#30daychartchallenge — Public Fediverse posts

Un resumen de mi participación en el #30DayChartChallenge 2026 :)

Un resumen de mi participación en el #30DayChartChallenge 2026 :)

#30DayChartChallenge Day 10 | Pop Culture. Top 20 #books of 2025 by genre & publish date, according to Readers’ favourites on Goodreads. 📚📖

Most romance novels were published in spring/summer. Nonfiction books have the longest book titles and Fantasy books lead in terms of total pages.

Tried to make it look like a book shelf, not sure I suceeded 😅

#Dataviz made with #Svelte and #D3

#30DayChartChallenge Day 10 | Pop Culture. Top 20 #books of 2025 by genre & publish date, according to Readers’ favourites on Goodreads. 📚📖

Most romance novels were published in spring/summer. Nonfiction books have the longest book titles and Fantasy books lead in terms of total pages.

Tried to make it look like a book shelf, not sure I suceeded 😅

#Dataviz made with #Svelte and #D3

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day30| Uncertainties – Data Day – Global Health Data Exchange | #30DayChartChallenge | Life Expectancy at Birth — Latin America & Caribbean. Built with #RStats using #ggplot2, #patchwork, #scales, #grid, #gridExtra and #tidyr.

#Day29 | Uncertainties – Monochrome | #30DayChartChallenge | . Coffee Price Forecast — Holt-Winters (HW) Built with #RStats using #forecast, #ggplot2, #dplyr, #lubridate, #scales and #tidyr.

#Day29 | Uncertainties – Monochrome | #30DayChartChallenge | . Coffee Price Forecast — Holt-Winters (HW) Built with #RStats using #forecast, #ggplot2, #dplyr, #lubridate, #scales and #tidyr.

#Day28 | Uncertainties – Modeling | #30DayChartChallenge | Barro Colorado Island — Tree Species Richness Estimation. Built with #RStats using #ggplot2, #patchwork, #MASS, #mgcv, #scales, #vegan, #gridExtra and #grid.

#Day28 | Uncertainties – Modeling | #30DayChartChallenge | Barro Colorado Island — Tree Species Richness Estimation. Built with #RStats using #ggplot2, #patchwork, #MASS, #mgcv, #scales, #vegan, #gridExtra and #grid.

Fun #dataviz for the #30DayChartChallenge Day8 Circular 👽🛸 UFO sightings around the world reported to the National UFO Reporting Center between 1926 & 2013 (total >80,000).

Reports increased since the 1990s, maybe internet made reporting easier or more happening in space? (note: that last week with fewest dots is when the year has 53 weeks which doesnt happen a lot)

Data via #TidyTuesday

Fun #dataviz for the #30DayChartChallenge Day8 Circular 👽🛸 UFO sightings around the world reported to the National UFO Reporting Center between 1926 & 2013 (total >80,000).

Reports increased since the 1990s, maybe internet made reporting easier or more happening in space? (note: that last week with fewest dots is when the year has 53 weeks which doesnt happen a lot)

Data via #TidyTuesday

#Día27 | Incertidumbre – Animación | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr, #ggplot2 y #gganimate.

#Día27 | Incertidumbre – Animación | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr, #ggplot2 y #gganimate.

#Día26 | Incertidumbre – Tendencias | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr y #ggplot2

#Día26 | Incertidumbre – Tendencias | #30DayChartChallenge | Tendencia de la temperatura global. Creado con #RStats usando #dplyr y #ggplot2

#Day25 | Uncertainties – Space | #30DayChartChallenge | Near-Earth Asteroid Orbit Uncertainties. Built with #RStats using #ggplot2 and #ggrepel.

#Day25 | Uncertainties – Space | #30DayChartChallenge | Near-Earth Asteroid Orbit Uncertainties. Built with #RStats using #ggplot2 and #ggrepel.

Day 26 of the #30DayChartChallenge: Trend 📊 (a couple of days early)

A simple chart made with #RStats to show how easy it is to get nice-looking, effective charts with {ggauto}!

#30DayChartChallenge2026 #Day26

Day 26 of the #30DayChartChallenge: Trend 📊 (a couple of days early)

A simple chart made with #RStats to show how easy it is to get nice-looking, effective charts with {ggauto}!

#30DayChartChallenge2026 #Day26

#Día24 | Series de Tiempo – Día Temático - South China Morning Post | #30DayChartChallenge | Producción de Café en Centroamérica: Tendencias 2021-2025. Creada usando #Rstats con #ggplot2, #patchwork, #dplyr, #grid, #gridExtra, #scales, #sf, #rnaturalearth y #rnaturalearthdata.

#Día24 | Series de Tiempo – Día Temático - South China Morning Post | #30DayChartChallenge | Producción de Café en Centroamérica: Tendencias 2021-2025. Creada usando #Rstats con #ggplot2, #patchwork, #dplyr, #grid, #gridExtra, #scales, #sf, #rnaturalearth y #rnaturalearthdata.

#Día23 | Series de Tiempo – Seasons (Temporadas) | #30DayChartChallenge | Malcolm in the Middle. Creada usando #Rstats con #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #scales y #glue.

#Día23 | Series de Tiempo – Seasons (Temporadas) | #30DayChartChallenge | Malcolm in the Middle. Creada usando #Rstats con #ggplot2, #dplyr, #ggtext, #showtext, #patchwork, #scales y #glue.

#Día22 | Series de Tiempo – Nueva Herramienta | #30DayChartChallenge | Precio histórico del aceite de palma. Creada usando #Python con #io, #pandas, #numpy y #matplotlib

#Día22 | Series de Tiempo – Nueva Herramienta | #30DayChartChallenge | Precio histórico del aceite de palma. Creada usando #Python con #io, #pandas, #numpy y #matplotlib

#Día21 | Series de Tiempo – Histórico | #30DayChartChallenge | Precio histórico del café y cacao. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr, #scales, #ggtext, #patchwork, #magick y #cowplot.

#Día21 | Series de Tiempo – Histórico | #30DayChartChallenge | Precio histórico del café y cacao. Creada usando #Rstats con #quantmod, #ggplot2, #dplyr, #scales, #ggtext, #patchwork, #magick y #cowplot.

Day 22 of the #30DayChartChallenge: New Tool 📊

🛠️ Tool: @datawrapper table
🏭 Data: @ourworldindata
💜 With some data wrangling in #RStats

#30DayChartChallenge2026 #Day22

Day 22 of the #30DayChartChallenge: New Tool 📊

🛠️ Tool: @datawrapper table
🏭 Data: @ourworldindata
💜 With some data wrangling in #RStats

#30DayChartChallenge2026 #Day22

From the boom of AI 🤖, to the strategic use of sea routes 🌊 and on to changes in municipal populations🏡, visualisations are useful for illustrating complex topics. Find out more about them and a new batch of #30DayChartChallenge visualizations in our latest blog!👇
https://www.datawrapper.de/blog/data-vis-dispatch-april-21-2026-sea-routes-ai-and-penguins

From the boom of AI 🤖, to the strategic use of sea routes 🌊 and on to changes in municipal populations🏡, visualisations are useful for illustrating complex topics. Find out more about them and a new batch of #30DayChartChallenge visualizations in our latest blog!👇
https://www.datawrapper.de/blog/data-vis-dispatch-april-21-2026-sea-routes-ai-and-penguins

Catching up #dataviz for #30dayChartChallenge Day5 Experimental. Which flower attracts which pollinator? 🌸🐝🦋

Played around with data from the UK Pollinator Monitoring Scheme. For bees or any pollinator plant some lavender, for butterflies go with buddleja aka butterfly bush.

Made in #Svelte + #D3. Still practicing, so progress is slow but learning lots!

Catching up #dataviz for #30dayChartChallenge Day5 Experimental. Which flower attracts which pollinator? 🌸🐝🦋

Played around with data from the UK Pollinator Monitoring Scheme. For bees or any pollinator plant some lavender, for butterflies go with buddleja aka butterfly bush.

Made in #Svelte + #D3. Still practicing, so progress is slow but learning lots!

#Día20 | Series de Tiempo – Cambio Global | #30DayChartChallenge | Anomalía anual de temperatura superficial global respecto al promedio 1951–1980. Creada usando #Rstats con #ggplot2, #dplyr, #readr, #scales y #ggtext.

💡 Changes life expectancy follow changes in total death counts -- an observation of Alexsey Raksha ✨
📝 https://doi.org/10.31219/osf.io/g9mxt
🔗 #rstats code: https://github.com/ikashnitsky/30daychart2026
🧙‍♂️ no ai jumpstarter this time, I worked off Jonas Schoeley's code, all here https://github.com/ikashnitsky/ex-delta
DAY 16 -- causation 💫 #30DayChartChallenge

#Día19 | Series de Tiempo – Evolución | #30DayChartChallenge | Nuevas especies de mamíferos descritas por la ciencia · 1900–2050. Creada usando #Rstats con #ggplot2, #dplyr, #scales y #patchwork.

DAY 15 -- correlation 😱 #30DayChartChallenge
I was struggling to come up with a funny correlation example, so decided to revisit the famous plot that claimed rappers dye young, featured in Calling Bullshit 😆
🔗 #rstats code: https://github.com/ikashnitsky/30daychart2026
🧙‍♂️ pplx chat: https://www.perplexity.ai/search/day-15-correlation-i-recall-th-R_fAVa8gTEuZZ4tOikdgwA#1

#Day18 | Relationships – UNICEF – Data Day | #30DayChartChallenge | UNICEF Children's Climate and Environment Risk Index (CCRI). Built with #RStats using #ggplot2, #dplyr, #ggrepel, and #showtext.

#Day17 | Relationships – Remake | #30DayChartChallenge | Are we making more or fewer remakes as the years go by?. Built with #RStats using #rvest, #dplyr, #stringr, #ggplot2 and #showtext.

Day 17 of the #30DayChartChallenge: Remake 📊

🛠️ A remake of a remake! This is a remake of a chart I originally made with #RStats a few years ago then remade with #Python for the Plotnine contest, and remade again today - this time using LibreOffice Calc!

(Originally started the remake with Excel but it was actually easier in LibreOffice in case you needed another reason to switch to LibreOffice)

#30DayChartChallenge2026 #Day17

#Día16 | Relaciones – Causalidad | #30DayChartChallenge | ¿Más personas = más ciudad?. Creada usando #Rstats con #ggplot2, # patchwork, #grid, #ggrepel, #scales, #showtext y #ggflags.

#Día16 | Relaciones – Causalidad | #30DayChartChallenge | ¿Más personas = más ciudad?. Creada usando #Rstats con #ggplot2, # patchwork, #grid, #ggrepel, #scales, #showtext y #ggflags.

Installations of small-scale renewable technology like solar panels, heat pumps and battery storage have risen notably in the UK over the past few years. ☀️ 🌬️

In March the UK government announced that plug-in solar panels will become legal soon. This will make the data on rollout more patchy, but probably boost uptake.

#Dataviz for the #30DayChartChallenge Day4 Slope

#Renewables #Solar

DAY 14 -- trade 💰 #30DayChartChallenge
This is a very lazy shoot, the map below is completely compiled by Claude Sonnet 4.6 via Perplexity 🤯
I got curious about the cases when land was traded between countries
🔗 #rstats code: https://github.com/ikashnitsky/30daychart2026
🧙‍♂️ pplx chat: https://www.perplexity.ai/search/day-14-trade-another-idea-for-36pTpR.SRW.3mqSuBL_Nlg

🎨 {linuxcolors} a small #rstats package with the identity colors of the most popular #Linux distros 🐧
💎 #ggplot2 ready with scale_{color/fill}_linux() functions

🔗: https://github.com/ikashnitsky/linuxcolors 📦

DAY 13 -- ecosystems 🌍 #30DayChartChallenge
✨ #FOSS world is a unique human #ecosystem

#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

#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

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? 🕵️‍♂️

#DataViz #RStats #ggplot2 #Day29

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? 🕵️‍♂️

#DataViz #RStats #ggplot2 #Day29