Day 20 — Global change
In 1900, most children were out of school.
Today, it’s a small minority.
But not for everyone —
girls were left behind for decades.
Progress is real. Inequality too.
#30DayChartChallenge #DataViz #Education
9 line charts in a grid showing how the proportion of people living in extreme poverty has decreased in 8 world regions and the world population overall. From around 3 in 4 people in all regions in 1820 to 1 to 7 % in all regions except for Africa, South of the Sahara (36 %).
Remarkable progress has been made in reducing extreme poverty worldwide. Yet 1 in 10 people still live in extreme poverty.
#30DayChartChallenge | Day 20 - Global Change #dataviz
Line chart showing trends in overweight and stunting prevalence among children under 5 from 2000 to 2024, split horizontally into four panels by country income group (from left to right: high, upper middle, lower middle, and low income) and vertically into overweight on top and stunting below. Overweight prevalence (top row, grey) remain low across all income groups with a slight upward trend. Stunting prevalence (bottom row, red) are much higher in lower middle and low income countries but have declined substantially over the period. Each panel shows individual country lines in the background and a bold line representing the group average.
#30DayChartChallenge #Day18 UNICEF data
Malnutrition takes different forms across income levels. Among children under 5, stunting remains alarmingly high while overweight keeps slowly rising.
Code: github.com/rajodm/30Day...
#dataviz #rstats #ggplot2
Mapa de calor rectangular de anomalías térmicas globales por mes (columnas) y año (filas) desde 1880. La parte superior correspondiente a años antiguos es predominantemente cian, indicando temperaturas bajo la media. La franja inferior, correspondiente a las dos últimas décadas, es de un magenta oscuro continuo en todos los meses.
El tapiz del calentamiento global (1880-Pres). 🌍🔥 La matriz térmica revela un cambio de régimen: el azul del s.XIX ha sido devorado por un avance implacable del calor extremo. Las estaciones ya no importan, el exceso es sistémico. #30DayChartChallenge #Day20 #RStats
Gráfico de crestas (ridgeline plot) mostrando la curva de tipos de interés de Estados Unidos, apilando los años de 2000 a 2026 en el eje Y vertical. Se colorean de magenta intenso los periodos donde la curva de plazos se invirtió, señalando gráficamente las previas de las grandes crisis económicas.
El Reloj de la Recesión. ⏳ Evolución de la Curva de Tipos de EE.UU. (2000-2026). Las olas magentas revelan la inversión de la curva (tipos cortos > largos) previa a las grandes crisis: Punto-Com, 2008 y la era post-COVID. #30DayChartChallenge #Day19 #RStats #DataViz
The making of a chart :
from the first idea, to tweaking toe color palette, to fixing the labels, to translating the titles, to making a summary column...
#30DayChartChallenge #DataViz #OpenData #WileFire
I visualized France's 2024 wildfire data from the BDIFF national database.
Major conclusion: Before calling it a natural risk problem, it's worth looking at the human factor.
Tools: #Python & #Matplotlib
Data : from the BDIFF national database.
#30DayChartChallenge #DataViz #OpenData #WileFire
A dumbbell chart comparing under-five and neonatal mortality rates across four regions (Sub-Saharan Africa, World, South Asia, Latin America & Caribbean) between 1990 (open circle) and 2023 (filled circle). Both panels show sharp declines, but the neonatal panel reveals slower progress — illustrated by shorter dumbbells relative to starting values. A callout notes that neonatal deaths rose from 39% to 47% of all under-five deaths globally between 1990 and 2023, signaling a concentration of child mortality risk in the first 28 days of life. Rates are per 1,000 live births.
📊 #30DayChartChallenge 2026 – day 18
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Relationships | Data Day — UNICEF
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
#30daychartchallenge / day 18 / unicef
#day17 of #30DayChartChallenge, Remake
A better version of day 13: bsky.app/profile/kara...
code: github.com/gkaramanis/3...
#RStats #dataviz
The built-in R faithful dataset shows how longer eruptions drain the geyser’s reservoir, causing a longer wait for pressure to rebuild.
#30DayChartChallenge | Causation
#Rstats #Dataviz #ggplot2
the hex plot is awesome!
this is a nice viz! would be a good dataset to use for a linear regression class
The plot shows a scatter of OECD countries, with annual working hours on the x-axis and trust in others on the y-axis, illustrating a negative correlation between longer working hours and lower trust.
#Day15 of the #30DayChartChallenge: Correlation
In OECD countries, lower levels of trust correlate with longer working hours, yet do not lead to higher productivity.
#DataViz #Rstats
OECD Digital Education Outlook 2026 : a word frequency analysis: 676 hedges ("may", "could", "perhaps") vs 356 commits ("will", "must", "clearly") Hedge/Commit ratio: 1.90× Data : https://www.oecd.org/en/publications/oecd-digital-education-outlook-2026_062a7394-en.html Tools: #Python & #Matplotlib
OECD Digital Education Outlook 2026 : a word frequency analysis:
676 hedges ("may", "could", "perhaps") vs 356 commits ("will", "must", "clearly")
Hedge/Commit ratio: 1.90×
Data : www.oecd.org/en/publicati...
Tools: #Python & #Matplotlib
#30DayChartChallenge #DataViz #OpenData #OCDE
Gráfico de dispersión (scatter plot) que compara el ciclo del S&P 500 en el eje Y frente al ciclo de la Masa Monetaria M2 en el eje X, ambos calculados como desviación porcentual de su tendencia. Los puntos cian están completamente dispersos sin un patrón claro. Una línea punteada roja marca la nula correlación (R cuadrado = 0).
El fraude del doble eje Y desmontado. 🛑 Al aislar los ciclos reales de la Masa Monetaria (M2) y el S&P 500 extirpando su inercia temporal, la ilusión colapsa: R² = 0.000. La impresión de dinero no dicta a la bolsa. #30DayChartChallenge #Day17 #RStats #DataViz
A nice and simple way to visualise who held the title of "The World's Tallest Building" for how long. Source: buff.ly/rGdg6vo
Last chance! April 30th is the deadline (yes, only two weeks from today!) for submitting a paper to the @visualisingclimate.com summit. 📊 🇮🇹
Note: Uploading the FULL paper is required by that date!
Same submission deadline, April 30th, for a ‘talk only’ (without a paper)
👉 visualisingclimate.com 👈
#30DayChartChallenge Day 16 - Causation
A forest plot is typically used for meta-analyses to show effects across studies. Here, it shows the difference in rating for @boardgamegeek.com games with (treatment) and without (control) a mechanism.
Code in #rstats: github.com/drjohnrussel...
Day 13, Ecosystem: Exploring the housing ecosystem in Lower Austria: how do land prices relate to the share of single family houses in second-hand ownership at the municipal level? #30DayChartChallenge
Also: this took me 4 days trying to replicate the layout in Matplotlib… I went a bit nuts getting everything to fit, but it’s close enough :p
#30DayChartChallenge
Inspired by @manasseh6.bsky.social ’s dataviz focusing on iron repair data
link: bsky.app/profile/mana...)
thanks for the inspiration!
#30DayChartChallenge #DataScience #DataViz
Infographic titled “THE REPAIR CYCLE OF MODERN BRANDS.” For the top 10 brands in the Restart Project Open Repair Data (2024‑07), each row shows: brand logo/name and number of repairs with fixed share; a donut chart of repair outcomes (Fixed, Repairable, End of life, Unknown); a small density curve of product age at repair (0–25 years) with median (solid gold) and mean (dashed green) lines; and a semicircle gauge labeled “repairability” (0–100) based on fixed share. Footer cites the data source and notes the sample excludes “Unknown” brand values.
The Repair Cycle of Modern Brands 🔧♻️
Using Open Repair Data (Restart Project / Open Repair Alliance, 2024‑07):
outcomes (Fixed/Repairable/End of life/Unknown), age at repair, + a simple repairability score (fixed share).
#30DayChartChallenge #DataScience #DataViz
#30DayChartChallenge 2026 Day14: Trade.
The arcplot shows sand trade by top 5 importing and top 5 exporting countries.
In 2024 sand trade was around USD 2.5 billion. It is a raw material that is usually taken for granted but there is a shortage of it in quite some countries.
#30DayChartChallenge Day 11 : Physical
So, have you been skipping leg day? 🧐
Actually I suspect you've been skipping calves nearly half the time, as well as abs, and, yes, maybe some legs too, according to data by #Boostcamp as shown in this chart
#Rstats #workout
A heatmap titled "Cranial Spine Expression in Fishes." The x-axis shows fish families arranged by phylogenetic relatedness, with labels removed. The y-axis shows cranial spine positions grouped by region, with dashed red lines separating spine groups and labels showing only the first spine in each group. Cell brightness encodes the percentage of species per family expressing each spine, ranging from dark (0%) to white (100%), on a black background. A continuous legend runs along the bottom. Annotations in red explain the dashed group separators and that each row within a group represents a unique spine expression. Families arranged by phylogenetic relatedness reveal that some closely related families share similar expression profiles while neighboring families on the tree can differ substantially.
#30DayChartChallenge #Day15 – Correlation: Not your typical scatterplot showing correlation, but a heatmap showing the association between cranial spine expression and fish family.
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#DataViz #Rstats
#30DayChartChallenge Day 10 : Pop Culture
The Festival d'Été de Québec #FEQ2026 is coming next July and so here is the schedule broken down by music genre 🎸
#Rstats #QuebecCity
A scatter plot titled "A seasonal pattern" showing the relationship between average monthly temperature and Uppsala city bus ridership for 2024 and 2025. The x-axis runs from about −5°C to 20°C and the y-axis from roughly 1.2 million to 2.8 million monthly trips. A grey regression line slopes downward from upper left to lower right, indicating a negative correlation. Points are labeled with three-letter month abbreviations and colored by year: indigo for 2024 and amber for 2025. Cold winter months such as January and February cluster in the upper left with the highest ridership, while warm summer months, particularly July, which sits at around 20°C and below 1.5 million trips, fall at the lower right. The two years follow a very similar pattern. Source: UL Statistik and SMHI Open Data.
#day15 of #30DayChartChallenge, Correlation
code: github.com/gkaramanis/3...
#RStats #dataviz
Two combined plots, both show German cheese exports and imports by country (2025). Left: A network graph with arcs as edges arranged linearly. Right: A dot plot with the export and import figure for each country. Top export destinations are: Italy, Netherlands, France. Top import countries are: Netherlands, France, Denmark
Where does Germany get its cheese? 🧀
And where do they sell it to? 🧀
The network graph and the dot plot essentially show the same data. While the network is probably more playful, the dot plot reveals the results more accurately.
#30DayChartChallenge | Day 14 - Trade #dataviz
Day 14 — Trade
Where European students go to study.
This Sankey maps flows of tertiary students across the EU — from top sending countries to main destinations.
#30DayChartChallenge #DataViz #Education #EU
