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@rladies-bot.bsky.socialOct 8, 2026, 2:08 PM

📝 "Análisis de las inversiones de EEUU entre 1947 y 2017"

Practicando visualización de datos con R y #TidyTuesday.

👤 Macarena Quiroga (@macarenaquiroga.bsky.social)

🔗 https://macarenaquiroga.com/post/analisis-inversiones-eeuu-ciencia-de-datos/

#rladies #rstats #oldiebutgoodie

@ssamwh.bsky.socialOct 7, 2026, 10:04 PM

#TidyTuesday Week 40: Avocado Oil Authenticity

If future researchers wanted to do a panel of fatty acids/sterols to quickly assay for authenticity, which ones should they use? It's pretty consistent across oils and "avocado" oil-containing foods.

Code: github.com/sam-wh/tidy_...

Graph of (bio?) markers of avocado oil/avocado oil containing foods
@deepalikank.inOct 7, 2026, 5:52 AM

#TidyTuesday week 40 - Avocado Oil Authenticity

Code: observablehq.com/@deepalikank...

#DataViz

@dslc.ioOct 6, 2026, 6:32 PM

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io!
#RStats #PyData #JuliaLang #RustLang #DataViz #DataScience #DataAnalytics #data #tidyverse #DataBS

@rladies-bot.bsky.socialOct 6, 2026, 1:44 PM

📝 "Three ways to look at #TidyTuesday UK pay gap data"

Analyze UK gender pay gaps using these 3 data methods.

👤 Julia Silge (@juliasilge.com)

🔗 https://juliasilge.com/blog/pay-gap-uk/

#rladies #rstats #oldiebutgoodie

@mitsuoxv.bsky.socialOct 6, 2026, 11:56 AM

My submission for #TidyTuesday, Week 40 on Avocado Oil Authenticity. I explore a combination of fatty acid and sterol profiles to identify pure avocado oil in 2020 bottled oil examination.

Code: github.com/mitsuoxv/tid...

Scatter plot; x-axis is palmitoleic acid in total fatty acids, y-axis is beta-sitosterol in total sterols, and points are classified into 3 categories (pure, suspected, or adulterated).
@manishdatt.comOct 6, 2026, 7:37 AM

Distribution of products based on the authenticity of the oil used.

Notebook: dataviz.manishdatt.com/posts/26A1/

#TidyTuesday #dataviz #rstats

Distribution of products like chips, mayonnaise, salad dressing, based on the authenticity of the oil used in them.
@sponce1.bsky.socialOct 5, 2026, 9:02 PM

📊 #TidyTuesday – 2026 W40 | Avocado Oil Authenticity
.
🔗: 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.
@jonthegeek.comOct 5, 2026, 7:38 PM

@dslc.io welcomes you to week 40 of #TidyTuesday! We're exploring Avocado Oil Authenticity!

📂 https://tidytues.day/2026/2026-10-06
📰 https://www.ucdavis.edu/food/news/avocado-oil-chip-youre-eating-may-not-be-made-pure-avocado-oil

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results! TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club! How to Participate Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data. Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language. Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.PCA biplot of fatty acid profiles from oils extracted from processed foods. Samples classified as consistent with authentic avocado oil cluster together on the right, clearly separated from inconsistent samples that cluster near vegetable oil comparators on the left, demonstrating that chemical fingerprinting can reliably distinguish authentic avocado oil from adulterated products.
@lisahornung.bsky.socialOct 5, 2026, 1:53 PM

Turned my explorations of word mentions in popular country songs into a mini collection.🐴🤠🎵

Had a lot of fun playing with circular shapes and earthy colour tones of country albums.

Data from #TidyTuesday, made in python matplotlib + Figma. Detailed versions 👇

#Dataviz

Collage of four squared images. Each of them showing a circular shape that visualises word mentions in country song lyrics. Exploring patterns for 'girl', 'baby', 'love' and 'town'.
@jonthegeek.comOct 5, 2026, 11:26 AM

TIL (via a #TidyTuesday submission): In `dplyr::rename()`, the RHS of each = can be a column *number* (index). That makes sense, but it's documented as "new_name = old_name syntax", so I hadn't thought about the implications of what "old_name" can be!

@klauswiese.bsky.socialOct 4, 2026, 9:12 PM

¿Cuánto verde queda en las ciudades de Honduras? Mi aporte a #TidyTuesday (semana 38) con datos de ONU-Hábitat muestra que El Progreso, San Pedro Sula y La Ceiba han perdido entre un tercio y más de dos tercios de su área verde desde 1990.

@lisahornung.bsky.socialOct 4, 2026, 4:48 PM

Exploring country songs featured in the Billboard Top 30 (2013-2019). If you played all 484 songs at once, most of the time you'd hear someone sing 'girl'.

Data via #TidyTuesday. Prototype in python, final version in Figma - code here: github.com/Lisa-Ho/smal...

#Dataviz

Visualisation exploring mentions of 'girl' in top country songs in 2013-2019. A spiral of individual markers where each marker depicts a single word in a song. The colour of each marker represents how many songs mention 'girl' at that exact location in the song.
@karaman.isOct 4, 2026, 1:53 PM

This week's #TidyTuesday is about hospitals in urban centres, from the EU's JRC. The data has quality problems, so I plotted the counts themselves instead. 95% of the counts are even, which suggests hospitals are counted twice.

Code: github.com/gkaramanis/t...

#RStats #dataviz

Two charts side by side under the title "Two hospitals, or none*", with a subtitle about data from the EU's Joint Research Centre. On the left, a bar chart of urban centres by their number of hospitals, from 1 to 32. Even numbers have much taller bars than odd ones. Two hospitals is the most common count at 1,982 urban centres, followed by four at 1,080, six at 677 and eight at 379. No odd number has more than 26. There is no bar at 1, marked with an arrow and the text "No urban centre has a single hospital". A second label reads "95% of hospital counts are even". On the right, one horizontal bar per region, with thickness showing how many urban centres the region has. Each bar is split into a blue part for urban centres with a hospital count and a grey part for those without. The blue share is smallest in Central and Southern Asia at 30%, then Sub-Saharan Africa at 47% and Eastern and South-Eastern Asia at 52%. It is largest in Europe at 95%.
@mitsuoxv.bsky.socialSep 30, 2026, 11:44 AM

My submission for #TidyTuesday, Week 39 on Health metrics in urban centres worldwide. I explore number of hospitals and pharmacies per capita, and pharmacies per hospital by income group.

Code: github.com/mitsuoxv/tid...

Scatter plot faceted by income group (High income, Upper Middle, Lower Middle, and Low income); x-axis is number of hospitals per capita (log scale), and y-axis is number of pharmacies per capita (log scale).
@dslc.ioSep 29, 2026, 6:20 PM

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io!
#RStats #PyData #JuliaLang #RustLang #DataViz #DataScience #DataAnalytics #data #tidyverse #DataBS

@manishdatt.comSep 29, 2026, 11:46 AM

CDFs for income-segregated urban population based on proximity to a hospital in 2025.

Notebook: dataviz.manishdatt.com/posts/2695/

#TidyTuesday #dataviz #rstats

Cumulative Distribution Function for income-segregated urban population based on proximity to a hospital in 2025.
@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.
@jonthegeek.comSep 28, 2026, 6:48 PM

@dslc.io welcomes you to week 39 of #TidyTuesday! We're exploring Health metrics in urban centres worldwide!

📂 https://tidytues.day/2026/2026-09-29
📰 https://human-settlement.emergency.copernicus.eu/ghs_ucdb_2024.php

#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds

Logo for the #TidyTuesday Project. The words TidyTuesday, A weekly data project from the Data Science Learning Community (dslc.io) overlaying a black paint splash.TidyTuesday is a weekly social data project. All are welcome to participate! Please remember to share the code used to generate your results! TidyTuesday is organized by the Data Science Learning Community. Join our Slack for free online help with R and other data-related topics, or to participate in a data-related book club! How to Participate Data is posted to social media every Monday morning. Follow the instructions in the new post for how to download the data. Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language. Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.Exterior of a modern hospital building at dusk, with a brick and glass facade lit from within, a colorful sculpture of a child holding balloons on the wall, young trees and a landscaped walkway in front, and a U.S. flag near the entrance. Photo by Acton Crawford on Unsplash, https://unsplash.com/photos/brown-and-white-concrete-building-near-green-trees-under-blue-sky-during-daytime-8PB_TFEy2XQ
@sponce1.bsky.socialSep 28, 2026, 2:52 PM

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