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Yasar Sharfudeen

@newbie2k25.bsky.social

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 30 #30DayChartChallenge — Global Health Data Exchange Day Since 1990, global life expectancy has increased by 6 years. Beating enteric and lower respiratory infections contributed the most, while COVID-19 subtracted 1.6 years. Net gain: +6.0 years. Data: IHME GBD 2021 Tools: R + ggplot2

Horizontal waterfall chart on a dark navy background titled "How the world gained six years of life". Each row is a leading cause of death; each bar shows that cause's contribution to global life expectancy from 1990 to 2021, stacking cumulatively. Eight teal gain bars step right: enteric infections +1.12 years, lower respiratory infections +0.90, stroke +0.72, other communicable diseases +0.62, cancer +0.57, ischaemic heart disease +0.56, neonatal disorders +0.55, tuberculosis +0.52. A lavender bar rolls up ten smaller gains at +2.57 years. Two loss bars step back left: other pandemic-related mortality −0.52 (red) and COVID-19 −1.60 (orange). A final orange bar at the bottom shows the net change, +6.01 years, beside a large "+6.0 years of global life expectancy" callout. Source: IHME Global Burden of Disease Study 2021.

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 29 — Monochrome Each dot represents a living animal. 9 critically endangered land vertebrates remain—2,370 individuals worldwide. The two highlighted dots are Najin and Fatu, the last Northern white rhinos, both female, signifying functional extinction. #30DayChartChallenge #rstats

Monochrome dot grid on a dark navy background. Nine horizontal bands, one per species, each containing a number of small white dots equal to the surviving population. From top to bottom: Northern white rhino (2 dots, ringed in coral with an inline note "Both surviving rhinos are female · functional extinction is now arithmetic"), Vaquita (8), Hainan gibbon (37), Sumatran rhino (40), Javan rhino (50), Amur leopard (120), Cross River gorilla (250), Tapanuli orangutan (800), Mountain gorilla (1,063). Each band is labeled with a large bold count to the left. Title: "Every dot is one of them." Total individuals across all nine species: 2,370.

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 28 #30DayChartChallenge — Modeling Five futures, one planet Three demographic models project vastly different 2100s: UN (peak 10.3B in 2083), IHME (peak 9.7B in 2065, then decline), and Wittgenstein SSPs (9.2B to 13.1B). Data: UN WPP 2024 · IHME GBD 2020 · Wittgenstein SSPs Made with R + ggplot2

Line chart on a dark navy background titled "Five futures, one planet." World population from 1950 to 2100 in billions. A single white line shows observed history rising from 2.5 B in 1950 to 8.1 B in 2023. From 2023 the line splits into five projection paths: a coral UN WPP 2024 line peaking at 10.3 B around 2083 and ending at 10.2 B in 2100, surrounded by an 80 % and 95 % prediction-interval ribbon that fans outward; a blue IHME GBD 2020 line peaking earlier at 9.7 B in 2065 and falling to 8.8 B in 2100, with its own pale-blue 95 % uncertainty ribbon; three Wittgenstein SSP scenarios — green SSP1 declining to 8.0 B, yellow-green SSP2 ending at 9.9 B, and amber SSP3 still rising to 13.1 B at 2100. Each line is labelled at its 2100 endpoint with the model name and final value. The UN's 95 % prediction interval visibly brackets most of the disagreement between models. Sources noted at bottom: UN World Population Prospects 2024, IHME Vollset et al. 2020, and Wittgenstein Centre Data Explorer v3.

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 27 #30DayChartChallenge — Animation 1800-2024 energy sources: Biomass was dominant in 1800 (99%), overtaken by coal around 1900, and then by oil in 1965. 2024 levels: Oil 55k TWh, Coal 46k, Gas 41k. Solar data begins in 1983. Data: Our World in Data Tools: R + ggplot2 + gganimate

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 26 #30DayChartChallenge — Trend 175 years of global temperature data. 2024: +1.31°C above the 1951–1980 average. The uncertainty ribbon was ±0.17°C in the 1850s. Today it is ±0.03°C. Data: Berkeley Earth Land/Ocean Record Tools: R + ggplot2

Bar chart showing global surface temperature anomaly from 1850 to 2024, relative to the 1951–1980 average. Blue bars show cooler-than-average years, red bars show warmer years. A shaded uncertainty ribbon surrounds the bars, visibly wide in the 1800s and narrowing to almost nothing by 2024. A white LOESS trend line rises steeply after 1980. A dotted yellow line marks the +1.5°C Paris Agreement threshold. The 2024 value reaches +1.31°C. Chart title: A Warming Trend, Measured with Increasing Precision.

Surbhi Bhatia @surbhaai.bsky.social · 3mo ago

#Day24: South China Morning Post Theme Day इনடఇಅ/India, a living mosaic of diverse languages. Inspired by: multimedia.scmp.com/culture/arti... @scmpgraphics.bsky.social #30DayChartChallenge #dataviz

Surbhi Bhatia@surbhaai.bsky.social · 3mo ago

eep, that excludes others note was for myself while doing manual edits, forgot to move it out. Person: 0, Manual Figma Editing: 1, i guess #30DayChartChallenge

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 25 #30DayChartChallenge — Space 6,197 confirmed exoplanets. 5,498 plotted. The bright cluster shows where our instruments see easily. The dark void at lower-right — Earth-size planets with year-long orbits — is nearly invisible to us. Data: NASA Exoplanet Archive Tools: R + ggplot2 + hexbin

Hexbin plot of confirmed exoplanets by orbital period and radius, highlighting detection bias: bright clusters mark common discoveries, while small long-orbit planets are mostly missing from current observations.

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 24 #30DayChartChallenge — South China Morning Post Theme Day China’s high-speed rail network expanded from its first 350 km/h line in 2008 to 50,000 km by 2025. Source: China State Railway Group / official government data Tools: R + ggplot2 #RStats #China #HighSpeedRail

A line chart titled “China’s High-Speed Rail Boom” showing operating length increasing rapidly from 2008 to 2025, with milestones at 10,000 km in 2013, 20,000 km in 2016, 40,000 km in 2021, and 50,000 km in 2025.

Yasar Sharfudeen @newbie2k25.bsky.social · 3mo ago

Day 23 #30DayChartChallenge — Seasons Kyoto’s cherry blossom record (812–2024) shows spring arriving earlier, shifting full bloom from mid-April to a record-early March 26 in 2021. Data: sakura_bloom GitHub + JMA Tools: R + ggplot2 #RStats #CherryBlossom #Phenology #ClimateChange

Scatter plot of Kyoto bloom dates over 1,200 years, showing a strong modern shift toward earlier spring and a record-early bloom in 2021.

Heatmap of average bloom date by decade, showing that recent decades are earlier than most of the historical record.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 22 #30DayChartChallenge — New Tool Global sea level: −25 mm in 1993 → +85 mm in 2025. One relentless trend at +3.1 mm/year. First animated chart of the challenge — the line draws itself across 32 years. Data: NOAA Laboratory for Satellite Altimetry Tools: R + ggplot2 + gganimate

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 21 #30DayChartChallenge — Historical Pre-industrial Arctic Pb: 0.010 ng/g. 1950 peak: 0.42 ng/g — a 44× spike in 231 annual ice layers. Coal burning alone pushed it to 11× before leaded gasoline existed. Data: McConnell & Edwards 2008, PNAS Tools: R + ggplot2

A log-scale step chart showing annual lead concentration (ng/g ice) in the ACT2 Greenland ice core, 1772–2003. Four shaded bands mark the Pre-industrial, Coal Era, Leaded-Gasoline Era, and Phase-out epochs. A yellow step line shows annual values; an orange overlay shows the 10-year rolling mean. The 1950 peak (0.42 ng/g, 44× pre-industrial) is annotated with a white dot. A dashed grey line marks the 0.010 ng/g baseline. Six event markers note Tambora (1815), coal-burning onset (1860), tetraethyl lead (1923), Clean Air Act (1970), phaseout start (1976), and completion (1996).

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 20 #30DayChartChallenge — Global Change In 1975, solar cost $128/W and total global capacity was 0.54 MW. In 2024, $0.26/W and 1,852 GW. A 99.8% cost drop. . Data: OWID R + ggplot2

A connected scatter plot titled "Solar Got 99.8% Cheaper as the World Built More of It" showing solar PV module cost (y-axis, log scale, $0.20/W to $200/W) against global cumulative installed capacity (x-axis, log scale, 1 MW to 1 TW) for the years 1975–2024. Each year is a point colored on a purple-to-orange gradient, connected by an orange path. The trajectory runs from top-left (1975: $128.27/W, 0.54 MW, in purple) down to bottom-right (2024: $0.26/W, 1,852 GW, in orange) along a roughly straight line. A dashed gray reference line shows the canonical Swanson's Law slope of 20% cost reduction per doubling of capacity. Six year-points are labeled (1975, 1985, 1995, 2005, 2015, 2024). A white callout reads "Wright's Law (Swanson's Law for solar PV): every doubling of global solar capacity → ~20% cost decline. 1975 → 2024: Capacity ×3.4 million, Cost −99.8%." Drawn on a dark navy background.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 19 #30DayChartChallenge — Evolution 130 Mt in 1990. 460 Mt in 2019. Global plastic production has tripled in 29 years — and "plastic" is 8 different polymer families. PE alone = 24% of all production. Data: OWID (Geyer 2017)

A stacked area chart titled "Inside the Plastic Boom" showing global plastic production by polymer type, 1990 to 2019, in million tonnes per year. Eight colored bands stack from polyethylene (cyan, bottom, 110 Mt in 2019) up through PP, Other, Fibres, PVC, PET, PS, and PUR (pink, top, 18 Mt). Total grows from 130 Mt to 460 Mt — a 3.5× increase. Each polymer is labeled in a ranked column on the right (PE 24% at top → PUR 4% at bottom), connected to its band by a colored leader line. A white callout notes that PE alone is 24% of all plastic produced and only ~9% of plastic waste ever gets recycled.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 18 #30DayChartChallenge — UNICEF Data Day A map of childhood lead exposure: ~800M children worldwide have blood lead levels above 5 µg/dL — the threshold for cognitive harm. India alone accounts for 275M, 34% of the global total. Data: UNICEF & Pure Earth (IHME GBD 2017) R + ggplot2 + sf

A world choropleth map titled "275 Million Children in India Have Lead Poisoning." Countries are shaded by estimated number of children (0–19) with blood lead level above 5 µg/dL, from pale yellow (under 100K) through gold, orange, red-orange to deep red (over 50 million). India is the only country in the deepest-red bin. Most of Africa, South Asia and the Middle East are in mid-orange to red shades. North America, Europe and East Asia appear in lighter shades. A white callout marks India: "275 million children · 34% of global total."

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 17 #30DayChartChallenge — Remake 1880: -0.17°C. 2024: +1.28°C. 146 years of warming in one stripe per year. A take on Ed Hawkins' #ShowYourStripes — the top 10 hottest years all happened in the last 11. Data: NASA GISTEMP v4 R + ggplot2

A horizontal bar of 146 vertical stripes showing global mean temperature from 1880 to 2025. Stripes shift from cool blue on the left to deep red on the right. The 2024 stripe is the darkest red at +1.28°C above the 1951–1980 baseline — the warmest year on record. Three markers note the IPCC formation (1988), Paris Agreement (2015), and warmest year (2024).

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 16 #30DayChartChallenge — Causation 1989: 1.66 million ODP tonnes of ozone-destroying chemicals. 2021: 16,622. A 99% drop. The Montreal Protocol didn't just correlate — it CAUSED the reduction. One policy, one cliff. Data: UNEP via Our World in Data R + ggplot2 #DataViz #RStats

Area chart titled "The Policy That Saved the Ozone Layer" on dark background. Red filled area shows global ODS consumption from 1986 to 2021. A green dashed vertical line marks the Montreal Protocol signing in 1987. Consumption peaks at 1,662,589 ODP tonnes in 1989 then drops steeply — falling to 16,622 by 2021, a 99% reduction. Key annotations: peak value at 1989, latest value at 2021 with reduction percentage, and a label noting US EPA estimates of 280 million skin cancer cases prevented. X-axis shows years 1986-2021, y-axis shows consumption in thousand ODP tonnes. Data: UNEP Ozone Secretariat via Our World in Data.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 15 #30DayChartChallenge — Correlation r = -0.83. No single factor kills — PM2.5, unsafe water, and poor sanitation compound together. Composite environmental stress score vs life expectancy for 190 countries. Explains ~69% of the variation. Data: World Bank (2020) R + ggplot2 #DataViz #RStats

Scatter plot titled "Environmental Stress vs Life Expectancy" on dark background. X-axis shows composite stress score 0-80, y-axis shows life expectancy 50-85 years. 190 dots colored by continent with all country ISO3 codes labeled in white. A loess curve descends from upper-left to lower-right. European countries cluster at top-left with low stress and high life expectancy. African countries cluster at bottom-right with high stress and low life expectancy. Asian countries spread across the middle. USA sits below the trend at stress 3.2 but only 77 years. r = -0.83. Green label marks clean environment end, red label marks polluted end. Subtitle notes environmental stress explains 69% of variation. Data: World Bank 2020.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 14 #30DayChartChallenge — Trade 15 countries competed for the top 10 export spots over 24 years. Some rose, some fell. Bump chart: export rankings 2000–2023. Every line crossover = a shift in global trade power. Data: World Bank R + ggplot2 #DataViz #RStats

Bump chart titled "Shifting Trade Powers" on dark background. Colored lines track export rankings of nations within the top 10 from 2000 to 2023. Country flags and ISO codes label both sides. China's red line rises from rank 9 to 1. USA's blue line drops from 1 to 2. Japan's orange line falls from 3 to 7. Netherlands rises from 8 to 6. Singapore and India appear in the top 10 by 2023 while Canada, Italy, Hong Kong, and South Korea exit. X-axis shows every year 2000-2023 at 45 degrees. Y-axis ranks 1-10 reversed. Data: World Bank.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 13 #30DayChartChallenge — Ecosystems Madagascar: 2,894 threatened plant species. Indonesia: 212 threatened mammals. Both lead the world. Data: World Bank / IUCN Red List 2022 R + rphylopic #DataViz #RStats

Dot matrix chart titled "Biodiversity at Risk" on a dark background. Four columns show species categories with PhyloPic silhouette headers: a mammal in red, bird in blue, fish in purple, and plant in green. 15 rows show countries identified by flags, sorted by total threatened species. Dot size scales to count with white numbers above each dot. The Plants column dominates with the largest dots — Madagascar has 2,894 threatened plants (biggest dot), Ecuador 2,029, Mexico 1,426. Indonesia shows the largest mammal dot at 212. TOTAL column on right shows Madagascar leading at 3,191 and Papua New Guinea lowest at 862. Subtle horizontal lines separate each row. Data: World Bank / IUCN Red List 2022.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 12 #30DayChartChallenge — FlowingData Theme Day China generates 12 Mt of e-waste. It recycles 16%. Indonesia: 1.9 Mt, recycles 0%. Stacked bar chart showing the recycling gap for 25 countries — green = collected, red = dumped. Data: GEM 2024 (UNITAR/ITU) Built with R #DataViz #RStats

Horizontal stacked bar chart titled "The E-Waste Recycling Gap" on a dark background. 25 bars represent the top e-waste producing countries in 2022, sorted by total generation with China at top. Each bar is split: green portion shows formally collected and recycled e-waste, red shows uncollected. Country flags replace text labels on the y-axis. China's bar is longest at 12.07 Mt with 16% collection rate. USA shows 7.19 Mt with 56% collected. India shows 4.14 Mt with only 1% collected. Six countries show 0% collection: Indonesia, Iran, Egypt, Saudi Arabia, Pakistan, Philippines. France has the highest rate at 60%. Black text inside each bar shows the total Mt value. Collection rate percentages appear at bar ends in green (30%+) or red (<30%). Data: Global E-waste Monitor 2024 (UNITAR/ITU).

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 11 #30DayChartChallenge — Physical (follow-up) Norway: 26.8 kg of e-waste per person. China: 8.5 kg — despite being #1 in total. Same circle-packing chart, now sized by per capita generation. The story flips completely. Data: GEM 2024 (UNITAR/ITU) Built with R + packcircles #DataViz #RStats

Circle-packing chart titled "E-Waste Per Person: Who Throws Away the Most?" on a dark background. 50 bubbles represent countries with highest per capita e-waste generation in 2022, each containing the country's flag. Bubble area scales to kg per person. Norway (26.8 kg) and UK (24.5 kg) have the largest bubbles. European countries (green) dominate — Norway, UK, Switzerland, France, Denmark, Netherlands, Belgium all rank in the top 10. The US (21.3 kg), Japan (21.2 kg), and Germany (21.2 kg) appear as mid-sized bubbles. Numbers below each flag show the per capita value. Data: Global E-waste Monitor 2024 (UNITAR/ITU).

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 11 #30DayChartChallenge — Physical China: 12 Mt of e-waste per year. USA: 6.9 Mt. Together, nearly a third of the global total. Circle-packing chart of 50 — bubble area = millions of metric tons generated in 2022. Data: GEM 2024 (UNITAR/ITU) Built with R + packcircles #DataViz #RStats

Circle-packing chart titled "The Physical Weight of E-Waste" on a dark background. 50 bubbles represent the top e-waste producing countries in 2022, each containing the country's flag. Bubble area scales to millions of metric tons generated. China (12.07 Mt, red/pink) and USA (6.92 Mt, blue) are by far the largest, forming the centre of the cluster. India (4.14 Mt), Japan (2.64 Mt), and Brazil (2.44 Mt) are the next largest. Bubbles are color-coded by continent: Asia in pink-red, Americas in blue, Europe in green, Africa in amber, Oceania in purple. Numbers below each flag show the tonnage. A horizontal legend at the bottom identifies the five continent colors. Data source: Global E-waste Monitor 2024 (UNITAR/ITU).

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 10 #30DayChartChallenge — Pop Culture Breaking Bad peaked at the end. Game of Thrones crashed. The Simpsons is all over the place. A raincloud plot of episode ratings across 5 iconic TV shows. Data: TVMaze Tools: R + ggplot2 #DataViz #RStats

A raincloud plot on a dark background showing the distribution of TV episode ratings out of 10 for five shows: Breaking Bad, Succession, The Office, Game of Thrones, and The Simpsons. The y-axis lists each show with its total seasons and episode counts. Breaking Bad and Succession show tight, high-rated clusters between 8 and 10. Game of Thrones peaks high but shows a long tail of dots dropping down into the 5.0 range. The Simpsons displays the widest spread of ratings across the entire 5 to 10 scale.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 09 #30DayChartChallenge — Wealth In the US, the average adult has $551k, but the typical person has just $107k. A scatter plot comparing median vs. mean wealth across 164 countries. Data: UBS Global Wealth Databook 2023 Tools: R + ggplot2 #DataViz #RStats

A scatter plot titled "The Global Wealth Gap: Mean vs. Median" on a dark background. The x-axis shows median wealth per adult, and the y-axis shows mean wealth per adult, both in USD on logarithmic scales. Each dot represents one of 164 countries, color-coded by continent. A solid diagonal line represents "Absolute Equality" (1:1 ratio). Because no country is perfectly equal, all dots sit above this line. Additional dashed diagonal lines mark where mean wealth is 2x, 5x, or 10x higher than the median. Countries like Belgium, Australia, and Iceland cluster closer to the equality line, while countries like the United States, Russia, Brazil, and South Africa sit near the 5x and 10x lines, visually demonstrating extreme wealth concentration.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 08 #30DayChartChallenge — Circular Kerala gets 630mm in June alone. Rajasthan gets 478mm the entire year. Radial bar chart: 4 IMD subdivisions, monthly rainfall, SW vs NE monsoon contrast. Data: IMD via data.gov.in (1971–2017 avg) Built with R + ggplot2 #DataViz #RStats

Radial bar chart titled "When Does India Get Its Rain?" on a dark background. Twelve months arranged clockwise. Four colour-coded grouped bars per month show average rainfall for Assam & Meghalaya (purple), Kerala (green), Tamil Nadu (orange), Rajasthan (pink). Kerala's June bar is tallest at 630mm, followed by Assam & Meghalaya July at 516mm. Tamil Nadu peaks in November (179mm) during the northeast monsoon, contrasting with other regions peaking Jun–Aug. Rajasthan's bars are barely visible, peaking at 165mm in July. Green and red background wedges mark SW monsoon (Jun–Sep) and NE monsoon (Oct–Dec). Dashed reference rings at 100–600mm. Data: IMD 1971–2017 averages via data.gov.in.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

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

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.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

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

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.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 05 #30DayChartChallenge — Experimental Mind the gap. Indonesia: 58 pp gap between male and female smoking. France: just 3 pp. very different cultures. Data: WHO via World Population Review Built with R + ggplot2 #DataViz #RStats

Dumbbell chart titled "Mind the Gap: Who Smokes More?" showing the gender gap in smoking for 8 countries. Each row has two dots connected by a line — orange for male, pink for female. Indonesia has the widest gap at 58.2 percentage points with 59.3% male and 1.1% female. China follows at 42.7 pp. Germany has the smallest gap at 2.1 pp. France shows 3.2 pp with both genders smoking around 30%. Countries are sorted by gap size. Data from WHO, 2025.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 04 #30DayChartChallenge — Slope Who quit and who didn't? India cut tobacco use from 38.3% to 24.3% in 12 years. Indonesia went the other way — 36.2% to 38.2%. France? Barely moved. Data: World Bank Built with R + ggrepel #DataViz #RStats

Slope chart titled "Who Quit and Who Didn't?" comparing tobacco use prevalence in 2010 vs 2022 across 8 countries. Green lines show countries that decreased: India had the steepest drop from 38.3% to 24.3%, followed by Germany 28.5% to 21.3% and Australia 18.4% to 13.1%. A red line shows Indonesia increasing from 36.2% to 38.2%. Grey lines show Turkey, France, China, and USA with minimal change. Data from World Bank.

Yasar Sharfudeen @newbie2k25.bsky.social · 4mo ago

Day 03 #30DayChartChallenge — Mosaic How you die depends on where you live. In low-income countries, 50% of deaths are from infections. In high-income countries, heart disease (30%) and cancer (21%) dominate. Data: WHO Global Health Estimates 2021 Built with R + ggmosaic #DataViz #RStats

Mosaic chart titled "How You Die Depends on Where You Live" showing cause of death by World Bank income group for 2021. Four columns represent Low income, Lower-middle income, Upper-middle income, and High income countries, with width proportional to total deaths. In Low income, infectious diseases dominate at 50%. As income rises, cardiovascular diseases grow from 16% to 30% and cancers from 7% to 21%. Injuries decrease from 12% to 6%. Lower-middle income has the widest column at 24.1 million deaths. Data from WHO Global Health Estimates.