A two-panel chart titled "Uppsala emissions down" with the subtitle "Change in total and breakdown by sector." The upper panel is a line chart showing the percentage change in Uppsala's total greenhouse gas emissions relative to 1990, from 1990 to around 2023. Emissions rose slightly through the 2000s before falling sharply, reaching −44% by 2023. The lower panel is a stacked area chart showing absolute emissions in kilotons by sector over the same period. At its peak around 2010, total emissions were close to 1100 kt and almost halved by 2023 to 550 kt. Electricity and heat (orange) is the largest sector and has declined the most; Transport (indigo), Agriculture (grey), and Other (light grey) make up the rest. Source: Nationella emissionsdatabasen, SMHI.

#day7 of #30DayChartChallenge, Multiscale

code: github.com/gkaramanis/3...

#RStats #dataviz

Slide for #30DayChartChallenge 2026 Day 4 "Slope" and Day 6 Data Day "Reporters Without Borders". It has a screenshot of my Observable notebook with the title "Press Freedom Index 2020-2025" and a slope chart showing the evolution of the Press Freedom Index in 25 countries between the year 2020 and 2025. The countries names and the slope lines are coloured based on the continent: Africa in yellow, Americas in blue, Asia in red, Oceania in orange and Europe in green

#30DayChartChallenge 2026 #Day4 + #Day6: slope chart + Reporters Without Borders' Press Freedom Index
observablehq.com/@eleonore9/p...
#dataviz

Day 7 — #30DayChartChallenge

Wine quality dataset 🍷
Built a multiscale visualization:
• Macro → score distributions (red vs white)
• Meso → feature distributions by quality tiers
• Micro → feature behavior per score

🔗https://wine-quality-distribution.vercel.app/

#DataViz #D3js #DataStorytelling

Ausflugsziele und Ausflugstipps in Oberösterreich Die besten Ausflugstipps für Familien, Freunde, Paare und Gruppen. Infos über Sehenswürdigkeiten, Wanderwege, Touren, Wassertemperaturen, Schneeberichte, Wetter und Webcams.

#30DayChartChallenge #Day5
Comparisons: Experimental

Datenquelle: eigene Darstellung basierend auf oberösterreich.net und ausflugstipps.at

Tool: #RStats

I'm making QQ plots for day 7 of the #30DayChartChallenge: multiscale! Household incomes are higher than personal incomes, but usually not double... I threw in a log/log version as well, which is arguably best for showing the "bump" here. (But I don't really super love any of these...)

#30DayChartChallenge #Day7 – Multiscale: Interactive map of US agricultural land change from 2017-2022. Nearly every state saw a net loss in farmland over those 5 years. In the Shiny app, click any state to drill down to county-level data, where the story gets much more variable.
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#Day7 | Distributions – Multiscale | #30DayChartChallenge | Comparison of NDVI distributions across two spatial scales. Built with #RStats using #ggplot2, #dplyr, #terra, #tidyterra, #patchwork, #ggtext, and #scales.

Day 1, Part-to-Whole: Each bar in this graph shows the diverse population distributions of small neighbourhoods in the city region of Gmunden, which collectively contribute to mild segregation levels by country of origin, as measured by the multigroup H-Index. #30DayChartChallenge #Gmunden

Bivariate choropleth world map titled "Humanity in transition, Demographic Transition," subtitled "Life expectancy (y) × Total fertility rate (x), 2023." Countries are colored using a 3×3 biscale grid encoding two variables simultaneously: life expectancy at birth (y-axis) and total fertility rate (x-axis). Key regional patterns: sub-Saharan Africa is deep orange (low life expectancy, high fertility, 26.1% of countries); North America, Europe, and Australia are deep purple (high life expectancy, low fertility, 21.4%); central categories of mid life expectancy and mid fertility account for 15.0%. The Middle East and North Africa show mixed orange-brown tones; Russia and Central Asia display medium purple. Footer reads: "Data: World Population Prospects via {wpp2024} · #30DayChartChallenge 2026 · Day 7 · Multiscale · Ilya Kashnitsky @ikashnitsky.phd." [help.siteimprove](https://help.siteimprove.com/support/solutions/articles/80000863904-accessibility-image-alt-text-best-practices)

DAY 7 -- multiscale #30DayChartChallenge ⚖
Demographic Transition around the world 🌐
👩‍🎓 read more about Demographic Transition on @ourworldindata.org: ourworldindata.org/demographic-...
🔗 #rstats code: github.com/ikashnitsky/...
🧙‍♂️ pplx chat: www.perplexity.ai/search/day-7...

#30DayChartChallenge Day 7 - Multiscale

Looking at @boardgamegeek.com Board Games using two scales - the ranking within their category, and the ranking overall.

Code in #rstats: github.com/drjohnrussel...

Pair of 5×5 km raster maps of global Plasmodium falciparum incidence rates in 2024, where the same underlying data is classified using equal intervals (top) versus the Fisher algorithm (bottom) across five color classes from yellow to dark purple, demonstrating how the choice of classification method affects the perceived spatial distribution of malaria burden.

#Day7 of #30DayChartChallenge : Distribution - Multiscale

How different classifications can change the perception of Malaria distribution?

Code: github.com/rajodm/30Day...

#dataviz #rstats #tmap

A three-panel chart showing the distribution of renewable electricity share across countries in 2022 at three scales. Panel 1 is a histogram of the global distribution, revealing a bimodal shape with many countries clustered near 0–20% and another group near 100%, with a median of roughly 32%. Panel 2 shows half-eye density plots by World Bank income group — Low, Lower-middle, Upper-middle, and High — all with similar medians around 28–36%, but notably different spreads and shapes. Panel 3 zooms in on Upper-middle income countries as a dot plot, where individual-country variation is stark: South Africa sits near 0%, China and Turkey near 25–30%, and Brazil and Paraguay near 80–100%.

📊 #30DayChartChallenge 2026 – day 07
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Distributions | Multiscale
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

A three-panel chart titled "The long and short of India's borrowing costs," covering data from Jan 2007 to March 2026. The top panel shows 10-year and 1-year Indian government bond yields, with major global and domestic events annotated. The middle panel shows the term spread between long and short-term rates in basis points. The bottom stacked bar chart shows who lends to the Indian government, such as commercial banks, insurance cos, RBI. Source: Reserve Bank of India and Investing.com. Made for #30DayChartChallenge Day 7 prompt: Multiscale

#Day7: Multiscale
A look at ~20 years of government securities yields. Who lends to the Indian government and at what rates, reflects how investors price risk and uncertainty.
#30DayChartChallenge #dataviz

Gráfico de crestas de densidad (Ridgeline plot) titulado 'La Fractura Macro-Regional'. Compara la distribución de la renta neta por persona en España por zonas NUTS 1. La curva superior muestra el Total Nacional. Debajo, regiones como el Sur o Canarias muestran curvas estrechas y concentradas en rentas bajas (10.000€ - 15.000€), mientras que la Comunidad de Madrid presenta una curva muy aplanada que se extiende largamente hacia rentas superiores a 25.000€.

Día 7 del #30DayChartChallenge: Multiscale 💶. Renta neta en España (NUTS 1, 2023). Madrid es un caso aparte: su extrema dispersión y cola de rentas altas contrasta con la homogeneidad del Sur o el Noroeste. Datos AEAT/INE. #dataviz #rstats

Paris 2026 Elections , now on a map: #30DayChartChallenge After the arrondissement circle chart, this view makes the geographic split much clearer: where each bloc wins, and how the city is spatially structured at a glance.

Paris 2026 Elections , now on a map: #30DayChartChallenge

After the arrondissement circle chart, this view makes the geographic split much clearer: where each bloc wins, and how the city is spatially structured at a glance.

#Paris #Municipales2026 #DataViz #ElectionMap

To check if our short-term results hold over the long run, we looked at tiles exposed for ~15 yrs 🌊⏳. The patterns matched: calcifying algae vanish under low & extreme low pH 💀, calcification drops, and photosynthesis, respiration & nutrient uptake rise under extreme low pH ⚡🌱💨.
#30DayChartChallenge

My late submission for day 3 - Mosaic

100+ years of economic growth

Will try to get up to speed this week.

#30DayChartChallenge

Raincloud plot titled "Not All Carbon Footprints Are Equal" showing CO2 emissions per capita across 191 countries in 2023 on a log scale. Four rows by World Bank income group with density ridges. Dots colored by 5 regions: teal for Africa mostly in low income around 0.1 tonnes, purple for Americas, orange for Asia, blue for Europe across upper rows, red for Middle East at the far right of high income between 15 and 40 tonnes. Labeled countries include Burundi at 0.07, Nigeria, India, Brazil, China, Germany, United States, and Qatar at 40 tonnes. Data from Global Carbon Project.

Day 07 #30DayChartChallenge — Multiscale

Not all carbon footprints are equal.

Qatar: 40t CO2/person. Burundi: 0.07t. That's a 570x gap across 191 countries on a log scale.

Data: Global Carbon Project 2023
Built with R + ggridges

#DataViz #RStats #Climate

A heatmap showing RSF World Press Freedom Index rankings for 19 G20 countries from 2014 to 2025. Each cell displays the country's rank for that year, colored on a blue-to-red gradient where blue indicates a low rank (more press freedom) and red indicates a high rank (less press freedom). Countries are ordered from best average rank at the top to worst at the bottom. Germany, Canada, Australia, the United Kingdom, and France consistently appear in the top 10–45 range and show predominantly blue tiles throughout. The United States and Italy occupy the middle of the chart with purple tones, showing moderate rankings in the 40–80 range. Brazil, Indonesia, Mexico, India, Russia, Turkey, Saudi Arabia, and China form the bottom tier with deep red tiles, reflecting persistently high ranks above 100–170. Argentina shows notable improvement between 2021 and 2022, dropping from rank 68 to 29. China and Saudi Arabia remain among the lowest-ranked countries across all years.

Practicing my #Python with plotnine for the #30DayChartChallenge:

Day 6: Reporters Without Borders World Press Freedom Rankings for G20 Countries

A line chart titled "Evolution of freedom press index in France" showing the RSF score from 2013 to 2025. The chart is divided into two colored sections representing two presidencies: François Hollande in coral red (2013-2017) and Emmanuel Macron in dark blue (2018-2025). Circular portraits of each president are placed above their respective sections.

Day6 #30DayChartChallenge, today data on Reporters without borders, I showed the evolution of the freedom press index during Hollande and Macron presidencies

code here : github.com/MathieuGenu/...

#rstats #dataviz

Scatter plot titled "Do countries with more press freedom treat their animals better?" Flag icons represent 20 OECD-adjacent countries, positioned by animal welfare grade (B through E, left to right) on the x-axis and RSF press freedom score (0–100) on the y-axis. Countries with B grades — including the Netherlands, Sweden, Denmark, Switzerland, the UK, and Austria — cluster near the top with press freedom scores above 78. C-grade countries are more spread, with Germany and New Zealand near 80 but Italy at 68 and Mexico as a dramatic outlier at 45. D-grade countries range widely from Canada at 78 down to Turkey at 29, which sits alone near the bottom of the chart. Japan, the only E-grade country shown, scores 63 on press freedom. Spearman's rank correlation coefficient: 0.77. Sources: World Animal Protection Animal Protection Index; RSF World Press Freedom Index 2025.

Day 6 of #30DayChartChallenge 2026: Reporters Without Borders Data Day 🗞️🐾 Do countries with more press freedom treat their animals better? Largely, yes. nicolelily.github.io/press-freedo... Tech: @observablehq.com Plot + @svelte.dev
#DataViz #AnimalWelfare #PressFreedom

Day 6 #30DayChartChallenge | Data Day - Reporters Without Borders

I built an interactive world map 🌍 inspired by vintage tour posters, except this “tour” traces global incidents from the Global Terrorism Database.

🔗: reporters-without-borders.vercel.app

#DataViz #D3js #DataStorytelling

mosaic presentation of press freedom score across 2022 through 2025 based on the curated data set of Peter Baumgartner.

BRA-EUR contribution for Day 6 #30DayChartChallenge #Day6, we used a curated dataset to show the press freedom score per region across the years 2022 through 2025.

Horizontal bar chart titled "How Free Is the Press Where You Live?" showing the 2025 World Press Freedom Index for 16 countries. Bars are colored by zone: green for Good (Norway 92.3, Finland 87.2), blue for Satisfactory (Germany, UK, France, Australia in the 75-84 range), orange for Problematic (USA 65.5, Brazil 63.8, Japan 63.1), and red for Very Serious (India 32.9, Russia 24.6, Vietnam 19.7, Iran 16.2, China 14.8, North Korea 12.6, Eritrea 11.3). Each bar shows the country rank inside and score at the end. Data from Reporters Without Borders 2025.

Day 06 #30DayChartChallenge — Reporters Without Borders Data Day

How free is the press where you live?

Norway: 92.3. Eritrea: 11.3. Over half the world's population lives in red zones.

Data: RSF World Press Freedom Index 2025
Built with R + ggplot2

#DataViz #RStats #PressFreedom

Multiple slope charts showing mostly decreased net approval ratings for President Trump since the start of his second term, as polled to Americans by YouGov on January 28th, 2025 vs. March 30, 2026. Socio-demographic group include registered voters, female, male, republican, independent, democrat, under 30, 30-45, 45-65, 65+, high school or less, some college, college grad, postgrad, white, black, hispanic, other race. The sharpest inclines in disapproval, and vice versa steepest decreases in support, are found among independents, under 30 and 30-45, college grads, blacks, hispanics, and other races.

#30DayChartChallenge Day 4 : Slope

President #Trump 's job approval ratings on his second term, then and now, broken down by sociodemographics

#Rstats

Line chart of electoral bond donations by party from FY2018 to FY2024. BJP is highlighted in saffron while other parties are faded. BJP receives the highest funding every year with a major spike in FY2019 during the general election. Funding rises around election periods marked by vertical lines. A sharp spike for YSRCP appears in FY2020 due to Andhra Pradesh elections and timing of bond redemption. FY2024 shows only BJP data because other party data is incomplete after the scheme was struck down. Overall, funding is concentrated toward BJP and closely follows election cycles.

#Day6 of the #30DayChartChallenge: Reporters Without Borders(Data Day)
Indian election funding rises with elections. The patterns speak for themselves.
Built with #ggplot
#DataViz #india #election #funding

A vertical U.S. flag that is faded near the top, artistically representing an ongoing loss of press freedom. Two years are highlighted: 2020, and 2025, with global press freedom scores of 76.15 and 65.49, respectively.

Day 6 #30DayChartChallenge: Reporters Without Borders (Data Day)

Watercolor and ink on cold press paper.

#30DayChartChallenge Day6: Data day

Here is a database I would recommend:
#WorldBank's Global Shipping Traffic Density rasters:

datacatalog.worldbank.org/search/datas...

While this does not have most current shipping maps, it still highlights the concentration of shipping routes.

I’m a bit late to the party, but I still want to give this a shot and learn how to make charts. I’m visualizing the books I’ve read, I think it’s the first time I’ve actually managed to track something consistently. #30DayChartChallenge

Day 6 of the #30DayChartChallenge, but I'm sticking with my usual data... It's time for a Lorenz curve! The Gini I get is different from commonly reported ones because of inclusion criteria, I believe. The chart itself is spiritually like my day 2 one and technically like my day 4 one...