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Steven Ponce

@sponce1.bsky.social

Website: https://stevenponce.netlify.app/ GitHub: http://github.com/poncest/ LinkedIn: http://linkedin.com/in/stevenponce/

592 Followers  |  884 Following  |  245 Posts  |  Joined: 04.02.2024  |  2.1525

Latest posts by sponce1.bsky.social on Bluesky

Bar chart comparing EuroLeague basketball success by country. Greece leads with 27 Final Four appearances and 10 titles, followed by Spain (12 appearances, 6 titles) and Turkey (12 appearances, 4 titles). Seven other countries have 1-4 Final Four appearances each. Greece's 37% title conversion rate is noted.

Bar chart comparing EuroLeague basketball success by country. Greece leads with 27 Final Four appearances and 10 titles, followed by Spain (12 appearances, 6 titles) and Turkey (12 appearances, 4 titles). Seven other countries have 1-4 Final Four appearances each. Greece's 37% title conversion rate is noted.

📊 #TidyTuesday – 2025 W40 | EuroLeague Basketball
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#rstats | #r4ds | #dataviz | #ggplot2

06.10.2025 00:18 — 👍 6    🔁 1    💬 0    📌 0
Arrow chart showing changes in non-profit funder support from 2020 to 2025. Health increased by 8% to 75%, while Arts & Culture grew by 23% to 43%. Education declined by 13% to 60%, Human Services fell by 5% to 55%, and Other dropped by 23% to 30%. A dashed vertical line marks the 2025 median at 55%.

Arrow chart showing changes in non-profit funder support from 2020 to 2025. Health increased by 8% to 75%, while Arts & Culture grew by 23% to 43%. Education declined by 13% to 60%, Human Services fell by 5% to 55%, and Other dropped by 23% to 30%. A dashed vertical line marks the 2025 median at 55%.

📊 SWD Challenge - OCT 2025: Avoiding the Spaghetti Graph
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Directional arrows replace tangled lines. Health surged, Education slipped.
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#SWDchallenge | #dataviz | #rstats | #ggplot2 | #DataStorytelling

01.10.2025 13:48 — 👍 3    🔁 0    💬 0    📌 0
Two-panel chart showing Lake Hornborgasjön crane observations from 1994 to 2024. The top panel displays seasonal patterns with spring migration (blue, peaking around 10,000 cranes in April) significantly higher than fall migration (orange, peaking around 9,000 in October). The bottom panel shows cumulative annual observations, colored from purple (1990s) to orange (2020s), demonstrating population growth from approximately 77,000 cranes in 1994 to over 427,000 in 2024, a more than threefold increase over 30 years.

Two-panel chart showing Lake Hornborgasjön crane observations from 1994 to 2024. The top panel displays seasonal patterns with spring migration (blue, peaking around 10,000 cranes in April) significantly higher than fall migration (orange, peaking around 9,000 in October). The bottom panel shows cumulative annual observations, colored from purple (1990s) to orange (2020s), demonstrating population growth from approximately 77,000 cranes in 1994 to over 427,000 in 2024, a more than threefold increase over 30 years.

📊 #TidyTuesday – 2025 W39 | Crane Observations at Lake Hornborgasjön, Sweden (1994–2024)
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#rstats | #r4ds | #dataviz | #ggplot2

29.09.2025 22:23 — 👍 8    🔁 0    💬 0    📌 0
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Thank you for catching the legend error - now fixed! You're absolutely right about share vs absolute spending inequality. The chart focuses on budget allocation patterns across income groups.

24.09.2025 17:44 — 👍 6    🔁 0    💬 0    📌 0
Two-panel chart showing UK household spending inequality by income in 2024. The left panel displays spending gaps between the poorest and wealthiest households across 11 categories, with housing showing the most significant gap at 19.6 percentage points. The right panel shows distribution patterns across all five income quintiles using ridge plots. Housing costs disproportionately burden poor households, while transport spending favors wealthy households.

Two-panel chart showing UK household spending inequality by income in 2024. The left panel displays spending gaps between the poorest and wealthiest households across 11 categories, with housing showing the most significant gap at 19.6 percentage points. The right panel shows distribution patterns across all five income quintiles using ridge plots. Housing costs disproportionately burden poor households, while transport spending favors wealthy households.

📊 #MakeoverMonday – 2025 W39 | Family spending in the UK
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#rstats | #DataFam | #dataviz | #ggplot2

24.09.2025 09:03 — 👍 23    🔁 1    💬 5    📌 0
Multi-panel visualization showing chess player activity and achievements from FIDE data (August-September 2025). The top panel displays four histograms of game activity levels, showing that most players are casual (1-3 games) while fewer are highly active (16+ games). Bottom left shows top 20 rating improvements, led by Plzak, David, with +362 points, with a median of 255.5. Bottom right shows countries with the most titled players, led by Germany (1,004), Spain (702), and Russia (469), with a median of 391 players.

Multi-panel visualization showing chess player activity and achievements from FIDE data (August-September 2025). The top panel displays four histograms of game activity levels, showing that most players are casual (1-3 games) while fewer are highly active (16+ games). Bottom left shows top 20 rating improvements, led by Plzak, David, with +362 points, with a median of 255.5. Bottom right shows countries with the most titled players, led by Germany (1,004), Spain (702), and Russia (469), with a median of 391 players.

📊 #TidyTuesday – 2025 W38 | FIDE Chess Player Ratings
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#rstats | #r4ds | #dataviz | #ggplot2

21.09.2025 15:48 — 👍 4    🔁 1    💬 0    📌 0
Preview
Which ‘kidult’ hobbies and practices do Britons think are really for children? | YouGov Most Britons see cartoons and Disney as being for children, but tend to see Doctor Who and theme parks as being as much for adults as kids

Unfortunately, the article does not specify which Disney films are considered childish. The data in the chart shared, and the corresponding YouGov article, only refers to "Disney Films" as a general category. You can find the full article here: yougov.co.uk/society/arti...

16.09.2025 17:05 — 👍 1    🔁 0    💬 0    📌 0
Diverging bar chart showing generational differences in viewing activities as childish. Comic Books have the largest gap at +28 percentage points, with older adults more likely to see it as childish. Most activities follow this pattern, but Star Wars (-3 pts) and Disney Films (-6 pts) diverge in the opposite direction, with younger people more likely to view them as childish than older adults.

Diverging bar chart showing generational differences in viewing activities as childish. Comic Books have the largest gap at +28 percentage points, with older adults more likely to see it as childish. Most activities follow this pattern, but Star Wars (-3 pts) and Disney Films (-6 pts) diverge in the opposite direction, with younger people more likely to view them as childish than older adults.

📊 #MakeoverMonday – 2025 W38 | Which ‘kidult’ hobbies do Britons think are for children?
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#rstats | #DataFam | #dataviz | #ggplot2

16.09.2025 11:23 — 👍 3    🔁 0    💬 1    📌 0
Heat map showing recipe ratings by complexity, with cooking time on y-axis (≤30 min to 4+ hours) and ingredient count on x-axis (1-5 to 20+ ingredients). Color scale from dark blue (4.45 rating) to light green (4.63 rating) reveals higher ratings for recipes with more ingredients and longer cooking times. Each cell shows the average rating and recipe count from 14,426 AllRecipes.com recipes.

Heat map showing recipe ratings by complexity, with cooking time on y-axis (≤30 min to 4+ hours) and ingredient count on x-axis (1-5 to 20+ ingredients). Color scale from dark blue (4.45 rating) to light green (4.63 rating) reveals higher ratings for recipes with more ingredients and longer cooking times. Each cell shows the average rating and recipe count from 14,426 AllRecipes.com recipes.

📊 #TidyTuesday – 2025 W37 | Allrecipes
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#rstats | #r4ds | #dataviz | #ggplot2

14.09.2025 15:12 — 👍 10    🔁 2    💬 2    📌 0
Two-panel chart showing AI's job market impact from 2024 to 2030. The top panel displays a slope chart with IT jobs growing from 18.3M to 18.8M (green line) and Transportation declining from 18.8M to 18.3M (red line), with other industries in gray background lines. Bottom panel shows waterfall chart demonstrating cumulative job changes by sector, starting with Transportation's -444K loss, followed by smaller losses in Education (-93K) and Manufacturing (-12K), then gains in Finance (+39K), Retail (+221K), Entertainment (+387K), Healthcare (+403K), and IT (+512K), resulting in net positive job creation. The chart indicates that the data is synthetic, modeling AI adoption scenarios.

Two-panel chart showing AI's job market impact from 2024 to 2030. The top panel displays a slope chart with IT jobs growing from 18.3M to 18.8M (green line) and Transportation declining from 18.8M to 18.3M (red line), with other industries in gray background lines. Bottom panel shows waterfall chart demonstrating cumulative job changes by sector, starting with Transportation's -444K loss, followed by smaller losses in Education (-93K) and Manufacturing (-12K), then gains in Finance (+39K), Retail (+221K), Entertainment (+387K), Healthcare (+403K), and IT (+512K), resulting in net positive job creation. The chart indicates that the data is synthetic, modeling AI adoption scenarios.

📊 #MakeoverMonday – 2025 W37 | AI Impact on Job Market: (2024–2030) | ⚠️ Synthetic data
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#rstats | #DataFam | #dataviz | #ggplot2

09.09.2025 21:08 — 👍 1    🔁 0    💬 0    📌 0
Two-panel chart showing global passport power inequality in 2024. Top panel: horizontal bar chart displaying regional differences from global median visa-free access, with Europe leading at +91 destinations above the median, followed by the Caribbean (+53) and the Americas (+44), while Africa (-36), Asia (-31), and the Middle East (-27) fall below the median. Bottom panel: stacked horizontal bar chart showing regional composition by performance tiers, revealing Europe has 29 Global Elite countries and 7 Strong Performers. In comparison, Africa is dominated by 43 Emerging Markets countries and 8 Restricted Access countries. Charts demonstrate Europe's passport dominance, alongside significant disadvantages in Africa, Asia, and the Middle East.

Two-panel chart showing global passport power inequality in 2024. Top panel: horizontal bar chart displaying regional differences from global median visa-free access, with Europe leading at +91 destinations above the median, followed by the Caribbean (+53) and the Americas (+44), while Africa (-36), Asia (-31), and the Middle East (-27) fall below the median. Bottom panel: stacked horizontal bar chart showing regional composition by performance tiers, revealing Europe has 29 Global Elite countries and 7 Strong Performers. In comparison, Africa is dominated by 43 Emerging Markets countries and 8 Restricted Access countries. Charts demonstrate Europe's passport dominance, alongside significant disadvantages in Africa, Asia, and the Middle East.

📊 #TidyTuesday – 2025 W36 | Henley Passport Index Data
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#rstats | #r4ds | #dataviz | #ggplot2

08.09.2025 22:14 — 👍 9    🔁 1    💬 0    📌 0
Two-part comparison showing traffic dashboard redesign. Top image: "Before" - cluttered interface with multiple tabs (Overview, Volume, Speed, Vehicles, Data), numerous sidebar controls, and scattered KPI boxes showing 396,077 total volume, 41.7 avg speed, 3.8% large vehicles. Multiple charts compete for attention across a tabbed layout. Bottom image: "After" - clean single-page design with streamlined sidebar (3 controls only), prominent KPIs at top (28,906 avg daily volume, 44 mph median speed, 3.8% large vehicles), and two focused charts: daily traffic volume trend and weekday vs weekend hourly profile comparison.

Two-part comparison showing traffic dashboard redesign. Top image: "Before" - cluttered interface with multiple tabs (Overview, Volume, Speed, Vehicles, Data), numerous sidebar controls, and scattered KPI boxes showing 396,077 total volume, 41.7 avg speed, 3.8% large vehicles. Multiple charts compete for attention across a tabbed layout. Bottom image: "After" - clean single-page design with streamlined sidebar (3 controls only), prominent KPIs at top (28,906 avg daily volume, 44 mph median speed, 3.8% large vehicles), and two focused charts: daily traffic volume trend and weekday vs weekend hourly profile comparison.

Post image

📊 #SWDchallenge – SEP 2025 | dashboards that deliver
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Transformed a cluttered traffic dashboard from 8 controls + 5 tabs into a focused single-page solution for transport planners.
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#SWDchallenge | #dataviz | #rstats | #shiny | #dashboarddesign

03.09.2025 22:31 — 👍 4    🔁 1    💬 1    📌 0
Horizontal diverging bar chart ranking UK drug harms. Alcohol leads with the highest total harm (72 points), showing massive social harm (46.1) but moderate user harm (25.8). Heroin and crack cocaine follow, with higher user harm but lower social impact. Chart reveals disconnect between legal status and evidence-based harm - legal drugs, alcohol, and tobacco rank high, while many illegal drugs like LSD and mushrooms show minimal harm scores. Based on a 2010 Lancet study using multicriteria analysis of 16 harm factors.

Horizontal diverging bar chart ranking UK drug harms. Alcohol leads with the highest total harm (72 points), showing massive social harm (46.1) but moderate user harm (25.8). Heroin and crack cocaine follow, with higher user harm but lower social impact. Chart reveals disconnect between legal status and evidence-based harm - legal drugs, alcohol, and tobacco rank high, while many illegal drugs like LSD and mushrooms show minimal harm scores. Based on a 2010 Lancet study using multicriteria analysis of 16 harm factors.

📊 #MakeoverMonday – 2025 W36 | Drug Harms in the UK
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#rstats | #DataFam | #dataviz | #ggplot2

02.09.2025 21:00 — 👍 4    🔁 1    💬 0    📌 0
Beeswarm plot showing seasonal patterns of frog calling in Australia. Each colored dot represents the recording frequency of a single species, grouped by season (from Summer to Spring) on the y-axis and on a log-scale on the x-axis. White circles mark seasonal medians. Spring shows the highest activity with 81 species and 60,405 recordings, while Winter shows the lowest with 78 species and 32,002 recordings. Colors distinguish five major frog subfamilies (Hylid, Microhylidae, Myobatrachid, Toad, and Unknown), revealing diverse seasonal preferences within these taxonomic groups.

Beeswarm plot showing seasonal patterns of frog calling in Australia. Each colored dot represents the recording frequency of a single species, grouped by season (from Summer to Spring) on the y-axis and on a log-scale on the x-axis. White circles mark seasonal medians. Spring shows the highest activity with 81 species and 60,405 recordings, while Winter shows the lowest with 78 species and 32,002 recordings. Colors distinguish five major frog subfamilies (Hylid, Microhylidae, Myobatrachid, Toad, and Unknown), revealing diverse seasonal preferences within these taxonomic groups.

📊 #TidyTuesday – 2025 W35 | Australian Frogs
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#rstats | #r4ds | #dataviz | #ggplot2

01.09.2025 00:14 — 👍 15    🔁 3    💬 1    📌 0
Two-panel chart analyzing global meat production. The top panel displays regional trends from 1961 to 2023, featuring line graphs for six regions (Asia, North America, Europe, South America, Africa, and Oceania). This reveals Asia's dramatic growth in pig and poultry production, reaching over 60 million tonnes, while the other regions remain relatively flat. The bottom panel displays 2010-2023 statistical confidence intervals using dot plots with error bars across four meat types (Poultry, Pig, Beef & Buffalo, and Sheep & Goat), confirming Asia's dominance in most categories, with North America leading in beef production. Color coding is consistent throughout: brown for beef, bright pink for pig, orange for poultry, and green for sheep.

Two-panel chart analyzing global meat production. The top panel displays regional trends from 1961 to 2023, featuring line graphs for six regions (Asia, North America, Europe, South America, Africa, and Oceania). This reveals Asia's dramatic growth in pig and poultry production, reaching over 60 million tonnes, while the other regions remain relatively flat. The bottom panel displays 2010-2023 statistical confidence intervals using dot plots with error bars across four meat types (Poultry, Pig, Beef & Buffalo, and Sheep & Goat), confirming Asia's dominance in most categories, with North America leading in beef production. Color coding is consistent throughout: brown for beef, bright pink for pig, orange for poultry, and green for sheep.

📊 #MakeoverMonday – 2025 W35 | Meat Production by Livestock Type
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#rstats | #DataFam | #dataviz | #ggplot2

26.08.2025 13:56 — 👍 5    🔁 2    💬 0    📌 0
Two-panel chart illustrating the evolution of Latin music on the Billboard charts from 1995 to 2022. The top timeline shows major hits, including "Despacito" (16 weeks) and "Macarena" (14 weeks), as peaks. Bottom-faceted bar charts display the top songs across five cultural waves: Dance Revolution (Macarena), Latin Pop Wave (Ricky Martin, Jennifer Lopez), Crossover Success (Santana), Streaming Era (Despacito), and Urban Takeover (Cardi B). Data demonstrates Latin music's progression from novelty hits to mainstream dominance.

Two-panel chart illustrating the evolution of Latin music on the Billboard charts from 1995 to 2022. The top timeline shows major hits, including "Despacito" (16 weeks) and "Macarena" (14 weeks), as peaks. Bottom-faceted bar charts display the top songs across five cultural waves: Dance Revolution (Macarena), Latin Pop Wave (Ricky Martin, Jennifer Lopez), Crossover Success (Santana), Streaming Era (Despacito), and Urban Takeover (Cardi B). Data demonstrates Latin music's progression from novelty hits to mainstream dominance.

📊 #TidyTuesday – 2025 W34 | Billboard Hot 100 Number Ones
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#rstats | #r4ds | #dataviz | #ggplot2

25.08.2025 23:45 — 👍 10    🔁 3    💬 0    📌 0
Two-panel chart showing UK unemployment volatility from 1975-2025. Top panel: Bar chart of year-over-year unemployment changes by decade, with orange bars showing rising unemployment and blue bars showing falling unemployment. The largest spikes occur during the 1980s Thatcher Era and the 2008 financial crisis. Bottom panel: Dumbbell chart showing unemployment ranges by decade, with the 1980s having the highest volatility (5.7% to 11.9% range) and the 1970s the most stable (3.4% to 5.7% range).

Two-panel chart showing UK unemployment volatility from 1975-2025. Top panel: Bar chart of year-over-year unemployment changes by decade, with orange bars showing rising unemployment and blue bars showing falling unemployment. The largest spikes occur during the 1980s Thatcher Era and the 2008 financial crisis. Bottom panel: Dumbbell chart showing unemployment ranges by decade, with the 1980s having the highest volatility (5.7% to 11.9% range) and the 1970s the most stable (3.4% to 5.7% range).

📊 #MakeoverMonday – 2025 W34 | UK Unemployment Rate
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#rstats | #DataFam | #dataviz | #ggplot2

19.08.2025 21:46 — 👍 8    🔁 1    💬 0    📌 0
Population pyramid showing height distribution of Scottish Munros vs Munro Tops in 2021. Green bars (right) represent 282 Munros, brown bars (left) represent 226 Munro Tops. The chart reveals that Munros dominate higher elevations above 1100m, while Munro Tops are more prevalent in lower ranges. The largest groups are found at elevations of 950-1000m (78 Munros, 73 Munro Tops) and 900-950m (68 Munros, 71 Munro Tops), indicating a relatively even distribution at lower elevations.

Population pyramid showing height distribution of Scottish Munros vs Munro Tops in 2021. Green bars (right) represent 282 Munros, brown bars (left) represent 226 Munro Tops. The chart reveals that Munros dominate higher elevations above 1100m, while Munro Tops are more prevalent in lower ranges. The largest groups are found at elevations of 950-1000m (78 Munros, 73 Munro Tops) and 900-950m (68 Munros, 71 Munro Tops), indicating a relatively even distribution at lower elevations.

📊 #TidyTuesday – 2025 W33 | Scottish Munros
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#rstats | #r4ds | #dataviz | #ggplot2

18.08.2025 22:00 — 👍 9    🔁 2    💬 0    📌 1
A faceted line chart illustrating the regional evolution of extreme weather attribution science from 2010 to 2024 across eight regions. Each panel displays two metrics: study volume (green solid/dashed lines) normalized within each region, and rapid attribution adoption rates (orange dashed lines). Europe leads with 163 studies, followed by Eastern/South-Eastern Asia (161) and North America (135). The chart reveals distinct regional patterns: some regions, such as North America, exhibit early and rapid adoption peaks around 2018-2020, while others, like Eastern Asia, display steady volume growth with late-emerging rapid adoption. Background shading indicates three development phases: Emergence (2010-2013), Growth (2014-2018), and Maturation (2019-2024). Thin lines represent annual data, while thick lines show 3-year moving averages that exclude partial years.

A faceted line chart illustrating the regional evolution of extreme weather attribution science from 2010 to 2024 across eight regions. Each panel displays two metrics: study volume (green solid/dashed lines) normalized within each region, and rapid attribution adoption rates (orange dashed lines). Europe leads with 163 studies, followed by Eastern/South-Eastern Asia (161) and North America (135). The chart reveals distinct regional patterns: some regions, such as North America, exhibit early and rapid adoption peaks around 2018-2020, while others, like Eastern Asia, display steady volume growth with late-emerging rapid adoption. Background shading indicates three development phases: Emergence (2010-2013), Growth (2014-2018), and Maturation (2019-2024). Thin lines represent annual data, while thick lines show 3-year moving averages that exclude partial years.

📊 #TidyTuesday – 2025 W32 | Extreme Weather Attribution Studies
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#rstats | #r4ds | #dataviz | #ggplot2

12.08.2025 22:04 — 👍 9    🔁 1    💬 1    📌 0
Two-panel chart showing global convergence in electricity access from 2000 to 2023. Top panel: The scatter plot reveals that countries with the lowest access (\<25%) achieved the greatest improvements (50-70+ percentage points), while countries starting near universal access showed minimal change. Bottom panel: The line chart shows the progress of five cohorts over time, with very low starters (red line) rising from ~15% to 50% access, demonstrating a catch-up effect. Data shows evidence of global convergence as access gaps narrow.

Two-panel chart showing global convergence in electricity access from 2000 to 2023. Top panel: The scatter plot reveals that countries with the lowest access (\<25%) achieved the greatest improvements (50-70+ percentage points), while countries starting near universal access showed minimal change. Bottom panel: The line chart shows the progress of five cohorts over time, with very low starters (red line) rising from ~15% to 50% access, demonstrating a catch-up effect. Data shows evidence of global convergence as access gaps narrow.

📊 #MakeoverMonday – 2025 W33 | Access to Electricity
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#rstats | #DataFam | #dataviz | #ggplot2

11.08.2025 22:48 — 👍 7    🔁 1    💬 0    📌 0
Two-panel chart showing corruption perceptions by income level. Left panel: beeswarm plot reveals higher-income countries (orange dots) cluster at better corruption scores (60-90), while lower-income countries (blue dots) spread across worse scores (10-60). Right panel: The dumbbell chart shows the largest changes from 2012 to 2024, with Seychelles leading at a +20-point improvement and Estonia at a +12-point improvement, demonstrating that countries across all income levels can achieve dramatic anti-corruption progress.

Two-panel chart showing corruption perceptions by income level. Left panel: beeswarm plot reveals higher-income countries (orange dots) cluster at better corruption scores (60-90), while lower-income countries (blue dots) spread across worse scores (10-60). Right panel: The dumbbell chart shows the largest changes from 2012 to 2024, with Seychelles leading at a +20-point improvement and Estonia at a +12-point improvement, demonstrating that countries across all income levels can achieve dramatic anti-corruption progress.

📊 #MakeoverMonday – 2025 W32 | Corruption Perceptions Index (CPI)
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#rstats | #DataFam | #dataviz | #ggplot2

05.08.2025 22:08 — 👍 11    🔁 2    💬 0    📌 0
Two-panel chart showing income inequality patterns. The left panel displays density curves of post-tax inequality by world region, with Latin America (highlighted in dark purple) exhibiting the highest levels of inequality. Right panel shows a horizontal bar chart of 15 countries with minimal redistribution effects, ordered from highest to lowest. The Dominican Republic (0.52 pre-tax Gini, 0.008 redistribution) and Brazil (0.59 pre-tax Gini, 0.079 redistribution) are highlighted in dark colors, demonstrating that Latin American countries have high market inequality but minimal government redistribution, compared to countries like Norway (0.39 pre-tax Gini, 0.152 redistribution.

Two-panel chart showing income inequality patterns. The left panel displays density curves of post-tax inequality by world region, with Latin America (highlighted in dark purple) exhibiting the highest levels of inequality. Right panel shows a horizontal bar chart of 15 countries with minimal redistribution effects, ordered from highest to lowest. The Dominican Republic (0.52 pre-tax Gini, 0.008 redistribution) and Brazil (0.59 pre-tax Gini, 0.079 redistribution) are highlighted in dark colors, demonstrating that Latin American countries have high market inequality but minimal government redistribution, compared to countries like Norway (0.39 pre-tax Gini, 0.152 redistribution.

📊 #TidyTuesday – 2025 W31 | Income Inequality Before and After Taxes
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#rstats | #r4ds | #dataviz | #ggplot2

05.08.2025 00:18 — 👍 14    🔁 2    💬 0    📌 0
Dual-panel chart showing European minimum wage changes 2020-2025. Left panel: dumbbell chart displaying growth percentages from 23% (Luxembourg) to 87% (Poland), with gray circles marking 2020 starting wages and colored dots showing 2025 endpoints. Right panel: horizontal bar chart showing current deviation from €1,686 EU average, with blue bars for countries above average (Luxembourg highest at +€952) and red bars for countries below average (Czechia lowest at -€860). Eastern European countries exhibit the highest growth rates but remain below the EU average.

Dual-panel chart showing European minimum wage changes 2020-2025. Left panel: dumbbell chart displaying growth percentages from 23% (Luxembourg) to 87% (Poland), with gray circles marking 2020 starting wages and colored dots showing 2025 endpoints. Right panel: horizontal bar chart showing current deviation from €1,686 EU average, with blue bars for countries above average (Luxembourg highest at +€952) and red bars for countries below average (Czechia lowest at -€860). Eastern European countries exhibit the highest growth rates but remain below the EU average.

📊 #MakeoverMonday – 2025 W31 | Wages Across Europe
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#rstats | #DataFam | #dataviz | #ggplot2

29.07.2025 17:02 — 👍 4    🔁 1    💬 0    📌 0

I have a base theme that I tweak if needed.

28.07.2025 22:16 — 👍 2    🔁 0    💬 0    📌 0
Netflix Content Viewing Velocity Analysis showing scatter plots of movies and shows with views per day (y-axis, log scale) versus days since release (x-axis, 0-365 days). Movies show a steeper velocity decline than shows over time. Key performance metrics indicate that movies have a higher mean velocity (154K vs. 86K views/day) but fewer total titles (346 vs. 904). Four velocity categories are color-coded, ranging from low (\<18K views/day) in dark red to very high (200K+ views/day) in gold, with Netflix trend lines in red indicating overall decay patterns.

Netflix Content Viewing Velocity Analysis showing scatter plots of movies and shows with views per day (y-axis, log scale) versus days since release (x-axis, 0-365 days). Movies show a steeper velocity decline than shows over time. Key performance metrics indicate that movies have a higher mean velocity (154K vs. 86K views/day) but fewer total titles (346 vs. 904). Four velocity categories are color-coded, ranging from low (\<18K views/day) in dark red to very high (200K+ views/day) in gold, with Netflix trend lines in red indicating overall decay patterns.

📊 #TidyTuesday – 2025 W3- | What have we been watching on Netflix?
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#rstats | #r4ds | #dataviz | #ggplot2

28.07.2025 20:58 — 👍 10    🔁 1    💬 1    📌 0
Two-panel chart showing London Underground temperature data from 2013-2023. Left panel: heatmap displaying monthly temperature patterns for 8 tube lines, with warm colors indicating higher temperatures. Right panel: ridgeline plot showing temperature distributions for each line. Both charts use unified warm color palette (dark brown for cool, golden yellow for hot temperatures). Bakerloo and Victoria lines show consistently highest temperatures (reaching 30°C+), while Sub-Surface lines remain coolest year-round (rarely exceeding 25°C). Summer months (June-August) show greatest temperature variation between lines.

Two-panel chart showing London Underground temperature data from 2013-2023. Left panel: heatmap displaying monthly temperature patterns for 8 tube lines, with warm colors indicating higher temperatures. Right panel: ridgeline plot showing temperature distributions for each line. Both charts use unified warm color palette (dark brown for cool, golden yellow for hot temperatures). Bakerloo and Victoria lines show consistently highest temperatures (reaching 30°C+), while Sub-Surface lines remain coolest year-round (rarely exceeding 25°C). Summer months (June-August) show greatest temperature variation between lines.

📊 #MakeoverMonday – 2025 W30 | London Underground Average Monthly Temperatures
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

23.07.2025 10:46 — 👍 14    🔁 1    💬 1    📌 0
Faceted line chart showing MTA art material usage from 1980-2020. Seven panels display trends for different materials: Mosaic & Tile shows steady usage with peaks around 1990 and 2014; Glass has dramatic spikes in 2002 and 2010; Steel & Iron emerge mainly after 2005; Bronze & Copper peaks around 1988; Ceramic, Paint & Pigments, and Stone show minimal, sporadic usage throughout the period.

Faceted line chart showing MTA art material usage from 1980-2020. Seven panels display trends for different materials: Mosaic & Tile shows steady usage with peaks around 1990 and 2014; Glass has dramatic spikes in 2002 and 2010; Steel & Iron emerge mainly after 2005; Bronze & Copper peaks around 1988; Ceramic, Paint & Pigments, and Stone show minimal, sporadic usage throughout the period.

📊 #TidyTuesday – 2025 W29 | MTA Permanent Art Catalog
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

22.07.2025 16:05 — 👍 13    🔁 2    💬 0    📌 0

Hi Darakhshan! Early mornings are my secret weapon - 30-45 minutes before work when my mind is clear. I batch the work by preparing ideas ahead, then creating during focused sessions. Key insight: treat it as a skill-building activity, not just a hobby. The time investment pays off professionally!

16.07.2025 16:26 — 👍 3    🔁 1    💬 1    📌 0

Between exploring the data, deciding which viz to go with, and polishing them, about 3 hours.

16.07.2025 12:31 — 👍 2    🔁 0    💬 1    📌 0
Two side-by-side charts showing projected employment impacts from mass deportations 2025-2029. Left chart: horizontal bar chart ranking top 20 states by total job losses, with California leading at 1.1M jobs, followed by Texas at 865K. Color coding indicates impact tiers, ranging from 500K+ jobs (red) to under 100K (gray). Right chart: Dumbbell plot showing the same 20 states, with blue and red dots representing job losses for U.S.-born and immigrant workers, respectively. This demonstrates that both worker types are affected in every state. Data source: Economic Policy Institute.

Two side-by-side charts showing projected employment impacts from mass deportations 2025-2029. Left chart: horizontal bar chart ranking top 20 states by total job losses, with California leading at 1.1M jobs, followed by Texas at 865K. Color coding indicates impact tiers, ranging from 500K+ jobs (red) to under 100K (gray). Right chart: Dumbbell plot showing the same 20 states, with blue and red dots representing job losses for U.S.-born and immigrant workers, respectively. This demonstrates that both worker types are affected in every state. Data source: Economic Policy Institute.

📊 #MakeoverMonday – 2025 W29 | Trump’s deportation agenda will destroy millions of jobs
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

15.07.2025 22:20 — 👍 6    🔁 2    💬 1    📌 0

@sponce1 is following 20 prominent accounts