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@instats.bsky.socialOct 9, 2026, 9:19 PM

New Instats livestreaming seminar: Introduction to Longitudinal Analysis & Multilevel Modeling #Business #Economics #DataScience #Statistics #ggplot2 #R #Tidyverse #Research #ResearchTraining #Instats

@ginareynolds.bsky.socialOct 9, 2026, 5:24 PM

#ggplot2 #rstats

@sponce1.bsky.socialOct 6, 2026, 8:49 PM

📊 #MakeoverMonday – 2026 W40 | Songs with Cowbell
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Horizontal bar chart titled "Most cowbell comes one song at a time." It groups the 654 songs on cowbellsongs.com's reader-submitted list by how many songs each artist has there, and each bar counts the songs that group contributes. The highlighted bottom bar shows that artists listed only once supply 337 songs, just over half the list, one per artist. Above it, gray bars show 9 songs from Kiss, 8 from The Beatles, 14 from The Allman Brothers Band and Rush, 24 from Guns N' Roses, Led Zeppelin, Queen and Van Halen, and 15 from The Donnas, Jimi Hendrix and Prince, then 64 songs from 16 artists with four each, 63 from 21 artists with three, and 120 from 60 artists with two. A note says all seven Allman Brothers songs were added in one July 2026 update. Source: cowbellsongs.com.
@sponce1.bsky.socialOct 5, 2026, 9:02 PM

📊 #TidyTuesday – 2026 W40 | Avocado Oil Authenticity
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Unit chart titled 23 of 27 tested avocado-oil products failed authenticity testing in both tested lots. Each circle is one product, split into two halves for its two separately purchased lots; burgundy halves failed and outlined beige halves passed, where failure means a fatty-acid and sterol profile inconsistent with authentic avocado oil. Chips: 12 of 14 failed both lots, and two had one failing lot each. Mayonnaise: 5 of 7 failed both; two products passed both lots and share label wording, size, price, and purchase channel. Salad dressing: all 6 failed both. A caption notes that the results do not show how much avocado oil was present, are not a food-safety verdict, describe only the tested products, and do not identify responsible suppliers. Data: Wang Lab, UC Davis.
@joachimschork.bsky.socialOct 5, 2026, 7:39 AM

Overplotting is a common challenge when visualizing data.

One interesting way to address this issue is by using blending techniques with the ggblend package in R.

I recently explored this in a Statistics Globe Hub module: statisticsglobe.com/hub

#RStats #ggplot2 #ggblend #DataScience #Statistics

@ansgarw.bsky.socialOct 3, 2026, 8:18 PM

Parliamentary seat share in the German Bundestag since 1949

Inspired by @economist.com's chart showing how the factions and parties evolved in Israel's Knesset.

#ggplot2 📊

A ribbon chart which shows the share of parliamentary seats of political factions in the German Bundestag. Each election year is marked on the vertical axis. Each party's seat share is connect with the next election year. The name of the chancellors are marked at the first legislative term of their time in office. The chart shows a lot of stability, esp. between 1961 to 1980, with only 3 factions in the Bundestag. The Greens joined in 1983, the PDF/Leftist Party in 1990 after the reunification. The right-wing extremist AfD entered in 2017. Since 2025, the Bundestag consists of 5 factions + 1 MP from the SSW.
@dataviz.arOct 3, 2026, 7:44 PM

I recently became a dad, which is the most wonderful experience I've... experienced!

But it's been no bed of roses 🎵 No pleasure cruise 🎶👑

It is no news that a baby disrupts parents' sleep. Well, I share my evidence of that with a #dataviz

Still worth it!!!💙👶

#ggplot2 #rstats

@daxkellie.bsky.socialOct 2, 2026, 4:25 AM

This thread is so great! 🤩

To avoid annoying resizing, figured I'd mention an alternative to {ggview}: {showtext} can resize all text to a specific dpi

Text is the part that I find I have to fiddle with most when saving #ggplot2 plots, so this usually works for me 😀

#rstats #dataviz

library(showtext) showtext_opts(dpi = 300) ggsave( plot = my_plot, file = "file_name.png", width = 6, height = 4, dpi = 300 )
@nrennie.bsky.socialOct 1, 2026, 10:17 AM

If you missed today's @rinpharma.bsky.social workshop, here are a few of my favourite "quick fixes" for some common #ggplot2 issues 📊

1. Use {ggview} to preview plots at the same size and resolution as you want to save them with ggsave() to avoid the annoying resizing

#RStats #DataViz

library(ggview) ggplot(mtcars, aes(wt, mpg)) + geom_point() + canvas( width = 6, height = 4, units = "in", dpi = 300 )
@simongreenhill.bsky.socialSep 30, 2026, 8:10 PM

why are there 100s of #Rstats packages providing colors for #ggplot2, and only 2 or 3 broken ones providing themes?

I'm getting sick of theme_classic() and theme_bw() and want something new.

@sponce1.bsky.socialSep 29, 2026, 10:02 AM

📊 #MakeoverMonday – 2026 W39 | World Population
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Waffle chart titled "Growth Slowed. The Yearly Additions Didn't. Where They Happen Did." From 1981 to 2020, the world added roughly 83–90 million people a year even as the growth rate fell from 1.8% to 1.1%, while Sub-Saharan Africa's contribution rose from 12 million to 29 million a year and East Asia & Pacific's fell from 25 million to 14 million. Thirteen grids show five-year windows from 1961–65 to 2021–25; each square is 1 million people of net population growth per year, ochre for Sub-Saharan Africa, teal for East Asia & Pacific, gray for the rest of the world. Gaps separate Before (1961–80), The Plateau (1981–2020), and Possible Break (2021–25). Sub-Saharan Africa overtook East Asia & Pacific in 2001–05. The final grid was smaller: 72 million a year, with East Asia & Pacific contributing 4 million. Source: World Bank, World Development Indicators.
@nrennie.bsky.socialSep 29, 2026, 8:11 AM

This week's #TidyTuesday data looks at health metrics in cities across the world 🏥 I created a dot density plot of the population density around hospitals 📊

Thanks to @gabspalomo.bsky.social for curating this week's dataset!

#RStats #DataViz #ggplot2

Four-panel dot density chart comparing the median population living within 1 km of a hospital in 2025 across World Bank income groups. Each dot represents approximately 200 people. Low-income countries have 50,000 people, lower-middle-income countries 53,000, upper-middle-income countries 44,000, and high-income countries 28,000. The chart’s main message is that higher-income countries tend to have lower population density around hospitals.
@sponce1.bsky.socialSep 28, 2026, 2:52 PM

📊 #TidyTuesday – 2026 W39 | Health metrics in urban centres worldwide
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Faceted scatter plot titled "Income looks like the divide. City density explains most of the gap." Four panels, one per World Bank income group, plot the share of residents within 1 km of a hospital against population density (log scale) for 6,427 urban centers. A large dot marks each group's typical city: 23% within 1 km in high-income countries, 33% in upper-middle, 36% in lower-middle, and 36% in low-income, even though high-income cities have about twice as many hospitals per person as low-income cities. The typical cities sit at rising densities, from about 3,000 people per km² (high income) to about 7,300 (low income), and all four lie close to one dashed curve showing the access–density relationship for all cities. Density strips above each panel show where cities fall on the density axis; cities without hospital data lean denser in low-income countries. Source: GHS Urban Centre Database R2024A, European Commission JRC.
@rajodm.bsky.socialSep 24, 2026, 6:52 PM

re-up because the previous version was not easy to read (or misleading? check the gh repo)

#TidyTuesday 2026 week 38

Overall, African cities have seen their green space quietly disappear

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

#dataviz #rstats #ggplot2

Histogram showing African cities' green space share falling across four decades (1990–2020); nearly all cities cluster under 5% green space, and by 2020 the median was just 4.12%, the lowest of the four decades.
@nrennie.bsky.socialSep 23, 2026, 8:44 AM

We're exploring green spaces for #TidyTuesday this week! 🌳

A simple small multiple bar chart showing changes in (some) UK cities over time 📊 Impressive increase from Stoke!

(Obviously I felt the need to style the bars as a mowed lawn pattern)

#RStats #DataViz #ggplot2

Small-multiple bar chart showing the average share of green area in 18 UK cities in 1990, 2000, 2010 and 2020. Stoke-on-Trent had the largest increase, from 8% in 2000 to 26% in 2020, while Dundee remained lowest, falling from 7% in 1990–2010 to 6% in 2020. Overall, green-space shares increased in several cities, while others were stable or declined.
@nrennie.bsky.socialSep 22, 2026, 3:04 PM

A question for the #RStats and #ggplot2 hivemind:

What things do you find most tricky or frustrating in ggplot2?

It might be things like specific chart types, aspects of styling, your workflow, saving charts etc.

@cborstell.bsky.socialSep 22, 2026, 2:42 PM

Urban Green Areas in Scandinavian capitals for this week's #TidyTuesday
🇩🇰🇳🇴🇸🇪

Stockholm started high in 1990 but has since dropped the most. City expansion, construction, definition of city limits, something else or all of the above?
#DataViz #ggplot2

Link: github.com/borstell/tid...

A small-multiples plot of the "Proportion of green areas across Scandinavian capitals: Copenhagen, Oslo and Stockholm". Each city has four panels - 1990, 2000, 2010, 2020 - showing a square of scattered green tiles representing the proportion of green areas. Below each square is an power bar-type plot showing each year's fill compared to the maximum across the 1990-2020 span, and next to it is a text label showing the percent change from the previous decade. While Oslo has had a quite stable level, Copenhagen lost a lot of relative green space in 2010, and Stockholm has been decreasing steadily and quite dramatically from the overall highest proportion in 1990, about half the proportion of 1990 in 2020. Data: UN Habitat Urban Indicators Dataset via TidyTuesday; Packages: {tidyverse, ggh4x}; Visualization: C. Börstell
@sponce1.bsky.socialSep 22, 2026, 12:40 AM

📊 #TidyTuesday – 2026 W3 | Average share of green areas across cities (UN-Habitat Urban Indicators)
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2

Beeswarm chart titled The High End of Estimated Urban Green Area Came Down. Four stacked strips for 1990, 2000, 2010, and 2020 place about 1,110 cities by estimated green area share on a 0 to 80% axis, with a dashed line at 40%. None of the 83 cities estimated at 40% or more in 1990, drawn in burgundy in every strip, were still at 40% or more in 2020, yet 73% stayed in the top quarter of cities. The 83 ran from 40% to 77% in 1990, 23% to 68% in 2000, 13% to 44% in 2010, and about 5% to 35% in 2020. Most cities sit below about 20% each year, and the maximum fell from about 77% to 38%. Data: UN-Habitat Urban Indicators Database.
@sponce1.bsky.socialSep 21, 2026, 8:11 PM

📊 #MakeoverMonday – 2026 W38 | CFB Roster Spending 2026
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #DataFam | #dataviz | #ggplot2

Range chart in four panels, titled SEC Roster Budgets Run Deeper Than Any Other Power Conference's. 14 of 16 SEC programs' estimated 2026 roster-budget ranges sit entirely above the $23.5M median, versus 9 of 18 in the Big Ten, 3 of 17 in the ACC, and 1 of 16 in the Big 12. Each bar spans one program's low-to-high estimate: burgundy if entirely above the median line, dark gray if it crosses the line, light gray if entirely below. Texas Tech is the only Big 12 program above the line. Source: The Athletic.
@lcolladotor.bsky.socialSep 18, 2026, 7:05 PM

140.776 - Statistical Computing at @jhubiostat.bsky.social @johnshopkinssph.bsky.social

Lecture 06 summary

lcolladotor.github.io/jhustatcompu...

#RStats #DataScience #ggplot2 #DataViz

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