#TidyTuesday
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nrennie.bsky.social
Code: github.com/nrennie/tidy...
tidytuesday/2026/2026-10-27 at main · nrennie/tidytuesday
This repository contains the R, Python, D3, and Svelte code used to produce my visualisations for the #TidyTuesday challenges. - nrennie/tidytuesday
github.com
October 28, 2026 at 7:15 PM
deepalikank.in
#TidyTuesday week 43 - IKEA Sofas and Their Swedish Places
#30DayMapChallenge Day 1 - Points

#Rstats #DataViz #DataVisualization #Ikea
Map of Sweden showing 178 possible places sharing names with 100 IKEA sofas and armchairs. Most (77%) lie south of 60°N. Dots mark candidate matches, not confirmed namesakes.
November 2, 2026 at 2:55 AM
surendrab.bsky.social
Long time lurker, first time poster. Giving this #tidytuesday thing a go (yes I know it's a dataset from the beginning of the year and not this week). Code on github: github.com/balraadjsing...
October 30, 2026 at 12:34 PM
karaman.is
This week's #TidyTuesday uses data from my IKEA place names project https://karaman.is/ikea-place-names. I made a small IKEA version of The Economist's Big Mac index.

Code: https://github.com/gkaramanis/tidytuesday/tree/master/2026/2026-week_44

#RStats #dataviz
A horizontal bar chart titled "The POÄNG index". It has one bar for each of 38 countries, showing the price of the same IKEA POÄNG armchair relative to its price in Sweden, sorted from most expensive at the top to cheapest at the bottom. The horizontal axis runs from 0 to just over 2. Sweden's bar is dark red and ends at 1, all other bars are light greyish beige. Finland is at the top at 2.25, followed by Mexico at 2.10, Jordan at 1.91 and the USA at 1.85. Kuwait, Saudi Arabia and Egypt follow at about 1.6. Most European countries sit between 1 and 1.3. Twelve countries are below Sweden, with Japan, Slovenia and Romania at the bottom, Romania at 0.80. A photo of the armchair, with a pale birch frame and a light beige cushion, sits in the lower right corner. The subtitle reads "The same POÄNG armchair costs $65 in Romania and $181 in Finland, more than twice the $80 it costs in Sweden. Each bar shows the price in one of 38 markets relative to Sweden. Prices from ikea.com on 28 September 2026, in US dollars." The caption credits ikea.com as the source.
October 31, 2026 at 2:30 PM
thedatalab.bsky.social
Let an AI agent do the downloading, cleaning and coding while you make the design calls.

Data & AI Meetup: Data Visualisation with AI Agents, with Jon Minton (Knowledge Tree and Butterfly). Live TidyTuesday charts, no coding needed.

📅 10 Nov, 17:30
📍 Bayes Centre

👉 https://ow.ly/qUPh5100W6p
November 2, 2026 at 9:00 AM
nrennie.bsky.social
This week's #TidyTuesday data is all about IKEA furniture🪑This was a fun, messy dataset to play with and I ended up with a parameterised beeswarm chart comparing costs for 1-5 seater sofas across the world 🗺️

Thanks to @karaman.is for curating the data!

#RStats #ggplot2 #DataViz
Beeswarm chart comparing the cost of 1-5 seater sofas in IKEA in Mexico compared to the global average. Generally more expensive.
October 28, 2026 at 7:15 PM
dslc.io
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
October 27, 2026 at 6:09 PM
cborstell.bsky.social
How much IKEA sofa do you get for $1,000 US? 🛋

On average a 5-seat sofa in 🇹🇭, much less in 🇲🇽.
#TidyTuesday
#ggplot2 #DataViz
Æ
Code: github.com/borstell/tid...
A graphic showing a "IKEA sofa index: How much IKEA sofa do you get for US $1000?". Thailand is the baseline, giving you a full 5-seat sofa for the amount, whereas in Mexico, you get less about a 2-seat sofa, shown with a dashed line cutting off the remainder with scissors. Country names Thailand, Canada, Sweden, Germany and Mexico are given with faux umlauts. Data: Ikea; Packages: {tidyverse, ggchicklet, patchwork}; Visualization: C. Börstell.
October 30, 2026 at 1:33 PM
jonthegeek.com
@dslc.io welcomes you to week 43 of #TidyTuesday! We're exploring IKEA Sofas and Their Swedish Places!

📂 https://tidytues.day/2026/2026-10-27
📰 https://karaman.is/ikea-place-names

#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. Map of Sweden with a dot for each place that shares its name with an IKEA sofa or armchair in the Swedish range. Filled dots are sofas, hollow dots armchairs, and ringed dots names used for both. The dots are densest in the south, especially in Skåne, Småland and around Stockholm, and thin out towards the north, where only a handful sit in the interior of Norrland. A red cross marks Älmhult in Småland, where IKEA started. One place, Herråkra in Småland, is highlighted with its name in a blue label.
October 26, 2026 at 5:23 PM
mitsuoxv.bsky.social
My submission for #TidyTuesday, Week 43 on IKEA Sofas and Their Swedish Places. I explore in which area 7 commonly sold IKEA products are highest priced.

Code: github.com/mitsuoxv/tid...
Line chart of 7 IKEA products; x-axis is price in USD (log scale), and y-axis is 49 areas in order of log mean.
October 28, 2026 at 11:39 AM
sponce1.bsky.social
feat(tidytuesday): add tt_2026_43

📊 #TidyTuesday – 2026 W43 | IKEA Sofas and Their Swedish Places
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2
Strip plot titled "For the same IKEA furniture, Estonia runs low and Ireland high." Sixteen euro markets are stacked as rows, and each row has 34 grey dots, one per identical IKEA article, placed by its pre-VAT price relative to that article's median across the 16 markets, on a log scale from minus 30 to plus 50 percent. A burgundy tick marks each market's median. Estonia sits lowest: below the article median on 31 of 34 articles, with a median about 12 percent under. Ireland and Finland sit highest, about 12 percent over; Ireland is above on 29 of 34. Mid-table markets such as Germany split 17 below and 17 above. A right-hand column lists below, tie, and above counts for every market. A note explains that the three Baltic markets share a shelf price on 31 of 34 articles, so Estonia's higher VAT leaves it with a lower pre-VAT price. Data: IKEA.com listings on 28 September 2026, via TidyTuesday.
October 26, 2026 at 8:56 PM
rajodm.bsky.social
#TidyTuesday 2026 week 43

IKEA's Products names

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

#dataviz #rstats #ggplot2
Faceted treemaps comparing the Swedish place types behind IKEA armchair (left) and sofa (right) names sold in Germany. Small settlements, such as hamlets, isolated dwellings and villages, dominate both: 73% of armchair names and 79% of sofa names. Natural features make up 19% of armchair names but only 6% of sofa names. Urban places account for 6% of armchair names and 10% of sofa names. Sofas also have a 5% "Other" category, mostly archaeological sites. Administrative areas are a small share in both (2% of armchairs, 1% of sofas).
October 30, 2026 at 9:19 AM
rladies-bot.bsky.social
📝 "Use resampling to understand #TidyTuesday drought in TX"

Analyze TX drought patterns using spatial resampling!

👤 Julia Silge (@juliasilge.com)

🔗 https://juliasilge.com/blog/drought-in-tx/

#rladies#rstats#oldiebutgoodie
October 30, 2026 at 1:41 PM
karaman.is
This week's #TidyTuesday is about college majors and generative AI. In 7 of the 8 largest fields, majors overlap more with language models than image generation. Engineering is the exception.

Code: https://github.com/gkaramanis/tidytuesday/tree/master/2026/2026-week_43

#RStats #dataviz
A ridgeline chart titled "How exposed are college majors to generative AI?". It has one row for each of the 8 broad fields with the most graduates, with the field name in large faint letters behind each row. From top to bottom the rows are Psychology, Social Sciences, Computer Science, Business, Communication, Engineering, Biological Sciences and Health Professions. Each row has two overlapping density curves of the majors in that field: pink for exposure to image generation and blue for exposure to language modeling. Where the curves overlap they turn purple. Tick marks under each curve show the individual majors. The horizontal axis is the exposure score, where 0 is the average occupation, marked by a dotted vertical line. Most curves sit between 0.5 and 1.5. In Engineering the pink curve sits clearly to the right of the blue one, while in the other fields blue is to the right or the two overlap. Health Professions is the only field spread to the left of 0, with one major near -1. The subtitle explains that a higher score means more overlap with AI, not that the job will be automated. The caption credits the AI Occupational Exposure index as the source.
October 25, 2026 at 2:44 PM
mickael.canouil.fr
Keisen & Gribouille for this week TidyTuesday using Typst.
🎉

m.canouil.dev/tidytuesday/...

#Typst #TidyTuesday
A table with one row for each of the 14 broad fields that have 8 or more majors, from the lowest median AI exposure percentile at the top to the highest at the bottom. Each row shows the field, its number of majors, a horizontal bar with the median exposure percentile, and a strip of one point for each major. Colour shows exposure percentile on a scale from orange for low exposure, through grey at the middle, to blue for high exposure, and the bars use the same colours. In the strips, the horizontal axis is median starting salary, from below $25k to above $100k, with the axis repeated under the last row. Light vertical guides mark $25k, $50k, $75k and $100k, and a dashed line marks the median of all 285 majors, $41,189. From the top: Engineering Tech, 17 majors, median 14. Agriculture, 15 majors, 19. Health Professions, 18 majors, 20. Visual & Performing Arts, 10 majors, 20. Biological Sciences, 14 majors, 45. Engineering, 38 majors, 55. Architecture, 8 majors, 56. Education, 12 majors, 61. Business, 19 majors, 71. Computer Science, 8 majors, 71. Physical Sciences, 8 majors, 71. Social Sciences, 12 majors, 72. Foreign Languages, 8 majors, 79. Interdisciplinary, 22 majors, 81. The bars get longer from the top to the bottom, and their colour moves from orange to blue, but the strips show no such trend along the salary axis. All 17 Engineering Tech points and all 38 Engineering points sit to the right of the dashed line, although their medians are 14 and 55, and all 10 Visual & Performing Arts points, in a field whose median is 20, sit to its left. In 11 of the 14 fields, orange and blue points are mixed along the salary axis, with majors both below and above the 50th percentile of exposure.
October 22, 2026 at 10:01 AM
nrennie.bsky.social
This week’s #TidyTuesday data explores college majors, graduate numbers, salaries, and exposure to AI 📊

Business majors have the largest number of graduates, the bubble chart shows this a large share of graduates are entering occupations with relatively high AI exposure 🎓

#DataViz #RStats
Bubble plot showing median salary four years after college against average AI occupational exposure for business-related college majors. Each circle represents a major, with larger circles indicating more graduates. Most business majors cluster toward the higher end of AI exposure, with salaries varying widely; several high-exposure majors have median salaries above $80,000. The chart highlights that business majors tend to combine relatively high AI exposure with comparatively strong post-college earnings.
October 23, 2026 at 3:59 PM
jessimoore.bsky.social
#tidytuesday AI occupational exposure. Made with ggplot2 and edited using Figma.
AI Occupational Exposure, shown by field of study and median graduate starting salary. The top 6 most common fields of study are shown: Business and Marketing, Health Professions, Computer and Information Sciences, Psychology, Social Sciences and Engineering. Health Professions show the most spread of AI exposure and salaries. Most fields of study shown have higher AI exposure than average. Engineering shows the highest cluster of starting salaries.
October 23, 2026 at 10:21 AM
mhosek.bsky.social
And another attempt at #tidytuesday.
October 21, 2026 at 3:09 PM
deepalikank.in
#TidyTuesday week 42 - Which College Majors Are Safest From AI (and Do They Pay)
Interactive Version: observablehq.com/@deepalikank...

#DataViz #AI
Circular chart of AI exposure across 463 occupations: 63% above average and 37% below. Genetic counselors and financial examiners rank highest; dancers lowest. Coral bars show above-average exposure, teal below. Exposure reflects overlap with AI capabilities, not predicted job loss.
October 21, 2026 at 12:04 PM
dslc.io
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
jonthegeek.com Jon Harmon (he/him/his) @jonthegeek.com · 13d
@dslc.io welcomes you to week 42 of #TidyTuesday! We're exploring Which College Majors Are Safest From AI (and Do They Pay)?!

📁 https://tidytues.day/2026/2026-10-20
📰 https://arxiv.org/abs/2303.01157

#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. Scatter plot of 285 bachelors majors comparing the AI exposure of the occupations each major leads to (x-axis, left is least exposed) against median starting salary one year after graduation (y-axis). Points are green when a major is in the safer, higher-paid half and purple when more AI-exposed. Bubble size shows the number of graduates measured. The points are spread out with no real correlation between AI exposure and starting salary. Registered nursing, nuclear engineering technology, and marine transportation are safe from AI and well paid, while English and philosophy are among the most exposed and lowest paid.
October 20, 2026 at 5:30 PM
cborstell.bsky.social
#TidyTuesday material
October 19, 2026 at 1:07 PM
jonthegeek.com
@dslc.io welcomes you to week 42 of #TidyTuesday! We're exploring Which College Majors Are Safest From AI (and Do They Pay)?!

📁 https://tidytues.day/2026/2026-10-20
📰 https://arxiv.org/abs/2303.01157

#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. Scatter plot of 285 bachelors majors comparing the AI exposure of the occupations each major leads to (x-axis, left is least exposed) against median starting salary one year after graduation (y-axis). Points are green when a major is in the safer, higher-paid half and purple when more AI-exposed. Bubble size shows the number of graduates measured. The points are spread out with no real correlation between AI exposure and starting salary. Registered nursing, nuclear engineering technology, and marine transportation are safe from AI and well paid, while English and philosophy are among the most exposed and lowest paid.
October 19, 2026 at 5:29 PM
mitsuoxv.bsky.social
My submission for #TidyTuesday, Week 42 on which college majors are safest from AI. I explore correlation between starting salary and AI exposure in total, text-based tasks, or visual tasks.

Code: github.com/mitsuoxv/tid...
Scatterplots faceted by AI exposure type (Total, Text-based tasks, and Visual tasks); x-axis is standardized AI exposure score, and y-axis is median earnings for one year from graduation.
October 20, 2026 at 11:46 AM
karaman.is
This week's #TidyTuesday is about frogs and toads. AmphibiaWeb lists 8,018 species. Six families hold 54% of them, and one genus, Pristimantis, has 631.

Code: https://github.com/gkaramanis/tidytuesday/tree/master/2026/2026-week_42

#RStats #dataviz
A dot plot in the shape of a frog, seen from the side with its head on the right, under the title "One frog, 8,018 species". Each dot is 5 species of frog or toad in AmphibiaWeb, and the dots are coloured by family in vertical bands from left to right. The bands run from the rump to the head. Beige is the 51 smaller families together with 3,721 species, teal is Rhacophoridae with 467, blue is Ranidae with 470, orange is Bufonidae with 666, purple is Microhylidae with 768, yellow is Strabomantidae with 840 and green is Hylidae with 1,086. The subtitle says the six largest families hold 54% of all species and that the genus Pristimantis accounts for 631 of them. Below the frog, a bracket under each band gives the family name and its number of species. The caption credits AmphibiaWeb as the source and Jose Carlos Arenas-Monroy for the silhouette.
October 18, 2026 at 12:11 PM
sponce1.bsky.social
📊 #TidyTuesday – 2026 W42 | Which College Majors Are Safest From AI (and Do They Pay)?
.
🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #dataviz | #ggplot2
Cumulative step chart titled "Hardly any graduates studied a major less AI-exposed than the average occupation." At the index's zero line, the curve reaches only 3.5%: 3.5% of 2,431,833 graduates with measured earnings studied one of the 31 bachelor's majors scoring below the average occupation on the AI Occupational Exposure (AIOE) index, highlighted in burgundy. The horizontal axis shows the AIOE score from about −1.3 to +1.5, with zero marking the unweighted average across 774 occupations. The vertical axis shows the cumulative share of measured graduates across 285 majors. To the right of zero, the curve climbs: a single step at +0.44, Registered Nursing, adds 17.6% of graduates, and the curve crosses 50% at about +1.07. Exposure measures overlap with AI abilities, not job displacement. Data: College Scorecard, AIOE (Felten, Raj and Seamans 2021), NCES/BLS CIP-SOC crosswalk.
October 19, 2026 at 7:31 PM