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#Tidytuesday
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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
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Good morning all you fantastic people in my phone. Ready for a terrific Tidy Tuesday? Have a wonderful day 😘
#tightywhities #thickandmeaty #bulge #daddy #TidyTuesday
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A bar graph depicting the total amount that G7 countries spend on healthcare and the source of those funds.
Many people say the US can’t afford universal healthcare, but the data tells a different story. The US already spends more per capita on publicly funded healthcare than many countries that have universal coverage #rstats #data #healthpolicy #tidytuesday
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A plot showing out-of-pocket payments as a percent of total health spending for many countries over time. Countries are grouped using 2022 World Bank income classifications. Reliance on out-of-pocket payments for health care is falling, but many countries still face heavy burdens
Yesterday we announced the alpha release of #ggsql 🎉 In celebration, here is a ggsql plot for #TidyTuesday Week 16 of 2026, using a dataset on global health spending! 💰
ggsql source: gist.github.com/georgestagg/...
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Purpose-wise distribution of health spending for top 10 countries with maximum total spending.
Distribution of health spending for top 10 countries.
Interactive graph prepared using Highcharts.
Notebook: dataviz.manishdatt.com/posts/2643/
#TidyTuesday #dataviz #Javascript
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Recent DSLC.club meetings:
🔵 #TidyTuesday Cookbook: Cats: data-driven annotations with ggtext youtu.be/L-WYZFQ7AEQ
Support the Data Science Learning Community at patreon.com/DSLC
#dataBS #TidyTuesday #RStats #DataViz #ggplot2
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Chart with arrows which start around 2000 and end around 2023; x-axis is percent share of OOPS in health spending, and y-axis is countries that changed more than 25 percent points.
My submission for #TidyTuesday, Week 16 on Global Health Spending. I explore countries with large OOPS share changes.
Code: github.com/mitsuoxv/tid...
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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.
A collage of four charts exploring global health expenditure using data from the Global Health Expenditure Database (GHED). Top left: a Voronoi treemap of total global health care spending broken down by country, dominated by high income countries. Top right: a small-multiples line chart showing out-of-pocket payments as a share of health spending are declining across all income groups, but remain high in many countries. Bottom left: a bubble chart showing many African countries fall short of the $86 per capita and 5% of GDP health spending targets. Bottom right: a scatter plot with LOESS curves showing curative spending rises with income while preventive spending gets squeezed.
@dslc.io welcomes you to week 16 of #TidyTuesday! We're exploring Global Health Spending!
📂 https://tidytues.day/2026/2026-04-21
📰 data.one.org/analysis/out-of-pocket-h...
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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For this week's #TidyTuesday, I have messed around with #Shiny for the first time in a while. #rstats
App Here:
eh1prp-dwyfor16.shinyapps.io/Global_Healt...
Code Here:
pgdatavizandstats.netlify.app/data_visuali...
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World map where each country is represented by a small, single row heatmap showing the percentage of health spending that is preventive between 2016 and 2023. African countries typically spend more, and there are lots of highlighted stripes in 2021.
This week's #TidyTuesday data is all about Global Health Spending 💰️
I created a minimalist world map showing percentage of health spending on preventive care 📊
Notable upticks in 2021 across many countries, higher % overall in African countries, and lots of missing data
#DataViz #RStats #ggplot2
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Recent DSLC.club meetings:
🔵 The Art of Data Visualization with ggplot2: The #TidyTuesday Cookbook: Chapter 6 "Canadian wind turbines: waffle plots and pictograms" youtu.be/brvhDGNRIAY #RStats #DataViz #ggplot2
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A grid of 26 small maps on a white background titled "The captain's logbook." The top-left area contains the subtitle and source caption. The remaining panels show seabird observation locations in the Southern Ocean for 25 individual ship observers, ordered by total observations. J. Jenkins dominates with 6583 observations (1969–1988), his panel filled densely with blue dots scattered across Antarctic and subantarctic waters. N. Cheshire has 1462 observations (1975–1983). Subsequent rows show progressively fewer observations, down to observers with only 1–4 sightings in the final row. Each map covers approximately 65–180°E longitude and 20–75°S latitude, with a muted blue ocean and soft tan land masses. Observer name, total observations, and year range are shown above each panel. Caption reads "Source: Museum of New Zealand Te Papa Tongarewa · Graphic: Georgios Karamanis."
This week's #TidyTuesday dataset comes from the at-sea seabird records held by Te Papa Tongarewa, built largely from the handwritten logbooks of Captain J. Jenkins, who recorded 6583 bird sightings on Southern Ocean voyages from 1969 to 1988.
Code: github.com/gkaramanis/t...
#RStats #dataviz
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A two-panel data visualization. The left panel is a scatter plot showing government health spending versus out-of-pocket payments as a percentage of current health expenditure for 195 countries in 2023. Countries with lower government spending cluster in the upper-left with high out-of-pocket burden, highlighted in burgundy. Countries with high government spending cluster in the lower-right with low out-of-pocket burden, highlighted in steel blue. A dashed diagonal reference line and a dotted 40% hardship threshold line provide analytical anchors. The right panel shows global median trends from 2000 to 2023: government spending (blue) rising steadily, out-of-pocket payments (burgundy) declining. A shaded band marks COVID-19 years. Together, the panels show where governments spend less, households spend more — and that this pattern has slowly improved globally over two decades.
📊 #TidyTuesday – 2026 W16 | Global Health Spending
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
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For #tidytuesday week 2026-04-07 looking at the repairability of household items via a repair cafe dataset, I compared repair completeness between categories (i.e., whether a repair was completed or partially completed).
#ggplot #rstats #dataviz
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#TidyTuesday week 14 #rstats #dataviz
Repair Success Rates Vary Widely by Item Type
code: github.com/b3m3bi/tidyt...
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Scatter plot showing a positive correlation between review text sentiment and review scores for Animal Crossing, with a fitted S-curve. Reviews with positive sentiment cluster around scores of 8–10, while negative sentiment reviews are spread across all score levels.
Bar chart showing the distribution of Animal Crossing review scores on Metacritic. The distribution is strongly U-shaped, with scores of 0 (approximately 1,100 reviews) and 10 (approximately 650 reviews) dominating, and scores in the middle range (5–7) receiving the fewest reviews.
Day 15: Correlation
Are Animal crossing review ratings correlated with review text sentiment?
Yes... but rating scores are not normally distributed.
#30DayChartChallenge #TidyTuesday #tidyplots
year <- 2020
week <- 19
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#TidyTuesday 2026-04-14 Bird Sightings at Sea.
A was wondering how weather and sea state associated with which bird could be seen.
Code: gitlab.com/karel_fiser/...
#DataViz #RStats #ggplot2
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From the DSLC.video aRchives:
🔵 The Art of Data Visualization with ggplot2: The #TidyTuesday Cookbook: Introduction youtu.be/TKD_i8orDwY
Support the Data Science Learning Community at patreon.com/DSLC
#dataBS #RStats #DataViz #ggplot2 #dataVisualization
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@sara-altman.bsky.social and I recently got together to compare Claude Code and Posit Assistant for data analysis with a recent #TidyTuesday release! The recording is now live on YouTube: www.youtube.com/watch?v=7GI6...
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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
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New Zealand map with recorded points of seabird observations; faceted by years of 1969 and 1975-88.
My submission for #TidyTuesday, Week 15 on Bird Sightings at Sea. I plot recorded points within 100km from coastal lines.
Code: github.com/mitsuoxv/tid...
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Timeline bubble chart showing observations of different penguin types off the coast of New Zealand from 1969 to 1990. Adelia, Emperor, and Fiordland crested have only been seen a few times, but there are regular sightings of Little penguins.
For this week's #TidyTuesday data on sea bird sightings, there was an obvious choice for which type of bird to focus on - penguins! 🐧
📊 Bubble timeline made with #RStats
🎨 Colours inspired by {palmerpenguins}
📝 Annotations added with {cowplot}
#DataViz #ggplot2
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#TidyTuesday week 15: Bird Sightings at Sea🌊🐦
I built this bird swarm visualization using #D3js
I explored how sightings cluster across cloud type conditions and turn the data into a drifting swarm of bird marks.
#DataViz
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#TidyTuesday this week looked at bird sightings at sea. I chose to explore the observers' relationship with the "Navigators", a role according to their Māori roles. 🔗 github.com/afrikaniz3d-...
#DataViz #RStats #DataVisualization #ECharts4R #Quarto Open to freelance data viz/reporting work
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For #tidytuesday week 2026-03-31, looking at coastal ocean temperatures in Nova Scotia, I was mesmerized by the seasonal pattern displayed in my initial geom_point exploration plot that I had to keep it. Temperature generally goes down with depth.
#rstats #dataviz #ggplot2
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Exploring the Milan-Cortina 2026 Olympic schedule for #TidyTuesday ❄️⛸️⛷️
I looked at how different sports allocate officially scheduled sessions between training and competition.
🔗Code: github.com/Lexi711State...
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Bird Sightings in the Tasman Sea, New Zealand and Australian waters (1969–1990) for #TidyTuesday, week 14.
#30DayChartChallenge | Flowingdata
#Rstats #Dataviz #ggplot2
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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.
Screenshot of an interactive visualization summarizing bird sightings. At top, a zoomed out world map displays record density with thousands of observations intensely clustered in specific grid cells within the Southern Ocean. At bottom left, a temporal histogram starts with about 1000 records in 1969, no data for 1970 to 1974, then counts that peak at over 3000 records from 1984 to 1987, quickly trailing off and ending in 1990. At bottom right, a nested taxonomic sunburst chart shows that all records are within the Phylum Chordata and Class Aves. The vast majority of records are within Order Procellariiformes and Family Procellariidae, with representation from other Procellariiformes Families (primarily Diomedeidae) and other Orders: Charadriiformes (Family Laridae) and Pelecaniformes (Family Sulidae).
@dslc.io welcomes you to week 15 of #TidyTuesday! We're exploring Bird Sightings at Sea!
📁 https://tidytues.day/2026/2026-04-14
🗞️ obis.org/dataset/29ea15ed-8f76-40...
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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