Curator: @jonthegeek
https://DSLC.io welcomes you to week 15 of #TidyTuesday! We're exploring Base R Penguins!
https://tidytues.day/2025/2025-04-15
https://zenodo.org/records/14902740
Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md
Always worth your time: The Data Vis Dispatch, a weekly newsletter compiled by our colleagues over at DataWrapper.
The focus is on "the most beautiful data visualizations, large and small, from newsrooms and other organizations all over the world."
Après l’élection d’aujourd’hui à Soleure voici mon graphique MÀJ.
Noms 2594 DéputéEs: http://bit.ly/SiegesParlCantonauxNoms 153/154 Ministres: http://bit.ly/SiegesExCantonaux
#DataViz #CHvote 
Image HD ici: https://nashagagnebin.ch/img/eluscantonaux.png
#30DayChartChallenge Día 13: Clusters Animales! 
Explorando la relación Masa Corporal vs Longevidad Máxima. #RelationshipsWeek
Usando un dataset de Kaggle (+170 especies, ¡gracias S. Banerjee!) y tras una divertida limpieza de datos con rangos/unidades mixtas 
, este scatter plot log-log revela patrones.
Coloreamos por Dieta: 
Carnívoro(verde) 
Herbívoro(ocre) 
Omnívoro(azul).
Se ve la tendencia general (más grande = más longevo), pero los clústeres por dieta sugieren distintas **estrategias de historia de vida**. ¿Cómo gestionan su energía y longevidad según lo que comen? 
¡Una visualización para explorar la alometría y la diversidad ecológica!
#rstats #ggplot2 y mi nuevo tema #theme_week3_animals.
Código/Viz: https://t.ly/ehPiu
Useful questions to ask before you start coding a #dataviz dashboard, from @ivelasq3 's #ShinyConf2025 presentation.
Full slides: https://ivelasq-shinyconf2025.share.connect.posit.cloud/#/section
#QuartoPub #RShiny #RStats #ShinyConf
#30DayChartChallenge Day11 Stripes – Mentions of ‘butt’ and ‘fart’ in Bob’s Burgers episodes.
I’m a big fan of the TV Show, so this had to happen eventually. Butt(s) are mentioned more than 550 times, but Tina’s love for butts can’t top Gene’s fart song.
Made 100% in #python, using many of Joseph Barbier’s amazing libraries to work with colours, fonts and annotations! Full code https://github.com/Lisa-Ho/small-data-projects/tree/main?tab=readme-ov-file#042025-bobs-burgers-dialogue
#30DayChartChallenge - Day 9: Diverging Looking at labour force participation rates in UK, South Korea, Iran, Indonesia, Saudi Arabia, and the USA. Tried to for for the @data.ft.com look. #rstats #dataviz
My #Arctic sea-ice thickness and volume data visualizations were just updated through March 2025: https://zacklabe.com/arctic-sea-ice-volumethickness/ #SciComm #DataViz #OpenScience #OpenData
2025 #30DayChartChallenge | day 12 | distributions | data.gov
.
: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_12html
.
#rstats | #r4ds | #dataviz | #ggplot2
#30DayChartChallenge Día 12: Gov Data Day! 
Explorando la distribución del spread 10Y-2Y del Tesoro USA (datos de FRED desde 1976).
Este histograma/densidad va más allá del valor diario: muestra la *probabilidad* histórica de cada nivel del spread. ¡Clave para entender expectativas económicas!
Puntos clave:
* Modo principal > 0 (curva normal es lo más común).
* ¡La inversión (<0, línea discontinua) tiene una probabilidad no trivial! 
Es la famosa señal pre-recesión. La distribución nos dice cuán "normal" es esa señal en perspectiva histórica.
* La forma general revela info sobre la dinámica de tipos.
Una visualización sobre la estructura probabilística de un indicador líder fundamental.
#rstats #ggplot2 #quantmod #grid Código/Repo: https://t.ly/0RDmK
Just added #Arctic climate rankings for March 2025: https://zacklabe.com/archive-2025/, a month with particularly low wintertime sea ice conditions
A long-term project to bring long-term temperature data to life!
Data from the Met Office on average temperatures
Designed with {ggplot2} in #RStats
Crocheted with yarn
Back when this guy was last in office, and Covid was unchecked and data around it was being censored, and yet people were dying...I created this visualization to help communicate how many were dying...to communicate how big 400,000 deaths are: https://kristinhenry.github.io/howBig/
It's Day 11 of the #30DayChartChallenge, and the prompt is "Stripes"
Showing % of world income received by richest 1% from 1980-2023 (
= higher inequality)
Inspired by the iconic climate change warming stripes chart
Made with Plotnine in Python
2025 #30DayChartChallenge | day 11 | distributions | stripes
.: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_11.html
.
#rstats | #r4ds | #dataviz | #ggplot2
There's now an interactive version of this chart where you can select a country, and see how it compares - thanks to Yan Holtz for this suggestion!
Orientation of the chart changed to make it fit on a screen Built entirely in R with {ggiraph} and {htmltools}
Still working on some styling and how easy it is to select a point
Will probably end up as a blog post soon!
Check it out here: https://nrennie.rbind.io/data-viz-projects/#income-inequality
Extremely robust and well designed Shiny app from Curbcut, making massive amounts of local city data accessible via maps. They dealt with performance issues partly by pre-computing data for every city block 
and storing those in qs files, as well as rendering pre-generated Mapbox vector tiles.
Worth watching the talk by Maxime Bélanger de Blois if/when it's online.
App: https://montreal.curbcut.ca/
GitHub: https://github.com/Curbcut/curbcut-montreal
#RShiny #RStats #ShinyConf2025 #ShinyConf #dataviz
#30DayChartChallenge Día 10: ¡Buceando en la Distribución del VIX! 
En lugar de solo ver la línea del VIX, hoy analizamos su "distribución de probabilidad" por Presidencia de EE.UU. (Clinton -> Trump 2º). ¡La forma lo es todo!
Usando #rstats y #ggplot2, estas densidades facetadas nos permiten investigar:
* Modos Dominantes: ¿Cuál era el nivel "normal" de VIX (el pico más alto)? ¿Cambió mucho?
* Multi-modalidad: ¿Hay evidencia de múltiples estados de volatilidad (picos secundarios) dentro de un mismo mandato?
* Riesgo de Cola: ¿Qué tan probable era el "pánico" (VIX > 35)? ¡Compara las colas derechas!
Estos patrones reflejan los distintos regímenes de volatilidad y la percepción del riesgo sistémico. No es solo el nivel, ¡sino la "estructura" de la incertidumbre lo que importa!
Datos: Yahoo Finance via #quantmod.Código: https://t.ly/kikdo
Breaking down the last 100 bills in the US #government. #dataviz #civictech
**Major foreign holders of US Treasury Securities (2022)**
