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rodrigo404.bsky.social
time for #tidytuesday pt2

a simple one, but with a gradient to distract, lol
code at: barreiro-r.github.io/tidytuesday/

#r4ds #ggplot2 #dataviz
November 10, 2025 at 9:23 PM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W45 | WHO Tuberculosis Global Burden Estimates
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
Line chart showing HIV-associated TB deaths as percentage of total TB mortality by WHO region from 2000-2024. Africa's bold blue line dominates, declining from 60% to 28% (31.9 percentage point decrease). Five other WHO regions shown in gray remain below 35% throughout, with most under 15%. All regions labeled at right endpoint. Chart demonstrates Africa's significant progress yet persistent disproportionate burden of HIV/TB co-infection.
November 9, 2025 at 7:08 PM
cborstell.bsky.social
#TidyTuesday in the style of Chartle showing estimated incidence of TB

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

#R4DS #DataViz
A line chart in the style of Chartle (a statistics guessing game) on a dark gray background with light gray/white text and lines, showing "Estimated incidence of tuberculosis per 100 000 population". One line is highlighted in coral red, showing the change for Marshall Islands from 2012 to 2023, with a huge surge from around 400/100k to well over 1000/100k in 2018. Data: World Health Organization via {getTBinR} and Sam Abbott via TidyTuesday; Packages: {tidyverse, scales}; Visualization: C. Börstell
November 10, 2025 at 9:48 PM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W44 | Lead concentration in Flint water samples in 2015
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
A two-panel visualization comparing lead contamination in Flint, Michigan water samples (2015). The top panel shows a stacked bar chart with five risk categories from zero detection to above the EPA action level; Virginia Tech citizen science found 16.6% of 271 samples above the 15 ppb action level, versus the MDEQ government's 11.3% of 71 samples. The bottom panel displays cumulative distribution curves showing the percentage of samples below each lead concentration; at the 90th percentile, MDEQ measured 18 ppb, while Virginia Tech measured 26.6 ppb. The brown color palette progresses from light tan (safe) to dark brown (dangerous). Both charts emphasize EPA's health goal of 0 ppb.
November 1, 2025 at 7:00 PM
jonthegeek.com
Curator: @jenrichmondPhD
@dslc.io welcomes you to week 44 of #TidyTuesday! We're exploring Lead concentration in Flint water samples in 2015!

📂 https://tidytues.day/2025/2025-11-04
📰 https://academic.oup.com/jrssig/article/14/2/16/7029247

#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. This histogram shows the distribution of lead concentrations in water samples collected by Flint city officials from January to June 2015, during the Flint water crisis. The x-axis shows lead levels in parts per billion (ppb), with ND indicating non-detectable amounts, and the y-axis shows the number of samples at each concentration level. Most samples cluster at low concentrations, creating a right-skewed distribution with a long tail extending to very high values. Three vertical lines mark important regulatory levels. The blue dashed line is labeled The 90th percentile after excluding two high outliers, the black dashed line is labeled The EPA's lead action level, and the red dashed line is labeled The original 90th percentile before exclusions. Two samples shown in red were excluded from the official calculations, one at 20 ppb and another at 104 ppb.
November 3, 2025 at 1:40 PM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W43 | Selected British Literary Prizes (1990-2022)
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
A two-panel visualization analyzing gender equity in British literary prizes from 1990 to 2022. The top panel shows a line chart of women's share of winners, which increased from 35% to 50% and showed significant year-to-year variation. The bottom panel displays a horizontal pyramid chart comparing 13 major prizes, revealing that only four achieved gender balance (40-60% women). Seven prizes remain male-dominant, and two are female-dominant, showing persistent disparities despite overall progress.
October 26, 2025 at 11:21 AM
rodrigo404.bsky.social
#tidytuesday, on friday?!

--

So many things to do with this week dataset!

#r4ds #ggplot2 #dataviz
October 24, 2025 at 5:36 PM
cborstell.bsky.social
Friday night early #TidyTuesday of British Literary Prizes.

Wanted it as a bookshelf, made it into a bookshelf 📚

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

#R4DS #DataViz
A plot with the title "British Literary Prizes (1991-2022): Proportion of women & non-binary authors among literary prize recipients over time". The plot is designed to resemble a bookcase, with each 5-year time period from 1991 to 2022 featured as a single shelf, and books representing individual prize recipients. Women and non-binary winners are color coded pink-ish and light blue-ish, respectively. Women and non-binary authors were in the minority until 2010–2014 and have constituted about half of the winners since then. Data: Post45 Data Collective via TidyTuesday; Packages: {tidyverse, marquee}; Visualization: C. Börstell.
October 24, 2025 at 10:28 PM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W42 | Historic UK Meteorological & Climate Data
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
Combined visualization showing UK climate change from 1900-present. The top panel displays four trend charts: maximum temperature rising from ~12.5°C to 14°C, monthly rainfall increasing from 70mm to 80mm, monthly sunshine relatively stable around 115-120 hours, and air frost days declining from ~4 to 2 days. Bottom panel shows a timeline of extreme weather events with dots representing years in the top panel/bottom 5% for each metric. Extreme heat events (orange dots) cluster heavily after 1990, with 6 of 7 occurring after 2000, while extreme cold events (navy dots) become increasingly rare in recent decades. Extreme rainfall events (blue dots) are scattered throughout the period. Annotations highlight post-1990 clustering and the notable 2003 heatwave.
October 19, 2025 at 10:12 PM
opus1993.bsky.social
oh wow, yeah, I was all in with twitter from the moment our prof demonstrated @kearney.bsky.social 's rtweet package and @jonthegeek.com had some sort of early podcast and I learned about R4DS (dslc.io now) and #tidytuesday and lots of great youtube content
October 17, 2025 at 2:46 AM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W41 | World Food Day
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
Line chart showing five food security indicators tracked by countries from 2005 to 2025. Four gray lines (Child Overweight, Low Birthweight, Child Stunting, Undernourishment) remain steady at 50-120 countries. One purple line (Severe Food Insecurity) dramatically rises from 35 countries in 2016 to 60 in 2022, with a gray shaded box highlighting the post-2016 period.
October 13, 2025 at 10:17 PM
jonthegeek.com
Curator: @jonthegeek.com
@dslc.io welcomes you to week 41 of #TidyTuesday! We're exploring World Food Day!

📁 https://tidytues.day/2025/2025-10-14
🗞️ https://en.wikipedia.org/wiki/World_Food_Day

#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. Logo of the Food and Agriculture Organization of the United Nations. On the left, a circular emblem features a stylized ear of wheat with the letters 'FAO' above the Latin phrase 'Fiat Panis,' meaning 'Let there be bread.' To the right, the organization's full name appears in blue text.
October 13, 2025 at 1:39 PM
cborstell.bsky.social
Euroleague Basketball #TidyTuesday

A mini dataset, so decided to find a way to plot stadium capacity in an interesting way: went with points along the trajectory of a basketball shot! Swoosh! 🏀

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

#R4DS #DataViz
A plot that says Euroleague Basketball teams by stadium capacity on a offwhite background and black/gray text. Each data point is plotted as a basketball (emoji) and text label with the name of the team(s) playing at the arena, along a parabola simulating the trajectory of a basketball shot towards a schematic basketball hoop located in the bottom right corner. A caption reads: "There are 20 teams in Euroleague Basketball, playing at 19 unique arenas with a median capacity of 12,700 spectators. The biggest arena by capacity is Belgrade Arena which hosts the teams Crvena zvezda Meridianbet and Partizan and seats up to 18,386 people. The smallest arena is Salle Gaston Médecin which hosts Monaco and has a capacity of 5,000. Data: EuroLeague & Wikipedia via {TidyTuesday}; Packages: {tidyverse, ggrepel, ggtext, glue, scales} ; Visualization: C. Börstell."
October 6, 2025 at 1:06 PM
cborstell.bsky.social
I partially contributed to this week's #TidyTuesday dataset of crane observations at the lake Hornborgasjön, 🇸🇪

I made a minimalist plot of observation maxima – earlier over time – in the style of written notes.

{magick} magic for images!

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

#R4DS #DataViz #ggplot2
A data visualization in the style of handwritten/-drawn notes on a piece of paper. Title reads "Cranes at Lake Hornborgasjön: Observations of cranes at the Swedish lake peak earlier over time". The graph shows ridgelines along dates from mid-March to end of April (x-axis) across the years 1994 to 2024 (y-axis). The first date each year where that year's maximum number of observations was reached is marked with a little handdrawn X on the year ridgelines. Across all years, there are lines showing the mean and median values for first max observations across years down the middle. X'es are generally farther to the right in early years (i.e. later in the spring) and farther to the left in more recent years (earlier in the spring). In the plot margins, there are embedded images of cranes in flight. Data: "Transtatistik", Naturum Hornborgasjön via TidyTuesday; Image: Gllawm, Wikimedia Commons curid=147233798; Packages: {tidyverse, magick}; Visualization: C. Börstell
September 29, 2025 at 1:59 PM
jonthegeek.com
@dslc.io welcomes you to week 39 of #TidyTuesday! We're exploring Crane Observations at Lake Hornborgasjön, Sweden (1994–2024)!

📁 https://tidytues.day/2025/2025-09-30

#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. Large flock of cranes gathered in a grassy field during golden hour, with some birds in flight overhead and autumn-colored vegetation in the background. Line chart showing the number of cranes per day at Lake Hornborga in Sweden during March and April from 2014-2021. Multiple gray lines represent different years, with the 2018 and 2021 seasons highlighted in blue. The chart shows crane migration patterns starting near zero in early March, building to peak numbers of 15,000-27,000 birds between March 30 and April 8 (highlighted in beige), then declining through late April. The highest recorded count was 27,300 cranes on April 3rd, 2019. Dashed lines indicate days when weather conditions made accurate counting difficult. A note explains that 2018 had unusually cold February temperatures causing later arrival. The chart is credited to Anna Thieme from Länsstyrelsen VG at https://transtat.lansstyrelsen.se/
September 29, 2025 at 1:38 PM
rodrigo404.bsky.social
Some real Queen's Gabit vibes in this week #tidytuesday

#r4ds #dataviz #ggplot2
September 23, 2025 at 12:38 AM
jonthegeek.com
@dslc.io welcomes you to week 38 of #TidyTuesday! We're exploring FIDE Chess Player Ratings!

📁 https://tidytues.day/2025/2025-09-23
📰 https://www.fide.com/fide-september-2025-rating-list-vincent-keymer-debuts-in-top-10-open/

#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. FIDE Rating List Top 10 as of September 1, 2025. At the top of the graphic, three players are featured prominently. Magnus Carlsen of Norway in first place with a rating of 2839, Hikaru Nakamura of the United States in second with 2807, and Fabiano Caruana of the United States in third with 2789, marked with a green +5 for his recent rating gain. Below them, the list continues. Fourth is Praggnanandhaa R of India with 2785 (+6), fifth is Arjun Erigaisi of India with 2771 (–5), sixth is Gukesh D of India with 2767 (–9), seventh is Wesley So of the United States with 2756 (+11), eighth is Alireza Firouzja of France with 2754 (–12), ninth is Wei Yi of China with 2753, and tenth is Vincent Keymer of Germany with 2751 (+21). The left side of the graphic features the FIDE logo and text reading 'FIDE Rating List Top 10' and 'September 1, 2025' in yellow on a black background.
September 22, 2025 at 1:38 PM
cborstell.bsky.social
The 🍗 or the 🍳? #TidyTuesday

Looking at the ingredients of over 2000 recipes online, where are different cuisines found on the chicken vs. egg (x-axis) and butter vs. oil (y-axis) scales?

As a Scandinavian, I guess I'm part of the egg+butter outlier!

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

#R4DS
A scatterplot on a cream white background with the title "The chicken 🐓 or the egg 🥚?". The square grid is split by four reddish arrows pointing outwards north/south/east/west. A textbox in the top left reads "Based on 2,218 recipes categorized by the cuisine (country, region, culture), this graph shows the proportional frequency of the ingredients chicken vs. egg (x-axis) & butter vs. oil (y-axis) mentioned across recipes of each cuisine". Each point and text in the plot indicates where on a scale from chicken (left) to egg (right) and butter (top) to oil (bottom) the cuisine is located. Turkish is found at the very center of the plot, Chinese and Thai in the bottom left (chicken & oil), whereas Austrian/German/Swiss and particularly Scandinavian are in the top right (egg & butter). An arrow points at the top right data point saying "Scandinavian cuisine is a clear outlier". Visualization: C. Börstell; Data: allrecipes.com via {tastyR} & TidyTuesday; Packages: {ggarrow, ggrepel, ggtext, tidyverse}
September 15, 2025 at 12:52 PM
jonthegeek.com
Curator: @hootist.bsky.social
@dslc.io welcomes you to week 37 of #TidyTuesday! We're exploring Allrecipes!

📂 https://tidytues.day/2025/2025-09-16
🗞️ https://www.brians.works/i-scraped-14k-recipes-so-you-wont-have-to/

#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. A close-up photo of a rectangular slice of flatbread or thin pizza on a white plate with red heart patterns. The flatbread is topped with fresh spinach leaves, sliced tomatoes, cubes of tofu in both light and dark varieties, and zucchini. A dollop of mustard-like spread is visible on the crust edge. In the background, part of another plate, a napkin, and a person’s arm are slightly out of focus.
September 15, 2025 at 2:04 PM
sponce1.bsky.social
📊 #TidyTuesday – 2025 W37 | Allrecipes
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2
Heat map showing recipe ratings by complexity, with cooking time on y-axis (≤30 min to 4+ hours) and ingredient count on x-axis (1-5 to 20+ ingredients). Color scale from dark blue (4.45 rating) to light green (4.63 rating) reveals higher ratings for recipes with more ingredients and longer cooking times. Each cell shows the average rating and recipe count from 14,426 AllRecipes.com recipes.
September 14, 2025 at 3:12 PM
sponce1.bsky.social
Here is my #viz for the #TidyTuesday challenge—W42. This one is about Southern Resident Killer Whale Encounters.
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🔗: stevenponce.netlify.app/data_visuali...
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Note: Chart inspired by Yan Holtz (@R_Graph_Gallery), see r-graph-gallery.com/414-map-mult...
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#rstats | #r4ds | #dataviz | #ggplot2
A bar chart displaying the number of orca encounters in the Salish Sea region, along with a map showing the locations of the encounters. Orcinus and Mike 1 are the vessels with the highest number of encounters, each exceeding 200.
October 15, 2024 at 12:55 PM
dslc.io
🤝We want to make it easier for you to help us! We'll streamline the #TidyTuesday curation process to give new people a chance to flex their data-wrangling muscles, and make it clearer how anybody can contribute to our github.com/r4ds projects

🧵6/7
March 14, 2024 at 12:40 PM
dslc.io
Recent DSLC.io club meetings:

🔵 R4DS: Factors youtu.be/xNwJIs8s_c4

From the aRchives:

🔵 R Packages: Cohort 5 Introduction youtu.be/iJahyHlPz0I

Visit dslc.video for hours of new #DataScience videos every week!

#dataBS #RStats #R4DS
R for Data Science: Factors (r4ds11 16)
YouTube video by Data Science Learning Community Videos
youtu.be
January 10, 2025 at 11:41 AM
nrennie.bsky.social
For #TidyTuesday this week, we're looking at Valentine's Day spending!

👀 make category labels human readable with {snakecase}
🎨 colour palette using {monochromeR}
📈 smoothed area plot with {ggstream}

Code: github.com/nrennie/tidy...

#RStats #R4DS #DataViz 📊
a proportion smoothed area chart showing the breakdown of total spending on Valentine's Day on categories: flowers, jewelry, other, evening out, greetings card, clothing, gift cards, and candy. Split between categories is shown to be reasonably constant between 2010 and 2022, with a slight increase in Other during the last few years. Categories are different shades of pink.
February 13, 2024 at 6:49 PM