Graph showing the jump in Home Heating Oil cost in Northern Ireland

Northern Ireland uses a lot of Home Heating Oil. Prices have doubled since the start of the war with Iran.

Data is from the Consumer Council for Northern Ireland

#rstats #ggplot2

Can you please send me your links to your favorite resources regarding story telling using graphs and maps? #rstats #storyTelling #ggplot2 #rmaps

How likely is 'likely'?

#TidyTuesday, wk 10 is all about discovering the invisible prob scale in our head.

I built radial &🍩charts- 10 H2H matchups where phrases compete on perceived prob. 1 pair was shown twice in reverse, but no anchoring effect in selection was found

#Rstats #Dataviz #ggplot2

Timeline for Iranian Nuclear weapon Timeline for Iranian Nuclear weapon. GitHub Gist: instantly share code, notes, and snippets.

#ggplot2 #rstats code is at
gist.github.com/cavedave/5e1... and the days there has sources

A black background with white/gray text with the title "Likelihood of likelihood phrases: As judged by 5000+ people in Kucharski (2026)". The proportional fill of each word corresponds to its median %-likelihood, from "Will Happen" and "Almost Certain" which are completely filled, to "Remote Chance", "Highly Unlikely" And "Almost No Chance" which are all almost completely unfilled. Data: Kucharski (2026); Packages: {tidyverse, marquee}; Visualization: C. Börstell.

Going very minimal for this week's #TidyTuesday looking at judged likelihood of likelihood-expressing phrases in English.

Code: github.com/borstell/tid...

#R4DS #ggplot2 #DataViz

Mirrored bar plot showing the distribution of estimated probabilities for different terms, for US and UK respondents. Will happen is ranked most likely, and almost no chance is least likely. Points highlight the median which are identical for both groups except for may/might happen and highly likely where US medians are higher.

A really fun dataset for this week's #TidyTuesday, looking at how people interpret different probabilistic statements 📊

There's so many aspects of this to visualise, but I decided make some barcode plots comparing how people from the UK and US responded!

#DataViz #RStats #ggplot2

Dot plot showing how people interpret common probability phrases on a 0–100% likelihood scale. Phrases like “Almost No Chance” and “Highly Unlikely” appear near 0%, “About Even” sits around 50%, and phrases such as “Likely,” “Very Good Chance,” and “Highly Likely” cluster above 90%, with “Will Happen” interpreted as essentially certain. Several middle phrases—including “May Happen,” “Might Happen,” and “Could Happen”—are interpreted very similarly, highlighting ambiguity in everyday probability language

How likely is "likely"?

This week's #TidyTuesday explores probability phrases

"Likely" is more likely than "Probable", but "Likely" is less likely than a "Very Good Chance"

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

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
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Looking to visualize distributions clearly and effectively? The ggdensity package enhances ggplot2 in R by simplifying the creation of appealing density plots.

Example taken from the official package website: jamesotto852.github.io/ggdensity/

#tidyverse #datastructure #dataviz #rstats #ggplot2

If you regularly create plots in R, the tidyplots package is definitely worth exploring.

Today, I released a new module in the Statistics Globe Hub that explains how to use tidyplots in R.

More info about the Statistics Globe Hub: statisticsglobe.com/hub

#rstats #datascience #tidyplots #ggplot2

Horizontal interval with dots chart showing the probability ranges assigned by survey respondents to 19 vague probability expressions, from "Almost No Chance" to "Will Happen". For each row shows the 50%, 80%, and 95% intervals of responses alongside the median. Terms at the extremes show more tight agreement while middle terms like "Realistic Possibility" and "Might Happen" show wide variation across respondents. Data source: Kucharski AJ (2026) CAPphrase: Comparative and Absolute Probability phrase dataset. Available at DOI: 10.5281/zenodo.18750055

Very early #TidyTuesday for 2026 week 10 😂

Data: Cap-phrases, when someone says something is "likely", what probability do they
actually mean?

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

A horizontal range chart titled "Lost in the Middle" showing how 5,174 respondents assigned numerical probabilities (0–100%) to 19 common probability phrases. Each row displays a phrase ordered from lowest to highest median estimate. The thick bar represents the middle 50% of responses (IQR) and thin lines show the 10th–90th percentile range. Phrases at the extremes — such as "Will Happen" (median 100%), "Almost Certain" (95%), and "Almost No Chance" (2%) — show narrow, tightly clustered bars in gray, indicating strong agreement. In contrast, "Realistic Possibility" (highlighted in deep burgundy, IQR spanning 25–75%) and three semantically similar phrases — "Might Happen," "May Happen," and "Could Happen" (shown in muted rose, all with median 40%) — display wide bars indicating substantial disagreement. "About Even" stands out as a single gray dot with zero spread, the only phrase on which respondents achieved perfect consensus at 50%.

📊 #TidyTuesday – 2026 W10 | How likely is 'likely'?
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Raincloud plot, centred on 1

But since it's just a wrapper around existing #ggplot2 functions, you can override with `scale_x_continuous()` if you don't want that.

ggauto(plot_data$v1) +
scale_x_continuous()

Line chart with four coloured lines, with add symbols and direct labels for accessibility.

A new, work-in-progress #RStats package which

📊 Tries to automatically choose the best chart type from data types and values
🎨 Uses accessible colours, with added labels and shapes
📈 Has cleaner, more readable default #ggplot2 styling

in just one line

ggauto(df$v1, df$v2, df$v3)

#DataViz

Photo of Pulteney Bridge in Bath in the sunshine.

I had a lovely visit to Bath yesterday! ✈️

Thanks to SAMBa CDT for the invitation to give a seminar about data visualisation, followed by an interactive workshop on how to make better charts with #ggplot2 📊

#RStats #DataViz

`geom_rect()` in `coord_radial()` appearing as perfect rectangles Hello, I'm working on translating an old package from lattice to ggplot2. One function produces plots like this: Under-the-hood there's a lot of complex trigonometry going on as this plot is act...

forum.posit.co/t/geom-rect-... - any clever #ggplot2 #rstats users want to offer some assistance with this tricky problem? I say 'tricky' - it's probably either really easy if you know how or totally impossible. Help appreciated!

A faceted line chart showing the sex ratio of Hermann's tortoises across three locations (Beach, Konjsko, and Plateau) on Golem Grad island in North Macedonia from 2008 to 2023. The Plateau (right most) panel shows consistently high male bias, peaking around 93 males for every female around 2013 before declining to around 16 by 2023, but remaining heavily male-biased throughout. Beach (left most panel) shows moderate male bias with high variability. Konjsko (middle panel) mostly dips below the parity line into female-biased territory, shown by a shaded region. A dark dashed horizontal line marks the 1:1 sex ratio across all panels. The y-axis is on a log10 scale.

#TidyTuesday 2026 week 09

🐢 On Golem Grad island, Lake Prespa, male Hermann's tortoises have overwhelmed females for over 16 years.

code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

Last boost. That Kieran Healy data visualisation book is great. As is the BBC R cookbook. #ggplot2 #rstats

Zooming out - since 2006 we have seen this decrease uniformly across all boroughs. However, the rate of decrease in shooting incidents has been the slowest in the Bronx.
#NYC #DataViz #ggplot2 #rstats #art #data

NYC shooting incidents have not only corrected from the pandemic-era spike but are now trending below 2019 levels. 📉🗽 #NYC #DataViz #ggplot2 #rstats #art #data

We need a geom_snake(...)

#rstats #DataViz #ggplot2

Timeline for Iranian Nuclear weapon Timeline for Iranian Nuclear weapon. GitHub Gist: instantly share code, notes, and snippets.

#ggplot2 #rstats gist.github.com/cavedave/5e1...

ggplot2 extenders meetup questionnaire

Looking forward to hearing from James Otto - 'reflections on developing and teaching a #ggplot2 layer extension workshop'! Next week Tuesday at 3pm ET. 📊🚀 Join info in replies...

Title: Female tortoises were healthier in places with fewer males Subtitle: Average body condition of female and male Hermann's tortoises for three locations at Lake Prespa, North Macedonia: Beach (teal), Konjsko (yellow), and Plateau (brown) locations (Summer records only; no 2013 data). A higher score indicates a healthier animal. Circle size shows how many individuals were measured per timepoint. Notably, Plateau males consistently had the most records. Figure: Two horizontal line plots showing average body condition (y-axis) by year (x-axis), each with three lines corresponding to Beach (teal), Konjsko (yellow), and Plateau (brown) locations. The annual record counts is depicted as a circle overlaid on each line. Female body condition (upper plot) values for Konjsko are consistently higher than those for Beach and Plateau, demonstrated by the yellow line that is above and distinct from the teal and brown lines. Male body condition values for Konjsko lack this trend, demonstrated by the overlap between the lines for the three locations. The record count for males at Plateau are very large across all time points. Caption: Source: Sex Ratio Bias Triggers Demographic Suicide in a Dense Tortoise Population (Arsovski et al. 2026) | Graphic: [bluesky icon] morgangray [github icon] morethangray

A time series for #TidyTuesday showing annual trends in body condition for female and male tortoises

Code: github.com/morethangray...

#dataviz | #ggplot2 | #r4ds | #rstats

Announcement for ICPSR Summer Program's online workshop on Social Science Data and Model Visualization 101 using R, scheduled May 18-22.

New to #DataVisualization? Learn R, #Tidyverse, and #Ggplot2 from scratch! Create stunning plots, maps, and #ModelVisualization for papers, presentations, and the web. No experience needed—just bring your laptop. For more info: myumi.ch/8qdx3

#SumProg26 #ICPSR #RStats

A three-panel data visualization titled "Too Many Males, Too Few Females" examines Hermann's tortoise captures from Golem Grad island (Lake Prespa) over 16 years. Panel A shows a line chart of the percentage of female captures on the island from 2008 to 2023, consistently hovering near 5–10% — far below the 50% parity reference line. Panel B displays violin plots comparing the body condition index of mainland versus island females; mainland females (median: 8.1, n=976) show notably higher body condition than island females (median: 6.3, n=268). Panel C presents a dot plot of clutch size by locality; island females average 3 eggs per clutch (n=22) compared to a mainland average of 6 eggs per clutch (n=31). Together, the panels illustrate a cause-to-consequence chain: extreme male bias on the island is associated with poorer female body condition and reduced reproductive output.

📊 #TidyTuesday – 2026 W09 | Golem Grad Tortoise Data
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

This #TidyTuesday looks at Golem Grad Tortoise Data 🐢

On Golem Grad island, tortoises have become increasingly male-biased and females are showing declining body condition and reproductive output compared to the mainland population.

🔗 Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

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
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Graph of Timeline of Iranian nuclear

Iranian nuclear timeline over the last 35 years graph got removed from Reddit so here's a new version. Code and data in the same place #rstats #ggplot2 #iran

Timeline for Iranian Nuclear weapon Timeline for Iranian Nuclear weapon. GitHub Gist: instantly share code, notes, and snippets.

#ggplot2 #rstats code at
gist.github.com/cavedave/5e1... and the veg there has data sources