Lmst

#R4ds

Curator: @noamross
https://DSLC.io welcomes you to week 18 of #TidyTuesday! We're exploring National Science Foundation Grant Terminations under the Trump Administration!

📂 https://tidytues.day/2025/2025-05-06
📰 https://www.nytimes.com/2025/04/22/science/trump-national-science-foundation-grants.html

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

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

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 word cloud of the most frequent terms in terminated NSF grant titles and abstracts, with words of different sizes, and different colors, with purple for the largest, red medium, and green small. "Black" stands out prominently in purple. In red, we see "climate", "student", "network", "justice", "identity", "teacher", and "undergraduate".

🎯 Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

🔗 https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode #DataSci #R4ds #blog #function #RProgramming

🎯 Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

🔗 https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode #DataSci #R4ds #blog #function #RProgramming

🎯 Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

🔗 https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode #DataSci #R4ds #blog #function #RProgramming

🎯 Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

🔗 https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode #DataSci #R4ds #blog #function #RProgramming

📊 #TidyTuesday – 2025 W18 | NSF Grant Terminations under the Trump Administration
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🔗: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_18.html
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#rstats | #r4ds | #dataviz | #ggplot2

Scatter plot showing analysis of 1,041 terminated NSF grants totaling $613.26M, representing 1293.5 years of lost research time. The visualization is divided into four quadrants showing different grant clusters: Early-stage Mega Grants (51 grants, 66% complete, 547 days left), Early-stage High-value Grants (382 grants, 81% complete, 236 days left), Late-stage High-value Grants (353 grants, 32% complete, 865 days left), and Late-stage Mid-value Grants (255 grants, 86% complete, 192 days left). Each cluster is represented in a different color (orange, teal, purple, and pink), with point size indicating days remaining. The y-axis shows the funding amount on a logarithmic scale, and the x-axis shows the percentage of grants completed.

Recent @DSLC club meetings:

:rstats: R for Data Science: Quarto & Quarto formats https://youtu.be/6BeeTW2BVvo #RStats #R4DS

From the @DSLC :rstats:chives:

:rstats: "RWTF: Project-oriented workflow" https://youtu.be/LxOsSswRBTs #RStats

:rstats: "Modelado Tidy con R - 19. ¿Cuando deberías confiar en las predicciones?" https://youtu.be/KtqSS9GixTs #RStats

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

📊 2025 #30DayChartChallenge | day 30 | uncertainties | national geographics (theme)
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_30.html
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#rstats | #r4ds | #dataviz | #ggplot2

A map showing uncertainty in Alaska glacier measurements. Three distinct regions of glaciers are labeled, with colors ranging from yellow (low uncertainty) to purple (high uncertainty). An information box explains that uncertainty is affected by distance from observation points, glacier size, and remote locations. The visualization has the distinctive yellow border of National Geographic style.

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!

RT @jonthegeek https://fosstodon.org/@jonthegeek/114415898182808932

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

📊 #TidyTuesday – 2025 W17 | useR! 2025 program
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🔗: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_17.html
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#rstats | #r4ds | #dataviz | #ggplot2

Topic-Speaker Bipartite Network for useR! 2025 Conference. The visualization shows connections between speakers (salmon-colored circles) and five topics (blue circles with labels): Visualization, Machine Learning, Data Wrangling, Package Development, and Reproducibility. The network clusters speakers around their respective topics, with some speakers positioned between multiple topics, indicating cross-disciplinary interests. The layout places topics centrally, with speakers radiating outward, creating a star-like pattern for each topic cluster.

📊 2025 #30DayChartChallenge | day 29 | uncertainties | extraterrestrial
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_29.html
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#rstats | #r4ds | #dataviz | #ggplot2

A halfeye plot showing meteorite mass distribution by class (H Chondrite, L Chondrite, Pallasite, and Iron) with confidence intervals. Each class is split by discovery type (Observed Fall in purple, Found Later in teal).

Curator: @minecr
https://DSLC.io welcomes you to week 17 of #TidyTuesday! We're exploring useR! 2025 program!

📂 https://tidytues.day/2025/2025-04-29
🗞️ https://user2025.r-project.org/

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

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

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.

Duke University Chapel Drive in the fall, with the Duke University Chapel in the background, some students walking on the quad in front of the chapel, and trees lining the drive up to the chapel.

📊 2025 #30DayChartChallenge | day 28 | uncertainties | inclusion
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_28.html
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#rstats | #r4ds | #dataviz | #ggplot2

A dot plot showing the gender gap in educational exclusion across regions. Sub-Saharan Africa has the largest gap (+8.2%) where females have less education, followed by Central and Southern Asia (+4.6%) and Northern Africa (+2.9%). Europe and Eastern Asia show minimal differences. Latin America (-0.3%) and Oceania (-0.5%) have slightly more males with less education. Error bars indicate statistical uncertainty. A dashed vertical line represents gender parity, with subtle background shading distinguishing areas where females (pink) or males (blue) are more excluded.

📊 2025 #30DayChartChallenge | day 27 | uncertainties | noise
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_27.html
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#rstats | #r4ds | #dataviz | #ggplot2

This S&P 500 price chart shows historical performance from 2022 to 2025. It features a smooth trend line (signal) overlaid with actual price movements (noise). A dashed vertical line marks the present, followed by forecast uncertainty bands at 50%, 80%, and 95% confidence levels. The signal-to-noise ratio is 7.5.

📊 2025 #30DayChartChallenge | day 26 | uncertainties | monochrome
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_26.html
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#rstats | #r4ds | #dataviz | #ggplot2

A dot plot showing cardiovascular disease prevalence across U.S. states and territories with 95% confidence intervals. States are ordered vertically from highest to lowest prevalence (West Virginia at 6.7% to Virgin Islands at 1.4%). Each state/territory has a dot showing its estimate and a horizontal line extending from the dot showing the confidence interval range. A vertical dashed line marks the national median of 4.0%. The visualization demonstrates how statistical uncertainty varies, with some regions having wider confidence intervals than others.

📊 2025 #30DayChartChallenge | day 25 | uncertainties | risk
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_25.html
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#rstats | #r4ds | #dataviz | #ggplot2

NBA Team Risk Matrix visualization showing team performance uncertainty vs. win percentage for 2023-24 season. Teams are plotted in four quadrants: high-risk/unpredictable (upper left), good but unpredictable (upper right), consistently poor (lower left), and consistently good (lower right). Teams are color-coded by point differential (red=negative, blue=positive) with circle size representing blowout risk. Dallas shows the highest variability, while Boston has the best performance.

📊 2025 #30DayChartChallenge | day 24 | timeseries | Word Health Organization (data)
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_24.html
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#rstats | #r4ds | #dataviz | #ggplot2

Time series line chart (1990-2022) showing gender differences in diabetes prevalence across regions. Europe shows men have nearly 40% higher rates than women (positive values). Africa shows women historically had up to 20% higher rates than men (negative values), though this gap has narrowed. Asia shows minimal gender differences, while Oceania and Americas show moderate differences with women having slightly higher rates.

📊 2025 #30DayChartChallenge | day 23 | timeseries | log scale
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_23.html
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#rstats | #r4ds | #dataviz | #ggplot2

Line chart showing pharmaceutical giants' stock performance from 2018-2025 on a logarithmic scale. The chart displays five companies (LLY, ABBV, MRK, BMY, PFE) with Eli Lilly (LLY) showing the strongest growth, reaching nearly $1,000 by 2025. The COVID-19 pandemic period is highlighted with a pink vertical band in 2020. All companies experienced volatility during this time, with different recovery patterns afterward.

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io/join (find the #chat-tidytuesday channel)!

RT @jonthegeek https://fosstodon.org/@jonthegeek/114376279976367936

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

📊 2025 #30DayChartChallenge | day 22 | timeseries | stars
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_22.html
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#rstats | #r4ds | #dataviz | #ggplot2

Time series visualization of Hubble Space Telescope observations from 2016-2024, showing the 10 most frequently observed celestial targets. Gold dots represent observations, with NGC104-WFC having the most consistent observations over time. M-51 shows a concentrated period of observations in 2017-2018, while NGC5139-WFC observations only begin around 2020.

Curator: @jonthegeek
https://DSLC.io welcomes you to week 16 of #TidyTuesday! We're exploring Fatal Car Crashes on 4/20!

📁 https://tidytues.day/2025/2025-04-22
📰 https://osf.io/preprints/osf/tzcsy_v1

Submit a dataset! https://github.com/rfordatascience/tidytuesday/blob/main/.github/CONTRIBUTING.md

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

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.

Two plots entitled Daily Mean Fatality Count, 1992-2016, and Daily Mean Fatality Count in April, 1992-2016. The top plot shows fatalities on the y-axis, and day of year on the x-axis. April 20 is marked with a dashed line, and does not appear to be significantly higher than other days of the year. Fatalities are much higher on January 1st, and generally higher in the months following April than at the beginning of the year. The second plot shows fatalities by day of the year within the month of April. The 20th (day 110 in the year) is approximately in the middle of fatality counts for April. Error bars on both plots are noted as representing 95% bootstrapped confidence intervals. Both plots were generated using code provided by @Rmadillo on GitHub.

📊 2025 #30DayChartChallenge | day 21 | timeseries | fossils
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🔗: https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_21.html
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#rstats | #r4ds | #dataviz | #ggplot2

Line graph showing the rise and fall of trilobite fossils over time (540-250 million years ago). The data peaks in the Ordovician period around 480-450 Ma, then sharply declines after the Ordovician Extinction (444 Ma), continues at lower levels through the Silurian period, and diminishes further after the Devonian Extinction (372 Ma).

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