Christian Lawson-Perfect @christianp

62 posts53 participants0 posts today

**Miki :rstats:** @miki_peltzer@techhub.social · 9h

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 21: Fossils! Evolución del mix eléctrico peninsular en España (2015-2025). #TimeseriesWeek #SocialData

Gráfico de áreas apiladas con la proporción (%) de cada fuente (Fósiles/Térmica No Ren., Nuclear, Hidro, Eólica, Solar, Otras Renov.), usando datos diarios de @REDElectrica **suavizados con media móvil de 30 días** (`frollmean`).

¡La #TransicionEnergetica en tendencia!
Fósiles: Pierden peso relativo.
Nuclear: Base estable.
Hidro: Variable pero importante.
Eólica: Crecimiento sólido.
Solar: ¡El boom!

La media móvil ayuda a ver la señal principal sobre el ruido diario.

#rstats #ggplot2 #data_table #qs | Data: REE | Theme: #theme_week4_social
Código/Viz: https://t.ly/v3lz_

Gráfico de áreas apiladas mostrando la evolución porcentual, suavizada con media móvil de 30 días, del mix de generación eléctrica en la España peninsular, desde 2015 hasta abril de 2025. El eje Y indica el porcentaje de generación (0-100%) y el eje X los años. Las áreas de colores apiladas representan diferentes tecnologías (aprox. de abajo a arriba): Fósiles/Térmica No Renovable (gris oscuro/negro), Nuclear (naranja), Hidráulica (azul índigo), Eólica (amarillo/dorado), Solar (rosa), Otras Renovables (verde azulado), y Otros/Ajustes (gris oscuro fino arriba). Se observa un crecimiento notable de las áreas Eólica y Solar, mientras que el área de Fósiles/Térmica No Renovable muestra fluctuaciones pero una tendencia a la baja en participación relativa. Fuente: Red Eléctrica de España (REE).

#Day21 #Fossils #dataviz

**Data of New York** @nycdatabot@stefanbohacek.online · 11h

11h

Data of New York @nycdatabot@stefanbohacek.online

DOHMH Health Map - Hepatitis
Source: https://data.cityofnewyork.us/d/nk7g-qeep
#nyc #data #dataviz

A map with locations from the DOHMH Health Map - Hepatitis dataset. Please visit the link for full details.

**Dr Mircea Zloteanu** @mzloteanu@mastodon.social · 11h

11h

Dr Mircea Zloteanu @mzloteanu@mastodon.social

#statstab #326 Visualization in academic writing, by {Experimentology}

Thoughts: Good data visualization is just as important in clear academic writing as the text itself.

#dataviz #plots #academia #academicwriting #reporting
#writing #guide #tutorial

https://experimentology.io/015-viz.html

**Jon Harmon** @jonthegeek@fosstodon.org · 15h

15h

Jon Harmon @jonthegeek@fosstodon.org

Curator: @jonthegeek
https://DSLC.io welcomes you to week 16 of #TidyTuesday! We're exploring Fatal Car Crashes on 4/20!

https://tidytues.day/2025/2025-04-22
https://osf.io/preprints/osf/tzcsy_v1

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.

Two plots entitled Daily Mean Fatality Count, 1992-2016, and Daily Mean Fatality Count in April, 1992-2016. The top plot shows fatalities on the y-axis, and day of year on the x-axis. April 20 is marked with a dashed line, and does not appear to be significantly higher than other days of the year. Fatalities are much higher on January 1st, and generally higher in the months following April than at the beginning of the year. The second plot shows fatalities by day of the year within the month of April. The 20th (day 110 in the year) is approximately in the middle of fatality counts for April. Error bars on both plots are noted as representing 95% bootstrapped confidence intervals. Both plots were generated using code provided by @Rmadillo on GitHub.

#RStats #PyData #JuliaLang

**ruthpozuelo** @ruthpozuelo@mastodon.social · 16h

16h

ruthpozuelo @ruthpozuelo@mastodon.social

The battery of my laptop died so no more work.

I got this far: (see image)

#running #runnersofmastodon #gpx

**Bibliolater** @bibliolater@qoto.org · 16h *

16h *

Bibliolater @bibliolater@qoto.org

**10 charts prove that clean energy is winning — even in the Trump era**

“_Climate progress is actually good business._”

https://www.vox.com/climate/377072/data-energy-trends-renewables-transition-escape-velocity.

#Climate #ClimateChange #Environment #Energy #Politics #Renewables #Solar #Business #Data #DataViz @climatechange

A photo collage with an electric vehicle, geyser, wind turbines and solar panels interacting with various data visualization elements

Vox · 19h10 charts prove that clean energy is winning — even in the Trump eraBy Umair Irfan

**Carmen Torrecillas** @carmen_tm@vis.social · 16h *

16h *

Carmen Torrecillas @carmen_tm@vis.social

We are shortlisted for the @infobeautiful award in the category of People, Language and Identity, alongside #dataviz folks we admire so much such as @puddingviz @kontinentalist_ @flowingdata and others!

https://www.informationisbeautifulawards.com/showcase?acategory=people-language-identity&action=index&award=2024&controller=showcase&page=1&pcategory=short-list&type=awards

Screenshot of our shortlisted article: "One small step for a few, one giant leap for the rest: how to become a European citizen"

**Data of New York** @nycdatabot@stefanbohacek.online · 17h

17h

Data of New York @nycdatabot@stefanbohacek.online

DYCD Participant Demographics by Zip Code
Source: https://data.cityofnewyork.us/d/hebw-6hze
#nyc #data #dataviz

A map with locations from the DYCD Participant Demographics by Zip Code dataset. Please visit the link for full details.

**Dan Oehm** @danoehm.bsky.social@bsky.brid.gy · 18h

18h

Dan Oehm @danoehm.bsky.social@bsky.brid.gy

#TidyTuesday week 15: Base R Penguins Code github.com/doehm/tidytu... Gallery github.com/doehm/tidytues #dataviz #Rstats #ggplot2

**Nicola Rennie** @nrennie@fosstodon.org · 20h

20h

Nicola Rennie @nrennie@fosstodon.org

A trio of plots for day 12, 13, and 14 of the #30DayChartChallenge - for the prompts of Data.gov data, clusters, and kinship!

All made with the same #RStats theme to save time
Making use of highlighting and annotations
Different layouts using facets

#Day12 #Day13 #Day 14 #DataViz #ggplot2

Line chart showing change in income inequality from 1980 to 2020 in Europe, with three small multiples highlighting Denmark, Norway, and Sweden.

Two small multiple charts showing similar trends over time, one for income inequality and one for dairy product availability in the United States. Blue and red backgrounds show Democrat vs Republican presidents at the time.

Scatter chart showing income inequality in 2023, and the change between 2022 and 2023. Quadrants highlight high and low inequality, and positive and negative changes.

**The Kid Should See This** @tksst@universeodon.com · 20h

20h

The Kid Should See This @tksst@universeodon.com

ICYMI: Laser light technology called LiDAR is transforming Maya #archaeology by creating 3D maps of ancient cities hidden beneath jungle canopy. Where archaeologists once hacked through wilderness on foot, now remote sensors capture detailed data showing extensive urban networks.

The 2016 PACUNAM survey alone mapped over 2,000 square kilometers and identified more than 60,000 structures, dramatically expanding our understanding of Maya civilization's scale and complexity.

Learn more: https://zurl.co/mAEUk

A composite illustration showing green laser beams shooting from a person's eyes over dense jungle canopy, revealing Maya pyramids and structures hidden beneath the foliage.

#anthropology #CentralAmerica #city

**Steven Ponce** @sponce1@graphics.social · 20h

20h

Steven Ponce @sponce1@graphics.social

Line graph showing the rise and fall of trilobite fossils over time (540-250 million years ago). The data peaks in the Ordovician period around 480-450 Ma, then sharply declines after the Ordovician Extinction (444 Ma), continues at lower levels through the Silurian period, and diminishes further after the Devonian Extinction (372 Ma).

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

Nicola Rennie @nrennie@fosstodon.org

For Day 20 of the #30DayChartChallenge and the prompt of "Urbanization", I used #Python to make a very abstract plot in the style of a city skyline silhouette!

(Still using the income inequality data via Our World in Data)

Area chart showing percentage of income received by richest 1%

#DataViz #Day20

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

Nicola Rennie @nrennie@fosstodon.org

Catching up on Day 16 of the #30DayChartChallenge with the prompt of "Negative"!

Waterfall chart made in #RStats
Doesn't feel as intuitive when decreases are a positive outcome
Needed a few hacky approaches to get the right layout

A bar chart showing yearly changes in the income share of South Korea’s richest 1% from 1981 to 2015. Inequality decreased in the early 1990s but rose sharply from the late 1990s, peaking in 2010. Brown bars show increases; blue bars show decreases.

#DataViz #ggplot2 #Day16

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

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 20: Urbanización ¿Cómo ha cambiado la distribución de la población española por tamaño de municipio entre 2003 y 2022? #TimeseriesWeek #SocialData

Este gráfico de áreas apiladas (geom_area) lo visualiza usando datos del Padrón del INE. Cada color representa un tramo de tamaño municipal.

Observaciones clave:
* La España rural (<5k hab.) pierde peso porcentual (~14% -> 12%).
* Los municipios medianos (especialmente 20k-50k y 50k-100k) ganan algo de proporción.
* Las grandes ciudades (>100k) mantienen su cuota bastante estable (~40%).

Parece que en estas dos décadas, más que un éxodo masivo a las grandes urbes, ha habido una consolidación en ciudades intermedias. ¿Reflejo de la "España Vaciada" y crecimiento periurbano?

#rstats #ggplot2 #data_table | Data: INE | Theme: #theme_week4_social
Código/Viz: https://t.ly/AN1-q