Day 24 of #30DayChartChallenge: WHO data day. Sucks to be sick in Switzerland. Data: www.who.int/data/gho/dat...

Day 23 of #30DayChartChallenge: Log-Scale. Interactive version for all the labels: www.datawrapper.de/_/mun1B/ Data: thegamingsetup.com/console-powe...

Day 22 of #30DayChartChallenge: Stars. I decided on a Star Trek chart and got a bit carried away trying to mimic the LCARS displays. 😅 Data: memory-alpha.fandom.com/wiki/Enterpr...

Day 21 of #30DayChartChallenge: Fossil. This chart depicts the absence of "fossil" - the adoption of electric cars across different countries. Data: ourworldindata.org/electric-car...

Day 20 of #30DayChartChallenge: Urbanization. Data: ourworldindata.org/grapher/popu...

Day 19 of #30DayChartChallenge: Smooth. Couldn't find more current data... Might be fun to repeat later. Data: www.kaggle.com/datasets/leo...

Since I am still behind on the #30DayChartChallenge, this is a combined chart for "negative" (day 16) and "birds" (day 17). Nothing fancy, but a sad indicator of how we keep treating our planet. Data covers 168 monitored species in 30 European countries. Source: pecbms.info/trends-and-i...

Late entry for #30DayChartChallenge Day 15: Complicated. What's more complicated than millions of rows of IMDB data in a network? Had to analyze a specific subset because of hardware. Data: developer.imdb.com/non-commerci... Tools: R (tidygraph, ggraph, data.table), edited in Affinity Photo.

Day 14 of #30DayChartChallenge: Kinship. Maybe not perfect for visualization category "relationships", but I found this interesting: The percentage of kids with single parents varies a lot in Europe. Data: ec.europa.eu/CensusHub/ Tools: Data prep in R (mainly dplyr), viz with @datawrapper.de.

My entry for day 13 of #30DayChartChallenge: Clusters. Not super insightful, but a nice way to brush up my statistical analysis skills. Data: www.kaggle.com/datasets/sky... Tools: R(base and ggplot), edited in Affinity Designer, and some help on the way from ChatGPT.

A little behind on the #30DayChartChallenge. So here is day 12: data.gov. Data: www.ncei.noaa.gov/access/metad... Tools: R(dplyr, zoo for rolling average, ggplot), edited in Affinity Designer.

Day 11 of #30DayChartChallenge: The prompt "stripes" made me think of cliché prisoners. So here we go. Data: World Prison Brief www.prisonstudies.org/research-pub... Made with Tabula, Google Sheets and @datawrapper.de - interactive version: www.datawrapper.de/_/jxfwa/

Day 10 of #30DayChartChallenge: Multimodal. I tried my hand at a visualization video for the first time. Data: trends.withgoogle.com/year-in-sear... Tools: R(ggplot, ggridges, gganimate) and Da Vinci Resolve.

Day 9 of #30DayChartChallenge: Diverging. NFL teams spend a crazy amount of money on players' salaries - but being the biggest spender does not guarantee success. Unfortunately, being frugal doesn't either, as I've come to learn being a Steelers fan. 🫠 Data: www.spotrac.com/nfl/cash/

Day 8 of #30DayChartChallenge: Histogram. I took a look at one of my favorite books, The Lord of the Rings. I like how you can see the shifting narratives here. Have fun exploring! 🔎 Data: www.kaggle.com/datasets/ash... Tools: R(tidytext, ggplot) and Affinity Designer.

Day 7 of #30DayChartChallenge: Outliers. 💸 Data: www.forbes.com.au/news/billion... and www.kaggle.com/datasets/shu... Tools: R (dplyr, ggplot), edited in Affinity Designer.

Day 6 of #30DayChartChallenge: Florence Nightingale Theme Day. Maybe not the ideal way to display this data, but a fun challenge to mimic the original "rose chart". Data: ourworldindata.org/emissions-by... Made with R{owidapi, dplyr and ggplot2}, edited in Affinity Designer.

Day 5 of #30DayChartChallenge: Ranking! I used to think of borders as immovable. With what's happening in the world lately - not anymore. This chart shows that my original feeling was not so accurate to begin with. Data: en.wikipedia.org/wiki/List_of... Made in R, edited in Affinity Designer.

Day 4 of #30DayChartChallenge: Big or small. I'll admit it is a bit crowded, but nonetheless interesting (hopefully): The Bundesliga is very unequal in terms of market value, and teams like Mainz and Gladbach are performing better than market value might suggest.

Day 3 of #30DayChartChallenge: Circular comparisons. How does the sound of The Beatles change over time? Averages of Spotify indicators by album, I used the remastered versions for comparability. Data: www.kaggle.com/datasets/art... Data prep with Excel, chart made with Flourish and Photoshop.

Day 2 of #30DayChartChallenge: Slope. Playing around with IMDB data.

Thought I'd try my hand at this year's #30DayChartChallenge, mainly to stay in shape with my favorite data viz tools. Day 1: Fractions. Data: ourworldindata.org/grapher/shar... Tools used: RStudio, main packages owidapi, dplyr, ggplot, then some editing in Affinity Designer.