James Smith @Floppy

34 posts32 participants1 post today

**Tan G.** @tangandhara.bsky.social@bsky.brid.gy · 14m

Tan G. @tangandhara.bsky.social@bsky.brid.gy

#30DayChartChallenge - Day 12: data.gov data day Still playing catch up but here's a new one chart: the proportion of revenue the Dept of the Interior has made since 2003 from bonuses and royalties on leases of its natural resources. #rstats #dataviz

ANNUAL REVENUE OF U.S. NATURAL RESOURCES
The Department of the Interior collects revenues on over 60 different products and the majority of revenues come from oil, gas, coal, and renewables. The main types of revenue from these sources are royalties (a percentage of the revenue from the commodity sold) and bonus payments (the amount of the winning bid) but there are other payments, such as rents, civil penalties, and inspection fees. The chart shows the percentage of total revenue by type from 2003 to 2025.

**Eugene Alvin Villar** @seav@en.osm.town · 4h *

4h *

Eugene Alvin Villar @seav@en.osm.town

This animated visualization of the constellation #Orion is very pretty!

https://youtu.be/9uhVae828lM

YouTubeThe Shape of Orion in Three DimensionsBy Space Telescope Science Institute

#astronomy #constellations #dataviz

**Nicola Rennie** @nrennie@fosstodon.org · 9h

Nicola Rennie @nrennie@fosstodon.org

A quick plot for #TidyTuesday this week - celebrating the addition of the `penguins` data into base R I used {ggdist} to visualise the distribution of body weights with four different chart types in one!

I couldn't quite decide whether I prefer the light or dark version

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-04-15

Three box plot/density plot/rainfall plots showing body weight for three penguin species, with Gentoo significantly higher. Colours also show density, and species. Background is light with dark text.

#RStats #DataViz #ggplot2

**John Alexis Guerra Gómez** @jguerra@vis.social · 9h

John Alexis Guerra Gómez @jguerra@vis.social

Here is my #chi2025 paper explorer. Visually explore papers by similarity, search for an specific topic or even find what specific sessions to attend. Please let me know what you think! #dataviz @chi

https://johnguerra.co/viz/chi2025Papers/

GIF

**Steven Ponce** @sponce1@graphics.social · 15h

15h

Steven Ponce @sponce1@graphics.social

A scatter plot showing NBA players' True Shooting Percentage versus Usage Rate for the 2023-2024 season. Red dots highlight players with high usage but below-average efficiency (below 57%), labeled as the "Team Concern Area." Six notable inefficient high-usage players are labeled, including Anthony Edwards and DeAaron Fox. Blue dots represent all other players. The visualization demonstrates the negative relationship between offensive responsibility and scoring efficiency.

**Miki :rstats:** @miki_peltzer@techhub.social · 16h

16h

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 16: Negative Relationship FOUND!

¡Lo conseguimos! Tras ajustar por masa corporal, la relación entre Tasa Metabólica Específica (W/kg) y Longevidad Máxima (años) en ~530 especies animales (AnAge DB, outliers quitados) SÍ es negativa (Pearson ρ ≈ -0.42, p < 2.2e-16). #RelationshipsWeek #Animals

El gráfico log-log muestra la tendencia: mayor intensidad metabólica por kilo se asocia con vidas más cortas. ¡Apoya la idea del "ritmo de vida"! Colores por Clase Taxonómica.

Un recordatorio de la importancia de normalizar variables y limpiar datos para ver la señal correcta. ¡Ciencia en acción!

#rstats #ggplot2 #ggpubr | Data: AnAge | Theme: #theme_week3_animals
Código/Viz: https://t.ly/ouLN0

Scatter plot log-log mostrando la relación entre Longevidad Máxima (años, eje Y) y Tasa Metabólica Basal Específica (Watts/kg, eje X) para 534 especies animales (outliers eliminados). Los puntos están coloreados por Clase taxonómica (Mammalia, Aves, Reptilia, Amphibia). Una línea de regresión lineal de color marrón oscuro con intervalo de confianza gris muestra una clara tendencia negativa. El coeficiente de correlación de Pearson (rho = -0.42, p < 2.2e-16) se muestra en la esquina superior derecha. El gráfico utiliza un tema con fondo beige claro y textos en marrón. Fuente: AnAge Database.

#Day16 #Negative #dataviz

**geoObserver** @geoObserver@mastodon.social · 18h

18h

geoObserver @geoObserver@mastodon.social

QGIS-Tipp: Erweiterte Liniensymbolisierung / Advanced line symbolisation https://geoobserver.de/2025/04/16/qgis-tipp-erweiterte-liniensymbolisierung/ via @franzpc #qgis #switch2qgis #DataViz #gistribe #gischat #fossgis #foss4g #OSGeo #spatial #geospatial #opensource #gis #geo #geoObserver pls RT

**Tan G.** @tangandhara.bsky.social@bsky.brid.gy · 1d

Tan G. @tangandhara.bsky.social@bsky.brid.gy

#30DayChartChallenge - Day 11: Stripes Took what I learnt from the scraping the London Marathon results to pull the 2024 Boston Marathon data and plot it in that warming stripes kind of way. Not entirely happy with the way the colours are applied but it'll do. #rstats #dataviz #running

Boston Marathon 2024
The Boston Marathon was the longest-running annual marathon naving been held tor more than 100 consecutive years until 2020 when the Covia-19 pandemic forced its postponement. The race has tough qualitying standards and as such is seen as a prestigious target for many runners.
These charts show the the 30 minute finish time bands for the 25,000 finishers of the 2024 Boston Marathon. The colour of the stripes indicate whether there was a greater density or lesser denisty of finishers in that time band.

**DataScienceLearningCommunity** @DSLC@fosstodon.org · 1d

DataScienceLearningCommunity @DSLC@fosstodon.org

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!

RT @jonthegeek https://fosstodon.org/@jonthegeek/114336585969522119

#RStats #PyData #JuliaLang

**Nicola Rennie** @nrennie@fosstodon.org · 1d

Nicola Rennie @nrennie@fosstodon.org

The recording of my PyData Global talk about data visualisation with Plotnine in #Python is now available!

Give it a watch if you're interested in:

customising plotnine plots
combining with matplotlib functions
adding custom annotations with HighlightText

(or if you're an #RStats user wondering about Plotnine vs #ggplot2)

Link: https://www.youtube.com/watch?v=NBGJuaBF2rc

YouTubeNicola Rennie - Practical Techniques for Polished Visuals with Plotnine | PyData Global 2024By PyData

#DataViz #Plotnine

**Steven Ponce** @sponce1@graphics.social · 1d

Steven Ponce @sponce1@graphics.social

Scatter plot showing NBA 2023-24 season data comparing assists versus turnovers per 36 minutes (correlation = 0.64). Players are divided into four categories: Efficient Playmakers, High-Risk Playmakers, Low-Usage Ball Handlers, and Turnover-Prone. Key players like Haliburton, Wembanyama, and James are labeled, illustrating the complicated relationship between playmaking and ball security.

**Miki :rstats:** @miki_peltzer@techhub.social · 1d

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 15: Complicated Relationships!

Hoy, una matriz de scatter plots con ggpairs para explorar las relaciones entre medidas corporales (Long. Pico, Long. Aleta, Masa Corporal) en los pingüinos de Palmer. ¡Perfecto para el prompt "Complicated"! #RelationshipsWeek #Animals

La matriz lo enseña todo:
* Diagonal: Distribución de cada medida (densidad).
* Abajo: Scatter plots de cada par de medidas (coloreado por Especie).
* Arriba: ¡La correlación $ entre ellas!

Se ven las fuertes relaciones positivas (más grande = aleta más larga) y cómo las especies (Adelie, Chinstrap, Gentoo) forman clusters distintos en este espacio de rasgos. ¡Una forma densa de ver muchas relaciones a la vez!

#rstats #ggplot2 #GGally | Data: #palmerpenguins | Theme: #theme_week3_animals
Código/Viz: https://t.ly/GATJi

Matriz de scatter plots (ggpairs) 3x3 comparando tres medidas corporales para 3 especies de pingüinos de Palmer: Longitud Pico (mm), Longitud Aleta (mm) y Masa Corporal (g). La diagonal muestra gráficos de densidad para cada variable, coloreados por especie (Adelie, Chinstrap, Gentoo). Los paneles del triángulo inferior muestran scatter plots para cada par de variables, con puntos coloreados por especie. Los paneles del triángulo superior muestran el coeficiente de correlación de Pearson para cada par de variables, calculado para cada especie. El gráfico usa un tema con fondo beige y colores naturales para las especies. Fuente: paquete R palmerpenguins.

#Day15 #Complicated #dataviz

**Helena Klara Jambor** @helenajambor@mastodon.social · 1d

Helena Klara Jambor @helenajambor@mastodon.social

Great blog about animated #dataviz @eagereyes.org Alisonhorst

Disagree that impact of #HansRosling was the gapminder tool - I believe more important was narrated explanation of the visualization - much more needed in Vis!

#dataviz

https://observablehq.com/blog/effective-animation

Observable · Dec 5, 2024Five ways to effectively use animation in data visualizationAnimation can be a distraction in data visualization. Or, when applied effectively, it can enhance user engagement, reveal trends, and help viewers track patterns across metrics. Here are five ways to use animation for better data viz.

**Tan G.** @tangandhara.bsky.social@bsky.brid.gy · 2d

Tan G. @tangandhara.bsky.social@bsky.brid.gy

#30DayChartChallenge - Day 10: Multi-Modal It's marathon season for many (good luck!) so I decided to have a look at last year's London marathon results for non-elite runners. The data was scraped with {rvest} and plotted with {ggridges}. #rstats #dataviz #running

London Marathon 2024
Almost 54,000 runners participated in the mass start at the 2024 London Marathon. These charts show the proportion of these non-elite runners who finished quicker or slower than the average time for their age category.
The overall average finish time for women was 4hrs 49mins and for men it was 4hrs 11mins.

**Marie Jamet** @missme@piaille.fr · 2d

Marie Jamet @missme@piaille.fr

Day 9 - Diverging (still behind)
#30DayChartChallenge

I chose to use diverging in a visual way, comparing the number of bachelor's degree in computing in the USA between men and woman.

And used a no code tool (Flourish studio) for lack of time:
public.flourish.studio/story/3039220/

#dataviz

**Jon Harmon** @jonthegeek@fosstodon.org · 2d

Jon Harmon @jonthegeek@fosstodon.org

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

TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results!
TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club!

How to Participate
Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data.
Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data.
Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Scatterplot titled 'Flipper and bill length' showing the relationship between flipper length (x-axis, in mm) and bill length (y-axis, in mm) for three penguin species at Palmer Station LTER. Adelie penguins are represented by orange circles with a slight positive trend line. Chinstrap penguins are shown with purple triangles, clustering higher on the bill length axis and a steeper trend line. Gentoo penguins are shown with dark green squares, positioned farther right along the flipper length axis, with their own upward trend line. A legend at the bottom right indicates the color and shape used for each species. The image is from the palmerpenguins R package.

#RStats #PyData #JuliaLang

**DW Innovation** @dw_innovation@mastodon.social · 2d

DW Innovation @dw_innovation@mastodon.social

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."

https://www.datawrapper.de/blog/category/data-vis-dispatch

#DDJ #DataViz

**Sreedev Krishnakumar** @sreedevkkumar@journa.host · 2d

Sreedev Krishnakumar @sreedevkkumar@journa.host

No guns, just boardrooms. The US-China economic war is being fought by elites, not soldiers. Today's Number Theory looks at the differences and similarities of the elites in both the countries, something that could influence the outcome of this trade war, using data from the World Elite Database.

https://www.hindustantimes.com/editors-pick/number-theory-how-different-are-elites-in-china-and-america-101744601027407.html