Leuchtturm @leuchtturm

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**Steven Ponce** @sponce1@graphics.social · 21h

Steven Ponce @sponce1@graphics.social

Line chart showing pharmaceutical giants' stock performance from 2018-2025 on a logarithmic scale. The chart displays five companies (LLY, ABBV, MRK, BMY, PFE) with Eli Lilly (LLY) showing the strongest growth, reaching nearly $1,000 by 2025. The COVID-19 pandemic period is highlighted with a pink vertical band in 2020. All companies experienced volatility during this time, with different recovery patterns afterward.

**Nicola Rennie** @nrennie@fosstodon.org · 22h

22h

Nicola Rennie @nrennie@fosstodon.org

I'm almost back on track with the #30DayChartChallenge with another trio of plots - this time all made from scratch in D3!

I learned how to:
Make scatter plots, line charts, and area charts
Highlight and annotate specific data points
Fit Loess curves in D3

Chart showing lines for Australia and the World, with a shaded grey area in between

Chart showing lines for fossil fuels and income held by richest 1%

chart of income inequality over time with smooth line fitted

#DataViz #Day18 #Day19

**Miki :rstats:** @miki_peltzer@techhub.social · 23h

23h

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 23: ¡El poder de la Escala Logarítmica! Hoy vemos el PIB per cápita de España (1990-2023, PPA $const) de otra manera. #TimeseriesWeek #SocialData

¿La clave? scale_y_log10(). En esta escala, la *pendiente* de la línea azul representa la tasa de crecimiento porcentual. ¡Ideal para comparar el ritmo económico a lo largo del tiempo!

Se ven clarísimas las diferentes fases:
* Crecimiento fuerte pre-2008.
* El largo estancamiento post-Gran Recesión (zona rosa 08-13).
* El bache del COVID (zona rosa 20-21).
* La crisis de los 90 (zona rosa 92-94).

Una herramienta básica pero potente del análisis de series temporales económicas.

#rstats #ggplot2 #WDI | Data: World Bank | Theme: #theme_week4_social
Código/Viz: https://t.ly/e3_cv

Gráfico de líneas que muestra la evolución del PIB per cápita de España (PPA, dólares constantes 2017) desde 1990 hasta 2023. El eje Y utiliza una escala logarítmica, con etiquetas de $32,000 a $48,000. El eje X muestra el año. Una línea azul índigo con puntos traza la tendencia del PIB per cápita. Bandas verticales sombreadas de color rosa translúcido resaltan tres períodos de crisis económica: 1992-1994, 2008-2013 y 2020-2021. El gráfico tiene un fondo gris claro y utiliza la fuente Lato. Fuente: World Bank.

#Day23 #LogScale #dataviz

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

Miki :rstats: @miki_peltzer@techhub.social

#30DayChartChallenge Día 22: Stars! Planetas, planetas por todas partes! Gráfico del nº acumulado de exoplanetas confirmados (NASA Exoplanet Archive). #TimeseriesWeek #Astronomy

La curva muestra la explosión de descubrimientos desde los 90, ¡especialmente con Kepler & TESS! Ya superamos los miles de mundos conocidos.

*(Aclaración: El último punto de 2025 es el acumulado hasta hoy, 22 de Abril, no el año completo).*

Impresionante cómo avanza la ciencia que estudia los planetas de otras estrellas.

#rstats #ggplot2 | Data: NASA Exoplanet Archive | Theme: #theme_week4_social
Código: https://t.ly/bCzwJ

Gráfico de líneas que muestra el número acumulado de exoplanetas confirmados descubiertos, desde aproximadamente 1992 hasta mediados de abril de 2025. El eje X representa el año de descubrimiento y el eje Y el número acumulado de planetas. La línea, de color rosa, comienza cerca de cero y muestra un lento crecimiento inicial, seguido de una aceleración muy pronunciada alrededor de 2010 y otra después de 2018, alcanzando varios miles. El subtítulo aclara que el dato de 2025 es parcial hasta la fecha actual. El gráfico utiliza un tema claro con fondo gris pálido. Fuente: NASA Exoplanet Archive.

#Day22 #Stars #dataviz

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

Nicola Rennie @nrennie@fosstodon.org

For Day 22 of the #30DayChartChallenge and the prompt of "Stars", I found it quite tricky to make the income inequality data I've been working with fit the prompt.

Is it a radar chart?
Is it a star?
Is it a constellation?

I don't know whether I love it or hate it , but either way it's made with #RStats! For reference, the inner 'stars' represent 1%.

Radar chart showing income inequality for five regions, arranges to look like a star shape

#DataViz #ggplot2 #Day22

Di @didoesdigital@bne.social · 1d

Di @didoesdigital@bne.social

Down under, you can use the Southern Cross to tell time and direction.

Aboriginal Australians are among the world's first astronomers. The southern cross forms part of the Emu in the Sky, a dark constellation, which has featured in storytelling for thousands of years.

Find South Using the Stars
In the southern hemisphere, you can use the Southern Cross (aka “Crux”) and The Pointers constellations to find South.
The chart of the Southern Cross and The Pointers shows a line drawn from the highest star of the cross, Gracux, through the lowest star of the cross, Acrux, and another line drawn perpendicular from Alpha Centauri and Beta Centauri, and the two lines marked as roughly south, not far from where the South Pole is marked.
Chart: @DiDoesDigital

Check the Star Clock: Part 1
Imagine a 24-hour clock face. Around 1st April, Crux appears high in the sky at midnight. At the 20 hour mark, it's ~8pm.
The 24-hour clock face has 0 at the top, labelled (midnight), 6 to the right, 12 at the bottom, labelled (midday), and 18 to the left. The Southern Cross constellation is positioned at the 20 hour mark with repeating, fading copies of it clockwise indicating the direction of travel. The chart is labelled 1st April 8pm.
Chart: @DiDoesDigital

Check the Star Clock: Part 2
For each month after April, take 2 hours. At 1st May, if Crux is at the “20” mark, subtract 1 month × 2 hours to get 18: ~6pm.
The 24-hour clock face has 0 at the top, labelled, 6 to the right, 12 at the bottom, and 18 to the left. The Southern Cross constellation is positioned at the 20 hour mark. An arrow points anti-clockwise, labelled "Subtract 2 hours". The chart is labelled 1st May 6pm.
Chart: @DiDoesDigital

You Can Find South and the Time!
Around 1st June, when Crux appears at the “6” hour mark, subtract 2 months × 2 hours to get 2, which means it’s ~2am.
The 24-hour clock face has 0 at the top, labelled, 6 to the right, 12 at the bottom, and 18 to the left. The Southern Cross constellation is positioned at the 6 hour mark. An arrow points anti-clockwise, labelled "Subtract 4 hours". The chart is labelled 1st June 2am.
Chart: @DiDoesDigital

#30DayChartChallenge

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

Steven Ponce @sponce1@graphics.social

Time series visualization of Hubble Space Telescope observations from 2016-2024, showing the 10 most frequently observed celestial targets. Gold dots represent observations, with NGC104-WFC having the most consistent observations over time. M-51 shows a concentrated period of observations in 2017-2018, while NGC5139-WFC observations only begin around 2020.

**Nicola Rennie** @nrennie@fosstodon.org · 2d

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.

**Steven Ponce** @sponce1@graphics.social · 2d

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 · 3d

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 · 3d

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

**Steven Ponce** @sponce1@graphics.social · 3d

Steven Ponce @sponce1@graphics.social

A faceted area chart titled 'The Shifting Speeds of Urbanization (1960-2023)' showing annual percentage point changes in urban population for eight countries. Countries are arranged by their maximum rate of urbanization, with Japan exhibiting the highest spike (over 1.5%) around 2000, followed by China, which has sustained high rates, and Brazil, which experienced early high rates that gradually decreased. Nigeria, India, Egypt, the United States, and Germany show more moderate urbanization rates. Negative urbanization periods are indicated in orange for countries such as China, Egypt, and Germany.

Di @didoesdigital@bne.social · 4d

Di @didoesdigital@bne.social

One thing I enjoy about this social media chart challenge is that I can sneak in silly choices I wouldn't usually make.

Behold my gradient, lack of data point circles, and use of curvy lines!

I also don't have to spend time making it work at a bajillion screen sizes and ratios.

Carlton is a Densely Populated Suburb
Carlton, Melbourne is among Australia's most densely populated suburbs. And yet it boasts Victoria's 12th best offering of well-loved open spaces.¹

Line chart of Population density (persons/km²) shows in 2018: 11,453.8; 2019: 11,562.7: 2020: 11,472.5; 2021: 9,382.5; 2022: 9,928.5; and 2023: 11,753.5.

¹ Neighbourlytics (2024), 'Where are Australia's most well-loved open spaces?' https://www.neighbourlytics.com/post/where-are-australias-most-well-loved-open-spaces.
Data: Australian Bureau of Statistics (2024), Data by region, Region summary: Carlton https://dbr.abs.gov.au/region.html?lyr=sa2&rgn=206041117, accessed 19 April 2025.
Chart: @DiDoesDigital.

#30DayChartChallenge

**Steven Ponce** @sponce1@graphics.social · 4d

Steven Ponce @sponce1@graphics.social

A 2x2 grid showing 7-game rolling averages of 3-point shooting percentages for four NBA teams (Celtics, Suns, Thunder, and Timberwolves) during the 2023-2024 season. Each panel displays individual game data as gray dots with a colored trend line representing the rolling average.

**Steven Ponce** @sponce1@graphics.social · 5d

Steven Ponce @sponce1@graphics.social

Scatter plot showing the relationship between peak hour traffic volume (x-axis) and annual average daily traffic (y-axis) across California counties. Los Angeles (dark blue) and Orange County (medium blue) data points generally show higher traffic volumes than other Bay Area counties (light blue). A trend line indicates daily traffic is approximately 10.5 times peak hour volume, with points clustering closely along this line.

**Nicola Rennie** @nrennie@fosstodon.org · 6d

Nicola Rennie @nrennie@fosstodon.org

I'm a little bit behind on posting my #30DayChartChallenge plots - so here's Day 15 for the prompt of "Complicated"

Inverted the prompt and made an uncomplicated plot!
Trying the new #tidyplots package in #RStats
Appreciate the naturally clean chart aesthetics
Still works nicely with #ggplot2 extensions

Area chart showing percentage of income received by 1% of population.

#Day15 #DataViz

Continued thread

**Sam Van Horne, Ph.D.** @DataAngler@vis.social · 6d

Sam Van Horne, Ph.D. @DataAngler@vis.social

For day 2 of #30DayChartChallenge, I made an area chart in #rstats to show the increase in automobile crashes in Delaware (and the proportion in which alcohol was involved). It might be a stretch to call it a slope chart. :) #dataviz

Code: https://github.com/bardolater/30DayChartChallenge/blob/main/2025/02_Slope.r

This is an area chart that shows the number of fatality crashes in Delaware by whether alcohol was involved.

**Nicola Rennie** @nrennie@fosstodon.org · 6d

Nicola Rennie @nrennie@fosstodon.org

When I decided to visualise the same dataset on income inequality for all 30 prompts of the #30DayChartChallenge, I wasn't sure how I'd manage to make the prompt of "Birds" fit with that data... But I managed to shoehorn some data about birds in there!

The result? A "correlation is not causation" / "dual axes are bad" plot in the style of Spurious Correlations made with #RStats!

Dual axis line chart showing increasing wealth inequality and decreasing numbers of wild birds

#Day17 #DataViz

**Steven Ponce** @sponce1@graphics.social · 6d

Steven Ponce @sponce1@graphics.social

Bar chart titled "Birds of a Feather: How NBA's Bird Teams Show Identical Home Nest Advantage" comparing Hawks and Pelicans win percentages. Both teams win exactly 54.3% of home games (teal bars) and 45.7% of away games (red bars). Arrows connect the identical percentages between teams, highlighting the perfect mirroring of their home court advantage pattern.

**Tan G.** @tangandhara.bsky.social@bsky.brid.gy · Apr 17

Apr 17

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

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