#30DayChartChallenge - Day 28: Inclusion What's the opposite of inclusion? Exclusion and this chart compares the rate of adults at risk of poverty or social exclusion in the EU based on their country of birth. #rstats #dataviz
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#30DayChartChallenge
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30 Day Chart Challenge Blog Post
Featuring:
What I've learned
My favourite charts
Advice if you're thinking about participating!
Link: https://nrennie.rbind.io/blog/30-day-chart-challenge-2025/
Nicola Rennie30 Day Chart Challenge 2025 | Nicola RennieThe 30 Day Chart Challenge is a data visualisation challenge where participants create a chart for each daily prompt. In this blog post, I'll recap the charts I made during the 2025 challenge and discuss the data and tools I used, alongside what I learned during the process.
30 Days.
¡Reto #30DayChartChallenge 2025 COMPLETADO! 
30 días, 30 visualizaciones con #RStats y #ggplot2.
Ha sido un viaje increíble explorando comparaciones, distribuciones, relaciones (¡animales!), series temporales (sociales, económicas) e incertidumbre (riesgo, exoplanetas, mapas...).
Puedes ver la galería completa (y todo el código) en mi repositorio:
https://github.com/michal0091/dataviz/tree/main/R/30DayChartChallenge2025
¡Gracias por seguir el reto! #dataviz #DataVisualization #DataStorytelling #ChallengeComplete #Rprogramming
It's the last day of the #30DayChartChallenge, and the final prompt is "National Geographic Theme" so here's my first and only map created for the challenge!
Made with #RStats
Colours inspired by National Geographic logo
{ggpattern} to use striped areas for missing data
It's almost the end of the #30DayChartChallenge and for the prompt of "Extraterrestrial", I decided to make a chart designed in the style of an extraterrestrial who has never heard of good data visualisation principles!
How many chart crimes can you spot? 
The Parkes Observatory is national heritage listed in Australia. Apparently "Australia was an international leader in the ground-breaking field of radio astronomy research in the post-World War II period".
It may have even helped CSIRO invent wifi!
A very loose interpretation of the "Inclusion" prompt for Day 28 of the #30DayChartChallenge, by "including" two charts in one!
Data prep with #RStats Waffle plot made with D3
Icons from Font Awesome
Another #Quarto + #RStats + Observable combination for Day 27 of the #30DayChartChallenge
Two heatmaps to illustrate the prompt of "Noise" - unsorted heatmaps look like random noise, (sensibly) sorted heatmaps are more likely to reveal patterns!
#30DayChartChallenge - Day 20: Urbanization UN Habitat data across global regions showing how green areas per capita has declined since 1990. #rstats #dataviz
Catching up on Day 24 of the #30DayChartChallenge where the prompt is using data from the World Health Organization 
Tried an experimental way to visualise two variables, where lower values are better for both
Area shrinking towards zero indicates improvement
Think this would work better if there's a more natural scaling between two variables
For the "Monochrome" prompt on Day 26 of the #30DayChartChallenge, I decided to do a little bit of data art in D3!
For the "Risk" prompt on Day 25 of the #30DayChartChallenge I decided to create something using a 'risky' chart type - a pie chart!
(Actually not just one, but many, many pie charts!)
#30DayChartChallenge Day25 Risk
Rates of poor mental health are increasing – especially among young people, raising concerns about impacts on their health and employment prospects.
Missing out on early job experience makes it harder to build skills, secure stable work and progress to better-paid roles – without action, this will become a major challenge for the next generation.
More in our Commission for Healthier Working Lives report 
https://www.health.org.uk/reports-and-analysis/reports/action-for-healthier-working-lives
For Day 23 of the #30DayChartChallenge, the prompt is "Log scale"
(Technically, this isn't actually a log scale but I wanted to play around with the idea of giving the more recent, more densely located data points more space)
#30DayChartChallenge Día 24: ¡Usando datos de la @WHO! 
Hoy comparamos la cobertura de vacunación DTP3 (% niños 1 año) entre países agrupados por nivel de ingresos (Banco Mundial, 2000-2022). #TimeseriesWeek #SocialData #GlobalHealth
El gráfico muestra: ¡Gran mejora en todos los grupos hasta ~2019!
¡Pero una brecha enorme persiste! Los países de ingresos altos (amarillo 
) cerca del objetivo 95% (línea rosa).
Los países de ingresos bajos (azul índigo 
), aunque mejoraron mucho desde el 2000, se quedaron sobre el 70% y sufrieron un retroceso post-pandemia.
Un reflejo claro de la #EquidadEnSalud (o la falta de ella) a nivel global. ¡El acceso a vacunas básicas no es igual para todos!
#rstats #ggplot2 #data_table | Data: WHO GHO | Theme: #theme_week4_social Código/Viz: https://t.ly/fRi1m
I'm almost back on track with the #30DayChartChallenge with another trio of plots - this time all made from scratch in D3!
I learned how to: Make scatter plots, line charts, and area charts
Highlight and annotate specific data points
Fit Loess curves in D3
For Day 22 of the #30DayChartChallenge and the prompt of "Stars", I found it quite tricky to make the income inequality data I've been working with fit the prompt.
Is it a radar chart?
Is it a star?
Is it a constellation?
I don't know whether I love it or hate it , but either way it's made with #RStats! For reference, the inner 'stars' represent 1%.
Making use of highlighting and annotations
For Day 20 of the #30DayChartChallenge and the prompt of "Urbanization", I used #Python to make a very abstract plot in the style of a city skyline silhouette! 
(Still using the income inequality data via Our World in Data)
