Trending

#makeovermonday

Latest posts tagged with #makeovermonday on Bluesky

Latest Top

Posts tagged #makeovermonday

Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 days ago
A gap chart showing U.S. and Saudi Arabia oil production in terawatt-hours from 1965 to 2024. The area between the two lines is shaded in two colors: warm gray before 2009, labeled "Production tracked closely," and steel blue after 2009, labeled "U.S. pulls ahead." A dashed vertical line marks the inflection point of the 2009 shale boom. The U.S. line ends at 10k TWh in 2024; Saudi Arabia at 5.9k TWh. An annotation notes that U.S. output is approximately 1.7 times that of Saudi Arabia. The chart illustrates a structural shift from decades of competitive parity to clear U.S. dominance following the shale revolution.

A gap chart showing U.S. and Saudi Arabia oil production in terawatt-hours from 1965 to 2024. The area between the two lines is shaded in two colors: warm gray before 2009, labeled "Production tracked closely," and steel blue after 2009, labeled "U.S. pulls ahead." A dashed vertical line marks the inflection point of the 2009 shale boom. The U.S. line ends at 10k TWh in 2024; Saudi Arabia at 5.9k TWh. An annotation notes that U.S. output is approximately 1.7 times that of Saudi Arabia. The chart illustrates a structural shift from decades of competitive parity to clear U.S. dominance following the shale revolution.

📊 #MakeoverMonday – 2026 W14 | Global Oil Production
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

5 1 0 0
Adam Korengold
Adam Korengold
@adamkorengold.bsky.social
3 days ago
Data visualization showing that 75% out of 1,150 workers polled (and 956 using AI) who said their AI-generated output was at least 80% value added

Data visualization showing that 75% out of 1,150 workers polled (and 956 using AI) who said their AI-generated output was at least 80% value added

I teach a graduate #dataviz class; every term, we do a #MakeoverMonday challenge. This week’s theme is #AI “workslop”. I recalculated the data to emphasize value-add and account better for the 17% of respondents who don’t use AI. You can learn more at makeovermonday.co.uk

1 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
1 week ago
A two-panel data visualization titled "Low-quality AI output is common — and it scales into millions in hidden cost." The left panel shows a horizontal stacked bar chart depicting the workforce workslop profile among AI-using workers (n ≈ 952). Segments are color-coded by severity: Heavy (7%, deep crimson), Regular (11%, burnt orange), Occasional (46%, amber), and None (36%, gray-blue). A bracket above the three non-zero segments highlights that 64% of AI-using workers send at least some workslop. The right panel presents a cost cascade in progressively larger typography: 2 hours per incident (average time to resolve), $186 per employee per month (invisible productivity tax), and $9 million per year for a 10,000-person organization (annual organizational cost). A footnote states that 17% of respondents who did not use AI at work are excluded. Source: BetterUp Labs and Stanford Social Media Lab survey of 1,150 U.S. desk workers, September 2025; Niederhoffer, Robichaux, and Hancock (2026), Harvard Business Review.

A two-panel data visualization titled "Low-quality AI output is common — and it scales into millions in hidden cost." The left panel shows a horizontal stacked bar chart depicting the workforce workslop profile among AI-using workers (n ≈ 952). Segments are color-coded by severity: Heavy (7%, deep crimson), Regular (11%, burnt orange), Occasional (46%, amber), and None (36%, gray-blue). A bracket above the three non-zero segments highlights that 64% of AI-using workers send at least some workslop. The right panel presents a cost cascade in progressively larger typography: 2 hours per incident (average time to resolve), $186 per employee per month (invisible productivity tax), and $9 million per year for a 10,000-person organization (annual organizational cost). A footnote states that 17% of respondents who did not use AI at work are excluded. Source: BetterUp Labs and Stanford Social Media Lab survey of 1,150 U.S. desk workers, September 2025; Niederhoffer, Robichaux, and Hancock (2026), Harvard Business Review.

📊 #MakeoverMonday – 2026 W13 | Why People Create AI "Workslop"
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

5 1 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 weeks ago
Line chart showing estimated unemployed counts (thousands) in London from 2019 to early 2026, using 4-quarter rolling averages. Two lines are plotted: 16+ overall unemployment (dark navy) and 16–24 youth unemployment (dark red). Both lines declined through 2022–2023 before rising sharply after late 2024. The overall 16+ count reached 390k, and the youth 16–24 count reached 125.4k in the most recent period, leaving a gap of 264.5k — the largest in that period. Two shaded vertical bands mark the October 2024 Budget announcement and the April 2025 implementation of NIC and minimum wage changes, both of which coincide with the acceleration in unemployment counts.

Line chart showing estimated unemployed counts (thousands) in London from 2019 to early 2026, using 4-quarter rolling averages. Two lines are plotted: 16+ overall unemployment (dark navy) and 16–24 youth unemployment (dark red). Both lines declined through 2022–2023 before rising sharply after late 2024. The overall 16+ count reached 390k, and the youth 16–24 count reached 125.4k in the most recent period, leaving a gap of 264.5k — the largest in that period. Two shaded vertical bands mark the October 2024 Budget announcement and the April 2025 implementation of NIC and minimum wage changes, both of which coincide with the acceleration in unemployment counts.

📊 #MakeoverMonday – 2026 W12 | London Estimated Unemployment Rates
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

4 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
3 weeks ago
A three-panel chart about the world's fastest cars. The top two panels are diverging bar charts showing deviation from the median in cost per horsepower (left) and speed per horsepower (right). Burgundy bars indicate worse value; steel blue indicates better value. The Bugatti Chiron SS 300+ is the most expensive per horsepower at +$1,151 above median, while the Rimac C Two and Hennessey models sit below median cost. The bottom panel is a waffle chart showing the total units produced for all eight cars. Each dark gray square represents one car built against a light gray grid of equal size, making rarity directly comparable. The Rimac C Two leads with 150 units; the Hennessey Venom GT is the rarest at just 13 units.

A three-panel chart about the world's fastest cars. The top two panels are diverging bar charts showing deviation from the median in cost per horsepower (left) and speed per horsepower (right). Burgundy bars indicate worse value; steel blue indicates better value. The Bugatti Chiron SS 300+ is the most expensive per horsepower at +$1,151 above median, while the Rimac C Two and Hennessey models sit below median cost. The bottom panel is a waffle chart showing the total units produced for all eight cars. Each dark gray square represents one car built against a light gray grid of equal size, making rarity directly comparable. The Rimac C Two leads with 150 units; the Hennessey Venom GT is the rarest at just 13 units.

📊 #MakeoverMonday – 2026 W11 | Fastest Cars in the World (2026)
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

4 1 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 weeks ago
A dot-and-segment chart showing 20 Mario game titles ranked by both estimated gross revenue (steel blue dot) and units sold (burgundy dot). Titles are sorted by revenue rank, from best to worst. For most top titles, the two dots sit close together, indicating the metrics agree. Three titles stand out with long burgundy segments extending to the right: Super Mario All-Stars, Super Mario Galaxy 2, and Super Mario World 2: Yoshi's Island — all ranked much lower in revenue than their unit sales would suggest. Annotations explain that re-releases and budget pricing drove high unit counts without proportional revenue.

A dot-and-segment chart showing 20 Mario game titles ranked by both estimated gross revenue (steel blue dot) and units sold (burgundy dot). Titles are sorted by revenue rank, from best to worst. For most top titles, the two dots sit close together, indicating the metrics agree. Three titles stand out with long burgundy segments extending to the right: Super Mario All-Stars, Super Mario Galaxy 2, and Super Mario World 2: Yoshi's Island — all ranked much lower in revenue than their unit sales would suggest. Annotations explain that re-releases and budget pricing drove high unit counts without proportional revenue.

📊 #MakeoverMonday – 2026 W10 | Mario Game Sales
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

2 1 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
1 month ago
A two-panel horizontal chart showing Trump's approval ratings across 13 demographic groups. The left panel displays the net change in percentage points from Late February 2025 to February 2026, with margin of error bars (±8.6 pp). Groups with statistically significant declines are shown in blue; those within the margin of error — Age 65+, Democrats, Age 50-64, and Republicans — are shown in gray. The largest declines occurred among Age 35-49 and Latino Americans (−19 pp each), followed by Independents (−15 pp, highlighted in orange). The right panel shows February 2026 approval ratings in the same row order, revealing that steep declines do not always correspond to low current approval: Republicans dropped only −8 pp but retain 82% approval, while Independents fell sharply to just 26%. Both panels use the same row ordering — steepest decline at top — enabling direct cross-panel comparison. Source: CNN/SSRS poll, February 17–20, 2026 (n=2,496; MoE ±8.6 pp).

A two-panel horizontal chart showing Trump's approval ratings across 13 demographic groups. The left panel displays the net change in percentage points from Late February 2025 to February 2026, with margin of error bars (±8.6 pp). Groups with statistically significant declines are shown in blue; those within the margin of error — Age 65+, Democrats, Age 50-64, and Republicans — are shown in gray. The largest declines occurred among Age 35-49 and Latino Americans (−19 pp each), followed by Independents (−15 pp, highlighted in orange). The right panel shows February 2026 approval ratings in the same row order, revealing that steep declines do not always correspond to low current approval: Republicans dropped only −8 pp but retain 82% approval, while Independents fell sharply to just 26%. Both panels use the same row ordering — steepest decline at top — enabling direct cross-panel comparison. Source: CNN/SSRS poll, February 17–20, 2026 (n=2,496; MoE ±8.6 pp).

📊 #MakeoverMonday – 2026 W09 | Trump's Approval Ratings
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

2 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
1 month ago
Horizontal lollipop chart showing total venture funding for 14 AI startup categories from February 2025 to February 2026, grouped by layer: Foundation, Horizontal AI, Vertical AI, and Emerging & Frontier. Foundation Models & LLMs dominate at $80B — more than all other 13 categories combined — averaging ~$5,333M per startup, 28 times the average of all other categories. AI Agents & Copilots is a distant second at $13B. Most categories received under $10B.

Horizontal lollipop chart showing total venture funding for 14 AI startup categories from February 2025 to February 2026, grouped by layer: Foundation, Horizontal AI, Vertical AI, and Emerging & Frontier. Foundation Models & LLMs dominate at $80B — more than all other 13 categories combined — averaging ~$5,333M per startup, 28 times the average of all other categories. AI Agents & Copilots is a distant second at $13B. Most categories received under $10B.

📊 #MakeoverMonday – 2026 W08 | Periodic Table of AI
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

4 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
1 month ago
A combined dashboard showing global declines in wildlife populations from the Living Planet Index (1970–2020). A horizontal bar chart ranks eight categories by the percentage of population lost, from Latin America & the Caribbean (95% lost) to Europe & Central Asia (35% lost), with a dashed line marking the 73% global average. Below, line charts show the global trend declining from 100% to 27%, with ecosystem breakdowns revealing freshwater species (15% remaining) declined far more steeply than terrestrial (31%) or marine (44%) systems.

A combined dashboard showing global declines in wildlife populations from the Living Planet Index (1970–2020). A horizontal bar chart ranks eight categories by the percentage of population lost, from Latin America & the Caribbean (95% lost) to Europe & Central Asia (35% lost), with a dashed line marking the 73% global average. Below, line charts show the global trend declining from 100% to 27%, with ecosystem breakdowns revealing freshwater species (15% remaining) declined far more steeply than terrestrial (31%) or marine (44%) systems.

📊 #MakeoverMonday – 2026 W07 | Living Planet Index
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

7 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
1 month ago
Diverging bar chart of Big Mac Index changes vs US dollar, 2000-2025. Top 15 currency shifts from Israel (-78pp) to Poland (+33pp). Twelve currencies weakened (orange), three strengthened (blue). Arrows indicate direction: more expensive (left) vs cheaper (right).

Diverging bar chart of Big Mac Index changes vs US dollar, 2000-2025. Top 15 currency shifts from Israel (-78pp) to Poland (+33pp). Twelve currencies weakened (orange), three strengthened (blue). Arrows indicate direction: more expensive (left) vs cheaper (right).

📊 #MakeoverMonday – 2026 W06 | Global Big Mac Index
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

0 0 0 0
Pizza Club Games
Pizza Club Games
@pizzaclubgames.bsky.social
1 month ago
A 2d art rendition of Wizard Vandar, a character from the upcoming game DOOMTRAIN. Describes in a text box: Wizard Vandar - Great. DemonCrop is forcing face scan verification... Just another step towards capitalism ruining all the realms.

A 2d art rendition of Wizard Vandar, a character from the upcoming game DOOMTRAIN. Describes in a text box: Wizard Vandar - Great. DemonCrop is forcing face scan verification... Just another step towards capitalism ruining all the realms.

Wishlist DOOMTRAIN! store.steampowered.com/app/3741180/...

Vandar got a #makeovermonday vibe from @mbirdini!

Pizza Club defends the rights of human artists and believes genAI is harmful to human livelihoods. NO #aiart!

#antiai #citybuilder #pcgaming #indiegame

81 24 3 2
Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 months ago
A two-panel line chart showing the relationship between gold prices and real interest rates from 2003 to 2025. The top panel shows gold prices, indexed to 2003 at 100, rising from 100 to approximately 850 over the period. The bottom panel shows the 10-year real interest rate fluctuating between -1 percent and 3 percent. Three periods of negative real rates are highlighted with shaded bands: 2008 during the financial crisis, 2011 to 2013 following the debt ceiling crisis, and 2020 to 2022 during the pandemic response. Each shaded period corresponds to significant increases in the gold price, illustrating that gold tends to rally when real interest rates fall below zero. Annotations mark the 2008 Financial Crisis, 2020 Pandemic Response, and 2024 Rally Resumes. The chart shows an inverse relationship between real interest rates and gold prices.

A two-panel line chart showing the relationship between gold prices and real interest rates from 2003 to 2025. The top panel shows gold prices, indexed to 2003 at 100, rising from 100 to approximately 850 over the period. The bottom panel shows the 10-year real interest rate fluctuating between -1 percent and 3 percent. Three periods of negative real rates are highlighted with shaded bands: 2008 during the financial crisis, 2011 to 2013 following the debt ceiling crisis, and 2020 to 2022 during the pandemic response. Each shaded period corresponds to significant increases in the gold price, illustrating that gold tends to rally when real interest rates fall below zero. Annotations mark the 2008 Financial Crisis, 2020 Pandemic Response, and 2024 Rally Resumes. The chart shows an inverse relationship between real interest rates and gold prices.

📊 #MakeoverMonday – 2026 W05 | Gold Prices
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

2 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 months ago
Three-panel dashboard showing America's most in-demand jobs. The left panel displays "49 out of 50 states have Registered Nurse as #1." The right panel shows #2 job distribution: Retail Sales (31 states), Trucking (10 states, amber), Physician (6 states), Software (2 states, purple), and RN (1 state). Bottom panel compares Software vs. Trucking states across three metrics: prevalence (2 vs. 10 states), average wage ($125k vs. $51k), and total employment (82,260 vs. 346,870 workers). Purple and amber highlight the Software-Trucking divide; other categories are shown in gray.

Three-panel dashboard showing America's most in-demand jobs. The left panel displays "49 out of 50 states have Registered Nurse as #1." The right panel shows #2 job distribution: Retail Sales (31 states), Trucking (10 states, amber), Physician (6 states), Software (2 states, purple), and RN (1 state). Bottom panel compares Software vs. Trucking states across three metrics: prevalence (2 vs. 10 states), average wage ($125k vs. $51k), and total employment (82,260 vs. 346,870 workers). Purple and amber highlight the Software-Trucking divide; other categories are shown in gray.

📊 #MakeoverMonday – 2026 W04 | Most Posted US Jobs by State
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

0 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 months ago
A two-panel visualization showing the growth of women+ conductors on Broadway. The left panel displays a cumulative area chart from 1954 to present, showing slow early growth that accelerates sharply after 2000, reaching approximately 100 unique conductors by 2024, with an annotation noting the 50th conductor was reached in 2000. The right panel shows horizontal stacked bar charts comparing role distribution across four eras: Pre-1980 was split between Leadership (49%) and Support (46%); 1980-1999 was dominated by Support roles (84%); 2000-2019 showed more balance with Support (50%), Leadership (26%), and Supplemental (23%); and 2020+ shows the most diverse mix with Supplemental (41%), Support (34%), and Leadership (25%). Sample sizes range from 39 positions pre-1980 to 102 in 2000-2019.

A two-panel visualization showing the growth of women+ conductors on Broadway. The left panel displays a cumulative area chart from 1954 to present, showing slow early growth that accelerates sharply after 2000, reaching approximately 100 unique conductors by 2024, with an annotation noting the 50th conductor was reached in 2000. The right panel shows horizontal stacked bar charts comparing role distribution across four eras: Pre-1980 was split between Leadership (49%) and Support (46%); 1980-1999 was dominated by Support roles (84%); 2000-2019 showed more balance with Support (50%), Leadership (26%), and Supplemental (23%); and 2020+ shows the most diverse mix with Supplemental (41%), Support (34%), and Leadership (25%). Sample sizes range from 39 positions pre-1980 to 102 in 2000-2019.

📊 #MakeoverMonday – 2026 W03 | Women+ Conductors on Broadway
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
2 months ago
Two-panel visualization comparing housing bubble risk trajectories for 9 global cities from 2020 to 2025. Left panel: Slope chart showing dramatic divergence—Toronto, Hong Kong, and Munich fell from 1.8 to below 1.0 (teal lines), while Miami rose from 0.5 to 1.7 (grey lines). Right panel: Scatter plot of current risk score versus 5-year change, with point size reflecting magnitude of change. Cooling markets cluster in the lower-left quadrant; Hong Kong shows the most significant decline (-1.3 points).

Two-panel visualization comparing housing bubble risk trajectories for 9 global cities from 2020 to 2025. Left panel: Slope chart showing dramatic divergence—Toronto, Hong Kong, and Munich fell from 1.8 to below 1.0 (teal lines), while Miami rose from 0.5 to 1.7 (grey lines). Right panel: Scatter plot of current risk score versus 5-year change, with point size reflecting magnitude of change. Cooling markets cluster in the lower-left quadrant; Hong Kong shows the most significant decline (-1.3 points).

📊 #MakeoverMonday – 2026 W02 | The Biggest Housing Bubble Risks Globally
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 0 0 0
GoatShipMate
GoatShipMate
@goatshipmate.bsky.social
2 months ago
Post image

Here's my #MakeoverMonday entry for this week, about the world's tallest rollercoasters. This was all done in #Excel

4 2 0 0
GoatShipMate
GoatShipMate
@goatshipmate.bsky.social
3 months ago
Post image

Listening to #TheAshes while finishing off my first #MakeoverMonday. This week it's the relationship between crime and deprivation in London. Done with #PowerBI.

2 0 2 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
3 months ago
Area chart showing estimated crime rates per 10,000 residents across London’s income deprivation deciles, from most deprived (1) to least deprived (10). Crime rates are much higher in more deprived areas: the most deprived decile has about 1,656 crimes per 10,000 residents, which is roughly 134% higher than the least deprived decile at about 707. Rates peak around the second and third-most deprived deciles and then decline steadily toward the least deprived areas. Rates are based on equal-population estimates per decile and should be interpreted comparatively rather than as exact values.

Area chart showing estimated crime rates per 10,000 residents across London’s income deprivation deciles, from most deprived (1) to least deprived (10). Crime rates are much higher in more deprived areas: the most deprived decile has about 1,656 crimes per 10,000 residents, which is roughly 134% higher than the least deprived decile at about 707. Rates peak around the second and third-most deprived deciles and then decline steadily toward the least deprived areas. Rates are based on equal-population estimates per decile and should be interpreted comparatively rather than as exact values.

📊 #MakeoverMonday – 2025 W49 | London crimes by income deprivation decile
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

6 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 months ago
Two-panel chart showing AI model performance. The top panel displays performance cards for the six best models (Claude 4.1 Opus, Claude 4.5 Sonnet, Grok 4, Magistral Medium 7.2, GPT-5 high, and Kimi K2 0905) with their accuracy, hallucination rates, and combined scores. The bottom panel shows a scatterplot of all 18 models, with accuracy on the x-axis and hallucination rate on the y-axis, and the top 6 models labeled and highlighted. Proprietary models (blue) cluster in the high accuracy, low hallucination zone, while open-weight models (coral) show more varied performance.

Two-panel chart showing AI model performance. The top panel displays performance cards for the six best models (Claude 4.1 Opus, Claude 4.5 Sonnet, Grok 4, Magistral Medium 7.2, GPT-5 high, and Kimi K2 0905) with their accuracy, hallucination rates, and combined scores. The bottom panel shows a scatterplot of all 18 models, with accuracy on the x-axis and hallucination rate on the y-axis, and the top 6 models labeled and highlighted. Proprietary models (blue) cluster in the high accuracy, low hallucination zone, while open-weight models (coral) show more varied performance.

📊 #MakeoverMonday – 2025 W48 | Which AI Models Hallucinate the Most?
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

1 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 months ago
Two-panel chart examining cat resting behavior. Left panel: Scatter plot comparing 28 cats' resting time in summer (x-axis) versus winter (y-axis), ranging from 55-90%. Points cluster along the diagonal 'no change' line, mostly within 70-80%, indicating minimal seasonal variation. Blue points represent indoor cats, orange points represent indoor & outdoor cats. Right panel: Dot plots showing resting time by household environment. Cats in homes with children rest slightly less (~70%) than those without (~75%), while dog presence shows minimal difference. Individual cats are shown as points with mean ± SE bars overlaid.

Two-panel chart examining cat resting behavior. Left panel: Scatter plot comparing 28 cats' resting time in summer (x-axis) versus winter (y-axis), ranging from 55-90%. Points cluster along the diagonal 'no change' line, mostly within 70-80%, indicating minimal seasonal variation. Blue points represent indoor cats, orange points represent indoor & outdoor cats. Right panel: Dot plots showing resting time by household environment. Cats in homes with children rest slightly less (~70%) than those without (~75%), while dog presence shows minimal difference. Individual cats are shown as points with mean ± SE bars overlaid.

📊 #MakeoverMonday – 2025 W47 | Do cats really loaf all day?
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

6 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 months ago
Horizontal bar chart showing Kids' June 2025 Streaming Top 10 programs by total minutes viewed. Netflix dominates with four programs (shown in red): Ginny & Georgia leads at 1.43 billion minutes, followed by Squid Game (837M), Stranger Things (617M), and Alvin! and the Chipmunks (466M). Other platforms in gray include Disney+ (Bluey - 895M, Phineas and Ferb - 748M), Paramount+ (SpongeBob - 801M), Hulu (Gumball - 626M), Max/Netflix (Young Sheldon - 533M), and Peacock (Love Island USA - 473M). Notable: Several Teen/Mature-rated shows rank highly despite a 6-17 age demographic.

Horizontal bar chart showing Kids' June 2025 Streaming Top 10 programs by total minutes viewed. Netflix dominates with four programs (shown in red): Ginny & Georgia leads at 1.43 billion minutes, followed by Squid Game (837M), Stranger Things (617M), and Alvin! and the Chipmunks (466M). Other platforms in gray include Disney+ (Bluey - 895M, Phineas and Ferb - 748M), Paramount+ (SpongeBob - 801M), Hulu (Gumball - 626M), Max/Netflix (Young Sheldon - 533M), and Peacock (Love Island USA - 473M). Notable: Several Teen/Mature-rated shows rank highly despite a 6-17 age demographic.

📊 #MakeoverMonday – 2025 W46 | School’s out and the TV’s on: What kids in the U.S. watched in June
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 0 0 0
Iris van Bussel
Iris van Bussel
@irisvanbussel.bsky.social
4 months ago
Post image

#datafam It has been a long time since I posted something but I'm back! For our own internal #ironviz competition I used the #MakeoverMonday dataset to visualize what % of Britons think kidult hobbies and practices are for children.

Check it out: shorturl.at/G5cfu!

2 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 months ago
Four-panel dashboard showing US domestic terrorism trends from 1994-2024. Panel A shows total annual attacks increased 53% from early period to recent years. Panel B displays per capita rates (attacks per million people) increased 26%, demonstrating that population growth explains half the raw increase. Panel C presents smoothed trend lines by ideology, with right-wing attacks (orange line) rising from ~8 attacks in mid-2000s to ~25 in recent years, while jihadist (teal) and left-wing (light blue) attacks remain lower. Panel D shows right-wing share of total attacks, typically 60-90%, with a notable dip to ~50% in the late 2000s. Key finding: while raw attack counts rose substantially, population-adjusted rates show a more modest increase, and right-wing attacks consistently dominate throughout the entire period.

Four-panel dashboard showing US domestic terrorism trends from 1994-2024. Panel A shows total annual attacks increased 53% from early period to recent years. Panel B displays per capita rates (attacks per million people) increased 26%, demonstrating that population growth explains half the raw increase. Panel C presents smoothed trend lines by ideology, with right-wing attacks (orange line) rising from ~8 attacks in mid-2000s to ~25 in recent years, while jihadist (teal) and left-wing (light blue) attacks remain lower. Panel D shows right-wing share of total attacks, typically 60-90%, with a notable dip to ~50% in the late 2000s. Key finding: while raw attack counts rose substantially, population-adjusted rates show a more modest increase, and right-wing attacks consistently dominate throughout the entire period.

📊 #MakeoverMonday – 2025 W45 | Terrorism and Political Violence in the United States: What the Data Tells Us
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

6 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
4 months ago
Four-panel dashboard showing the number of living WWII veterans by state in 2025. Panel A: diverging bar chart of veterans per 100k vs US mean, with New Hampshire highest at +29.1. Panel B: lollipop chart of the top 20 states, all above the national average of 13.7 per 100k. Panel C: histogram showing right-skewed distribution, with most states under 1,000 veterans and the top 5 states holding 37% of the total. Panel D: box plots by region showing that the Northeast has the highest concentration per capita, while the South has the most states but lower median rates.

Four-panel dashboard showing the number of living WWII veterans by state in 2025. Panel A: diverging bar chart of veterans per 100k vs US mean, with New Hampshire highest at +29.1. Panel B: lollipop chart of the top 20 states, all above the national average of 13.7 per 100k. Panel C: histogram showing right-skewed distribution, with most states under 1,000 veterans and the top 5 states holding 37% of the total. Panel D: box plots by region showing that the Northeast has the highest concentration per capita, while the South has the most states but lower median rates.

📊 #MakeoverMonday – 2025 W44 | WWII Veteran Statistics
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
5 months ago
Bar chart showing percentage-point differences between Biden and Trump supporters on cultural issues. Blue bars extend to the right for Biden's advantages (legacy of slavery +52pp, openness +51pp, gender identity +50pp). Red bars extend left for Trump advantages (gun ownership -63pp, criminal justice -41pp, marriage priority -40pp).

Bar chart showing percentage-point differences between Biden and Trump supporters on cultural issues. Blue bars extend to the right for Biden's advantages (legacy of slavery +52pp, openness +51pp, gender identity +50pp). Red bars extend left for Trump advantages (gun ownership -63pp, criminal justice -41pp, marriage priority -40pp).

📊 #MakeoverMonday – 2025 W42 | Cultural Issues and the 2024 Election
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

1 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
5 months ago
Data visualization with three panels analyzing the UCI Drug Consumption dataset (N=1,884). Panel 1: Line chart showing cannabis usage peaks at 80% for ages 18-24, declining with age. Panel 2: Stacked bar charts revealing polydrug patterns, with 18-24 males showing the highest 4+ drug use. Panel 3: Bar chart demonstrating conscientiousness as a protective factor, with a 10.4 percentage point reduction in heavy use between very low and very high conscientiousness groups among high-risk individuals.

Data visualization with three panels analyzing the UCI Drug Consumption dataset (N=1,884). Panel 1: Line chart showing cannabis usage peaks at 80% for ages 18-24, declining with age. Panel 2: Stacked bar charts revealing polydrug patterns, with 18-24 males showing the highest 4+ drug use. Panel 3: Bar chart demonstrating conscientiousness as a protective factor, with a 10.4 percentage point reduction in heavy use between very low and very high conscientiousness groups among high-risk individuals.

📊 #MakeoverMonday – 2025 W41 | Drug Consumptions (UCI)
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 0 0 0
Steven Ponce
Steven Ponce
@sponce1.bsky.social
5 months ago
Two-panel chart comparing recession impacts on prime-age labor force participation (ages 25-54). The left panel shows that the 2020 recession had the steepest drop at -3.3 percentage points, compared to -1.7pp in 2001 and -1.6pp in 2008. The right panel displays recovery trajectories over 60 months: 2020 recovered fastest, exceeding pre-recession levels by +0.6pp; 2008 never recovered, remaining -1.7pp below; 2001 shows a gradual decline to -1.1pp below baseline.

Two-panel chart comparing recession impacts on prime-age labor force participation (ages 25-54). The left panel shows that the 2020 recession had the steepest drop at -3.3 percentage points, compared to -1.7pp in 2001 and -1.6pp in 2008. The right panel displays recovery trajectories over 60 months: 2020 recovered fastest, exceeding pre-recession levels by +0.6pp; 2008 never recovered, remaining -1.7pp below; 2001 shows a gradual decline to -1.1pp below baseline.

📊 #MakeoverMonday – 2025 W40 | Better things come to those who wait
.
🔗: stevenponce.netlify.app/data_visuali...
.
#rstats | #DataFam | #dataviz | #ggplot2

3 1 0 0