The #30DayChartChellenge was created by Cédric Scherer (@CedScherer) and Dominic Royé (@dr_xeo) and ran for the first time throughout April 2021. If you missed it, however, fear not as it seems a return in 2022 is already planned! The challenge was to create a new chart every day to a schedule of thirty topics grouped into five categories:
Examples of Submissions
Before getting in to the details of the challenge, I wanted to share a small sample of the many beautiful charts that were submitted throughout the month. I’ve added the twitter handles of the creator above each image and I’d highly recommend clicking this hashtag #30DayChartChallenge and scrolling through twitter to see many more. The quality of work was really outstanding, especially given the daily daily format.
Data Sources
There were no specific datasets provided, so participants had free reign to visualise whatever data they wished. The organisers provided a list of useful data sources, however, which can be found on the project’s github page. I made good use of these along with some other sources. The ones I used most frequently were Data is Plural, Eurostat and Our World in Data. A complete list of the data sources I used can be found at the end of this post.
Chart Types
Again, there was no restrictions here, however, some helpful guidance was given in the form of an image of charts best suited for that week’s category, for example, for week five, uncertainties, the below image was shared:
My Approach
I knew it was going to be a stretch to complete every day, so to make things a bit easier I decided to keep to the same format for all 30 days. All my submissions were created in Tableau, so I had one dashboard template that I copied over day-to-day. The title placement, fonts, footers, background and padding were kept almost identical throughout so I could focus on the chart.
Aside from saving time, I also wanted to impose some restrictions on the form factor. For most of my Tableau Public work, there’s rarely constraints but for my day job there is a fixed dashboard size we use and fonts, logo etc. and I thought it would be good practice imposing some similar constraints for the challenge to practice adapting each day’s chart as needed to work in that format.
The dashboard size I chose was 1200 x 800 pixels. Below is an example from day 20 looking at Nasa’s upwards trending planetary science spend:
Learnings
I found that locating a suitable data source and enriching it, if needed, and cleaning it took the most time. This wasn’t too surprising as I often find this to be the case, but with the daily cadence, this became a challenge and sometimes I ended up using a data source which I wasn’t totally happy with due to time.
The uncertainty category was the most challenging as I rarely have a need to create charts with features such as prediction intervals and confidence bands etc. That said, it was a good opportunity to learn more about these features and the types of data they best work with.
It was eye-opening to see the variety and style of charts it’s possible to create with R (and other tools). I hadn’t appreciated quite how flexible a tool it is.
30 Days, 30 Charts
Below are thumbnails of the 30 charts I created for the challenge. You can interact with and download all of them from my Tableau Public profile.
Links to visualisations and data sources
| Day | Category | Topic | Chosen Subject | Interactive visualisation | Data source |
|---|---|---|---|---|---|
| 1 | Comparisons | Part-to-whole | International students by country | https://tabsoft.co/31WGJpO | https://stats.oecd.org/ |
| 2 | Comparisons | Pictogram | Change in forest volumes | https://tabsoft.co/3rXHVnm | https://stats.oecd.org/ |
| 3 | Comparisons | Historical | Timeline 20th/21st century wars | https://tabsoft.co/2PvoYes | https://bit.ly/2RiMg7Z |
| 4 | Comparisons | Magical | Popular playing card choices | https://tabsoft.co/3sQSCsX | https://bit.ly/3mhy4Yd |
| 5 | Comparisons | Slope | Change in wealth of top 10 | https://tabsoft.co/39IIpaz | bit.ly/3umG6BN |
| 6 | Comparisons | Experimental | Comparing the four largest retailers | https://tabsoft.co/39Pm9vP | https://bit.ly/31Q0wXN |
| 7 | Distribution | Physical | Distribution of human heights by country | https://tabsoft.co/2PBSwY2 | https://bit.ly/3sXUIHL |
| 8 | Distribution | Animals | Animal median life expectancy | https://tabsoft.co/3cYHiFN | https://go.nature.com/3s45Txb |
| 9 | Distribution | Statistics | DVS Survey of time spent doing data vis | https://tabsoft.co/2Q4D06J | https://bit.ly/3dNsGs2 |
| 10 | Distribution | Abstract | Colours of Skittles | https://tabsoft.co/2OD6isR | https://bit.ly/3a25PI5 |
| 11 | Distribution | Circular | UK births by day | https://tabsoft.co/3fZReRl | https://bit.ly/3uF8saG |
| 12 | Distribution | Strips | Tour de France times and speeds | https://tabsoft.co/2QkCRvV | https://bit.ly/3s7uXDC |
| 13 | Relationships | Correlation | GDP to landline usage | https://tabsoft.co/3sgbqkv | https://bit.ly/32cAePG |
| 14 | Relationships | Space | Length of space missions | https://tabsoft.co/32cavXw | https://bit.ly/3tmcrZr |
| 15 | Relationships | Multivariate | Job and life satisfaction | https://tabsoft.co/2QwBb2s | https://ec.europa.eu/eurostat/en/ |
| 16 | Relationships | Trees | Trees in Westminster | https://tabsoft.co/3dlBciR | https://bit.ly/3wV8SeW |
| 17 | Relationships | Pop culture | MoMA artworks | https://tabsoft.co/3uS8b4e | https://bit.ly/2OVQK3p |
| 18 | Relationships | Connections | Likelihood of being Facebook friends | https://tabsoft.co/3turoZB | https://bit.ly/fbSCIdata |
| 19 | Time series | Global change | Volcano eruptions timeline | https://tabsoft.co/3x8RMu3 | https://bit.ly/3dqnuvd |
| 20 | Time series | Upwards | Nasa’s planetary space spending | https://tabsoft.co/3anlSQU | https://bit.ly/3ardksa |
| 21 | Time series | Downwards | Average annual working hours | https://tabsoft.co/3sFeB5o | https://bit.ly/3tATZfM |
| 22 | Time series | Animation | Change in temperatures in US counties | https://tabsoft.co/3eqVjLQ | https://bit.ly/3gBNo0X |
| 23 | Time series | Tiles | London crime rate by borough | https://tabsoft.co/3nepGcq | https://bit.ly/3gBh0LY |
| 24 | Time series | Monochrome | Aircraft Co2 emissions | https://tabsoft.co/3gA6zYW | https://bit.ly/32MAVzj |
| 25 | Uncertainties | Demographic | Population of EU countries | https://tabsoft.co/3tR6Fzm | https://bit.ly/3dLXZEE |
| 26 | Uncertainties | Trends | GDP per capita and happiness | https://tabsoft.co/3gFh5hD | https://bit.ly/3tYgWKe |
| 27 | Uncertainties | Educational | Graduate median salaries | https://tabsoft.co/3npAm8a | https://bit.ly/3niSYXi |
| 28 | Uncertainties | Future | UK house price forecasts | https://tabsoft.co/3eC005C | https://bit.ly/2QEbXQn |
| 29 | Uncertainties | Deviations | Broadband speeds by country | https://tabsoft.co/2SakVoN | https://bit.ly/3e286Fr |
| 30 | Uncertainties | 3D | Growth of 3D printing | https://tabsoft.co/3eGh76w | https://bit.ly/3vwwKE9 |
Thanks again to Cédric and Dominic for creating this initiative. It was great to see the variety of work submitted by everyone and to get a better sense of what’s possible with the variety of tools used. I look forward to its return next year.
Thanks for reading!
Marc Reid
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