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.

**INAGEP** @inagep@mastodon.social · 1d

INAGEP @inagep@mastodon.social

¿Qué es Tableau? La herramienta que convierte datos en decisiones visuales

#Tableau
#DataViz
#AnalíticaDeDatos
#BusinessIntelligence
#TableauTips
#EspecializaciónTableau

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

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.

**Francois Dion** @f_dion · 1d

Francois Dion @f_dion

Rolling metrics of #MTL and #CHI - they will play each other at 7PM in #Montreal. The #Canadiens can clinch their playoff spot if they win.

Their PK has improved and they've only lost 1 home game since the 4 nations break.

To top it all off, it'll be Ivan Demidov's debut at the Bell center, alongside Alex Newhook and Joel Armia.

#hnom #nhl #hockey #analytics #sportsanalytics #dataviz

https://sports.dionresearch.com/nhl/MTL/rolling_metrics_vs_CHI_2025-04-14.l.html

Rolling metrics for Power play, penalty kill and other metrics, comparing Habs and Hawks

**Marie Jamet** @missme@piaille.fr · 1d

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 · 1d

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 · 1d

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

**Data of New York** @nycdatabot@stefanbohacek.online · 1d

Data of New York @nycdatabot@stefanbohacek.online

NYC Cooling Tower System Inspection Results
Source: https://data.cityofnewyork.us/d/f9wb-g8mb
#nyc #data #dataviz

A map with locations from the NYC Cooling Tower System Inspection Results dataset. Please visit the link for full details.

**Last 100 Bills** @last100bills@stefanbohacek.online · 1d

Last 100 Bills @last100bills@stefanbohacek.online

Looking at the last 100 bills in the US #government. #dataviz #civictech

99 bills have been introduced, 0 bills passed the House, 0 bills passed the House & the Senate, 1 bills have been agreed to, 0 bills are being considered, and 0 bills have been enacted.

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

Steven Ponce @sponce1@graphics.social

A lollipop chart showing NBA player combinations organized by playstyle groups (Offensive-Minded, Developing Chemistry, Two-Way Elite, and Defensive-Minded). Each horizontal line represents a player pair with team affiliations in parentheses, extending to points indicating their win percentage. The Offensive-Minded category tops the chart with the highest win rates (up to 90%), dominated by New York Knicks players. Colors differentiate the playstyle groups: yellow for Offensive-Minded, blue for Developing Chemistry, red for Two-Way Elite, and purple for Defensive-Minded. The visualization demonstrates which player relationships produce the most wins across different playing styles and teams.

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

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 14: Kinship! Hoy toca visualizar "parentescos" animales, pero basados en ¡similitud de rasgos! #RelationshipsWeek #Animals

Este dendrograma horizontal es el resultado de un clustering jerárquico (hclust Ward.D2) sobre ~170 especies, usando su Masa Corporal y Longevidad Máxima (log-transformadas y escaladas). ¡Muestra quién se agrupa con quién según su estrategia de vida!

Las ramas unen las especies más similares. La longitud horizontal hasta la unión indica cuán diferentes son. Se ven grandes grupos que separan, por ejemplo, animales muy grandes/longevos de otros más pequeños/rápidos. Es una forma de ver la estructura oculta en los datos de rasgos.

(Solo se muestra 1/3 de las etiquetas para no saturar!)

#rstats #ggplot2 #ggdendro #stats | Datos: Kaggle (S. Banerjee)
Código/Viz: https://t.ly/Y_fwt

Dendrograma horizontal que visualiza el clustering jerárquico de aproximadamente 170 especies animales, basado en la similitud de su masa corporal y longevidad máxima (log-transformadas y escaladas). El árbol se ramifica de izquierda a derecha. Los nombres de las especies (un subconjunto) aparecen como etiquetas en las puntas de las ramas a la derecha. El eje vertical representa las especies/clusters, y el eje horizontal (superior, etiquetado como 'Altura') indica la distancia o disimilitud a la que se unen los clusters. El gráfico utiliza un tema con fondo beige claro y líneas/texto en marrón. Título: "Similitud Animal Basada en Masa y Longevidad". Fuente: Kaggle dataset by S. Banerjee.

#Day14 #Kinship #dataviz

**Data of New York** @nycdatabot@stefanbohacek.online · 1d

Data of New York @nycdatabot@stefanbohacek.online

Energy and Water Data Disclosure for Local Law 84 2018 (Data for Calendar Year 2017)
Source: https://data.cityofnewyork.us/d/4t62-jm4m
#nyc #data #dataviz

A map with locations from the Energy and Water Data Disclosure for Local Law 84 2018 (Data for Calendar Year 2017) dataset. Please visit the link for full details.

**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