Exploring the beauty of sound with fire spectrograms! These visualizations reveal hidden patterns in audio, like bat calls. #datavisualization #sound #spectrogram #wabisabi old_home/images/fire_spectrogram.png
#Datavisualization
Most textbook examples use somewhat boring monochrome visuals for Cellular Automata. In a series of posts, I’m exploring how to bring these lifelike systems to life through color. Here's the first one, where I experiment with color gradients to paint evolving landscapes.
#CellularAutomata #DataVisualization #GenerativeArt #ComplexSystems #ComputationalCreativity #GenArt
Hello! Here are some marathon infographics for @focus_magazin 🏃🏾♀️💨Thank you for another collaboration, AD @miriambloching & CD @frances.uckermann ✌️
#infographics #datavisualization #marathon #sportillustration #illustration #editorialillustration
🌿🌧️ Predicting the extent of invasive Buffelgrass is important for its management. Travis Matlock, a runner-up in the @uazlibraries 2024 #DataVisualization Challenge, in collaboration with the USA NPN, created an informative map that tracks rainfall "events" over a 30-day period to forecast buffelgrass green-up 1–2 weeks in advance. Check out the map here: https://doi.org/10.25422/azu.data.25705419
Travis Matlock (2024). CC-BY 4.0. #OpenData #OpenScience #DataViz #buffelgrass #climatedata #UniversityofArizona
gitstarter Hybrid UI that automates Git-CLI workflows for beginners and GitHub-API tasks for power users
📄 License
This project is released under the MIT License (see LICENSE).
© 2025 RePromptsQuest. All rights reserved.
🔗 Links
GitHub: https://github.com/reprompts/gitstarter
LinkedIn Group: https://www.linkedin.com/groups/14631875/
Twitter / X: @RePromptsQuest
🚀 Quickstart
Install
pip install gitstarter
Run: gitstarter
Follow the UI
#OpenSource #GitHub #PyPI #Python #Streamlit #DataVisualization #ShowDev #DevTools
Making your data analysis more insightful and informative is effortless with ggstatsplot. This powerful ggplot2 extension in R combines statistical analysis and data visualization in a single workflow, helping you generate plots that include statistical summaries directly on the visualizations.
The attached visual, which I created using ggstatsplot, showcases its capabilities.
Learn more: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
#programming #statisticalanalysis #datavisualization #dataanalytics
Creating beautiful charts with JRuby and JFreeChart
https://blog.headius.com/2025/04/beautiful-charts-with-jruby-and-jfreechart.html
#HackerNews #Creating #beautiful #charts #with #JRuby #and #JFreeChart #JRuby #JFreeChart #DataVisualization #Programming
Build stunning KPI tiles with these simple tricks in Excel #exceltips #datavisualization
Tired of cluttered dashboards that overwhelm your viewers? This post is for you! ✨ I'm showing you how to create eye-catching ... source
Hey everyone!
I’m looking for a good way to create a #graph or #chart from a #dataset under #Linux.
The data is in a #CSV file and has around 180,000 rows. One column contains the timestamps, and several other columns contain the actual #measurement values.
Not every value is recorded at every timestamp, so there are different sampling rates across columns, and some fields are empty. The solution should be able to handle that – missing data, large file size, and multiple series plotted over #time.
What’s your preferred tool or approach for this kind of visualization?
Ideally something easily repeatable.
#DataVisualization #CSVHandling #Linux
I’m looking for a good way to create a #graph or #chart from a #dataset under #Linux.
The data is in a #CSV file and has around 180,000 rows. One column contains the timestamps, and several other columns contain the actual #measurement values.
Not every value is recorded at every timestamp, so there are different sampling rates across columns, and some fields are empty. The solution should be able to handle that – missing data, large file size, and multiple series plotted over #time.
What’s your preferred tool or approach for this kind of visualization?
Ideally something easily repeatable.
#DataVisualization #CSVHandling #Linux
DesignBoom : BIG’s interactive water installation reveals power of roca connect at milan design week 2025 https://www.designboom.com/design/big-interactive-water-installation-roca-connect-milan-design-week-04-30-2025/ #steelarchitectureanddesign #interactiveinstallation #bjarkeingelsgroup/BIG #MilanDesignWeek2025 #smarthometechnology #datavisualization #bathroomdesign #design #roca
¡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
#30DayChartChallenge ¡Día 30 y FIN! 🎉 Último tema: National Geographic 🗺. Mi mapa: Riesgo de Desertificación en España (Península, Baleares y Canarias), estilo NatGeo. #UncertaintiesWeek #Mapping
Visualizando la vulnerabilidad territorial (riesgo/incertidumbre) con datos del PAND (MITECO 2008). Colores de amarillo pálido (Bajo) a rojo oscuro (Muy Alto).
Intenté capturar la esencia NatGeo: paleta, fuentes (Lato/Gudea), escala, norte y la famosa ¡banda amarilla! 🟨 (añadida con grid). Canarias colocadas con {mapSpain}.
¡Un desafío cartográfico para terminar el mes! ¡Encantado de haber completado los 30 días! 💪
🛠 #rstats #ggplot2 #sf #ggspatial #mapSpain #grid | Data: MITECO PAND | Theme: Custom NatGeo
📂 Código Final del Reto: https://t.ly/Ol06w
#Day30 #NationalGeographic #dataviz #DataVisualization #Desertificacion #España #MedioAmbiente #RiskMap #Cartografia #GIS #ggplot2 #RStats #ChallengeComplete
🆕 post: Data viz - Exploring recipe data set - where and what
In the third instalment of recipe data set exploration let's look at mainly where the recipes are from and discuss what's in them.
https://www.sarahgebauer.com/post/data-viz-exploring-recipe-data-set-where-and-what/
#30DayChartChallenge Día 29: Extraterrestrial! 👽✨ ¡Planetas con su incertidumbre a cuestas! #UncertaintiesWeek #Astronomy
Volvemos al gráfico Radio vs Insolación (log-log, color=Temp) de exoplanetas (NASA Archive). Pero hoy añadimos una capa visual para la incertidumbre: el "halo" gris ⚪️ detrás de cada punto.
El tamaño del halo es proporcional al log(error) reportado para la Insolación. ¡Halos grandes = más incertidumbre en la energía que recibe ese planeta!
Es un recordatorio de que los datos astronómicos tienen errores y no todos los puntos son igual de "seguros". Interesante ver qué planetas en la zona habitable (verde) tienen más incertidumbre. (+ Venus/Tierra/Marte 💎).
🛠 #rstats #ggplot2 #ggrepel | Data: NASA | Theme: #theme_week5_uncertainty
📂 Código/Viz: https://t.ly/ygNLW
#Day29 #Extraterrestrial #dataviz #DataVisualization #Exoplanets #HabitableZone #Astrobiology #UncertaintyViz #ErrorVisualization #NASA #ggplot2 #RStats #Science
![Gráfico de dispersión log-log que muestra exoplanetas según el Flujo de Insolación recibido (relativo a la Tierra, eje Y) frente al Radio Planetario (Radios Terrestres, eje X). Cada exoplaneta se representa con dos puntos superpuestos: uno de fondo, más grande, gris y semitransparente (halo), cuyo tamaño indica la incertidumbre en la insolación; y uno encima, más pequeño y menos transparente, coloreado por Temperatura de Equilibrio (K) [escala continua púrpura-amarillo]. Una banda horizontal verde translúcida marca la zona habitable aproximada. Puntos en forma de diamante blanco etiquetan Venus, Tierra y Marte como referencia. Fuente: NASA Exoplanet Archive.](https://files.mastodon.social/cache/media_attachments/files/114/420/241/019/825/052/original/36d27ad1ec5c2ae7.png)
🆕 post: Data viz - Exploring recipe data set - cooking time
This is a second post about exploring the recipe data set. After exploring the servings let's look at the cooking time and the prep time.
https://www.sarahgebauer.com/post/data-viz-exploring-recipe-data-set-cooking-time
The CFP for the 2025 Information Plus conference is now out and available at our website here: https://informationplusconference.com/2025/submissions.html
Please consider submitting a presentation, workshop, or exhibition for the conference at Northeastern and MIT November 14-16! #InfoPlus #DataVisualization #DataJournalism #HCI #Humanities #Data #Art
Tiesitkö, että Copilot Chatin avulla voit luoda kuvaajia visualisoimaan tietoa? Se hyödyntää Pythonia käyttäjäystävällisesti. Ota selvää lisää: aka.ms/CopilotSuomi #CopilotChat #DataVisualization #Python #byAI
Anyone know where I might be able to get a good source for which players are on the pitch for each NFL play. Hoping for #JacksonvilleJaguars data but am flexible. #NFL #datavisualization #planning
Tiesitkö, että Copilot Chatin avulla voit luoda kuvaajia tiedon visualisoimiseksi? Python-koodia hyödyntäen se tekee datastasi esiin tulevaa! Opi lisää ja kokeile ilmaiseksi: aka.ms/CopilotSuomi #DataVisualization #AI #CopilotChat #byAI
#30DayChartChallenge Día 28: Inclusion! 🤝 O... la falta de ella a nivel territorial en España 🇪🇸. Hoy comparamos la evolución de la tasa de paro trimestral (EPA/INE, 2005-2024) en varias CC.AA. vs la media nacional (rojo). #UncertaintiesWeek #SocialData
¡El gráfico habla por sí solo! Mirad la enorme brecha que se abre, sobre todo tras 2008, entre regiones como Andalucía y otras como País Vasco o Navarra. Madrid, más cerca de la media. Refleja mercados laborales muy diferentes y retos de cohesión enormes. La "inclusión" territorial en el empleo sigue siendo una asignatura pendiente.
Una visualización para reflexionar sobre las desigualdades estructurales.
🛠 #rstats #ggplot2 #data_table | Data: INE (EPA) | Theme: #theme_week5_uncertainty
📂 Código/Viz: https://t.ly/UwPQG
#Day28 #Inclusion #dataviz #DataVisualization #Paro #Unemployment #EPA #INE #España #Desigualdad #Territorio #ggplot2 #RStats #TimeseriesWeek
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