just under 20 years ago when my mum said that my grandma's first house cost exactly £100, and at the time the property market was towards the end of a massive boom (and then was stagnant for about the next 10 years). At the time, her £100 house would have been worth somewhere around £100k, which got me thinking to how crazy that it could have increased a thousand fold. Looking at UK historical inflation rates, it was interesting that from 1900 until the mid 1980s, inflation was pretty constant at around 7% per year, which has the interesting side effect that when compounded, prices approximately double every decade. Obviously, my data points are skewed towards the UK, but I suspect the US and Europe at least experienced similar effects, because of the ease of international trade, so any imbalances would be rapidly exploited by the market until they equalised. However, one data point I have from the US, which is a bit fuzzy in my recollection so it might be slightly off, was from reading Grapes of Wrath set in the 1930s Great Depression. IIRC the daily salary for a long day picking cotton was around 20 cents, so maybe…

You see even if you had power and wanted to do something about ecological exhaustion (not just warming, but resource and land overuse, biodiversity loss, pollution, etc), you're going to run into hordes of people who build their happiness in life on consumption and/or doing business selling that consumption. But effective countermeasures must reduce the absolute amount of consumption, whereas the only acceptable policies in practice are those that shift consumption elsewhere, hence no progress on this topic (absolute demand rises). Curiously related and posted here today https://blog.datawrapper.de/fair-share-resources/ (1) And don't even try policing behavior w.r.t. that goal. For that our species is just too broken in the head. Really the best shot is a complex system of supply side regulation, education, technological innovation with a dash of psychological and cultural influencing and hope. Really the better question to ask for oneself by now might be how to live with the knowledge that we're running off the cliff (with careful optimism "it might not become as bad" in the back of the mind). Humanity learns by catastrophe... sometimes, a bit. Or take solace in the fact that modern civilization is just a blip on geological timelines and it will all recover after we're gone (until the next ape rises). (1) which cites https://www.thelancet.com/journals/lanplh/article/PIIS2542-5196(22)000…

The same author has written three other articles specifically on colorblindness and data visualization: https://blog.datawrapper.de/colorblindness-part1/ https://blog.datawrapper.de/colorblindness-part2/ https://blog.datawrapper.de/colorblindness-part3/

One thing I didn't see mentioned, which is a pet peeve of mine, was how it's not ideal to use just hue or just lightness when you're supposed to read values from a dataset. Case in point: https://blog.datawrapper.de/wp-content/uploads/2021/01/full-200805_goo… Which slice of the pie represents Japan? Probably the leftmost, but they happen to be few and ordered, so it's easy to infer that. Now imagine this is a map, not a pie chart and you don't know where Japan is. Pretty hard to tell those very similar blues apart. One way out of this is to use two lightness levels per hue and sufficiently distant colours: https://www.researchgate.net/profile/Md-Sariful-Islam-6/publication/33…

> Designed to Last Well, I hope Jeff didn't put too much time into finding the color pallet. I don't want to be negative, but this background color just makes we want to run away/close the page. Maybe these links which I collected over the years can be helpful: - https://www.colorcombos.com - https://paletton.com - https://blog.datawrapper.de/beautifulcolors/ - https://design.lyft.com/re-approaching-color-9e604ba22c88 (ColorBox)

8 different font sizes, no logical line heights, some elements are spaced tightly (ordered list, share button) and some far apart (radio buttons). And the primary buttons in image 1 and 3 are teal, but in 2 we introduce a yellow button and in 3 there's a red one. Even though this is about infographics, there is a lot of useful information here: https://blog.datawrapper.de/beautifulcolors/

Maybe a different color scheme, keeping the option for color and contrast you currently have. I actually think this could be valuable for you: https://blog.datawrapper.de/beautifulcolors/

She gives very good advice for us non-designers though use color templates. They are available as book or webpage and by trying out just a handful that seem to fit your needs you can quickly get to a reasonably good design. It works for me at least. https://blog.datawrapper.de/colorguide/

I wrote an essay on methods of comparison in 2018 that’s tangentially related (though more focused on color than position). It was inspired by an article by Lisa Charlotte Rost on log scales. https://observablehq.com/@mbostock/methods-of-comparison-compared https://blog.datawrapper.de/weeklychart-logscale/ With the COVID-19 pandemic, the topic of log scales has even made its way into The New York Times as people try to wrap their heads around exponential growth. https://www.nytimes.com/2020/03/20/health/coronavirus-data-logarithm-c…

I can't help but notice that many of the "non ideal" examples are pretty much identical to the colors used by companies like Google: https://blog.datawrapper.de/img/full-200805_goodcolors28.png. Apple is another company that's not afraid to use extremely bold, saturated colors in their UI designs: https://developer.apple.com/design/human-interface-guidelines/ios/imag… - in fact the colors chosen in that UI goes against a large amount of the advice found on this article, that green to lime gradient is pretty much 100% saturated for example, with a background that is absolute #000000 black. The main theme of this article is to try to use complementary colors that aren't overly saturated or "pure", which is decent enough advice most of the time, but I feel that in an attempt to find color schemes that are more "professional looking", the author has used colors that come across as overly staid for most applications - navy blues and olive greens that are more associated with serious banking institutions then anything else. Which I guess is fine for infographics that need to display a level of trust and seriousness, but maybe not so much for a mobile app or social network that wishes to put on a more playful, friendlier face.