Christian Lawson-Perfect @christianp

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7 posts7 participants1 post today

**DataScienceLearningCommunity** @DSLC@fosstodon.org · 7h

DataScienceLearningCommunity @DSLC@fosstodon.org

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

**Jon Harmon** @jonthegeek@fosstodon.org · 1d

Jon Harmon @jonthegeek@fosstodon.org

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

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.

#RStats #PyData #JuliaLang

**Steven Ponce** @sponce1@graphics.social · 1d

Steven Ponce @sponce1@graphics.social

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).

**Steven Ponce** @sponce1@graphics.social · 3d

Steven Ponce @sponce1@graphics.social

A 2x2 grid showing 7-game rolling averages of 3-point shooting percentages for four NBA teams (Celtics, Suns, Thunder, and Timberwolves) during the 2023-2024 season. Each panel displays individual game data as gray dots with a colored trend line representing the rolling average.

**DSLC Videos** @DSLCvids@fosstodon.org · 4d

DSLC Videos @DSLCvids@fosstodon.org

Recent @DSLC club meetings:

R for Data Science: A field guide to base R https://youtu.be/XZoy3HWtkLI #RStats #R4DS

From the @DSLC chives:

"Advanced R: Vectors" https://youtu.be/mmcnkIjANps #RStats

:: "The Rust Programming Language: Getting Started & Introduction" https://youtu.be/DLG12TYJtkA #rustRustLangRustRustLang

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

youtu.be- YouTubeEnjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

**Steven Ponce** @sponce1@graphics.social · 5d

Steven Ponce @sponce1@graphics.social

#TidyTuesday – 2025 W15 | Base R Penguins
.
: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_15.html
.
#rstats | #r4ds | #dataviz | #ggplot2

A scatterplot showing bill dimensions (length vs. depth in mm) for three penguin species with density contours. The plot clearly displays three distinct clusters: Adelie penguins (orange, n=152) with shorter, deeper bills centered around 40mm length; Chinstrap penguins (blue, n=68) with medium-length bills around 49mm and greater depth; and Gentoo penguins (green, n=124) with longer, shallower bills around 48mm length and 15mm depth. Diamond markers indicate species means.

**Steven Ponce** @sponce1@graphics.social · 6d

Steven Ponce @sponce1@graphics.social

A scatter plot showing NBA players' True Shooting Percentage versus Usage Rate for the 2023-2024 season. Red dots highlight players with high usage but below-average efficiency (below 57%), labeled as the "Team Concern Area." Six notable inefficient high-usage players are labeled, including Anthony Edwards and DeAaron Fox. Blue dots represent all other players. The visualization demonstrates the negative relationship between offensive responsibility and scoring efficiency.

**DataScienceLearningCommunity** @DSLC@fosstodon.org · Apr 15

Apr 15

DataScienceLearningCommunity @DSLC@fosstodon.org

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/114336585969522119

#RStats #PyData #JuliaLang

**Steven Ponce** @sponce1@graphics.social · Apr 15

Apr 15

Steven Ponce @sponce1@graphics.social

Scatter plot showing NBA 2023-24 season data comparing assists versus turnovers per 36 minutes (correlation = 0.64). Players are divided into four categories: Efficient Playmakers, High-Risk Playmakers, Low-Usage Ball Handlers, and Turnover-Prone. Key players like Haliburton, Wembanyama, and James are labeled, illustrating the complicated relationship between playmaking and ball security.

**Jon Harmon** @jonthegeek@fosstodon.org · Apr 14

Apr 14

Jon Harmon @jonthegeek@fosstodon.org

Curator: @jonthegeek
https://DSLC.io welcomes you to week 15 of #TidyTuesday! We're exploring Base R Penguins!

https://tidytues.day/2025/2025-04-15
https://zenodo.org/records/14902740

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

Scatterplot titled 'Flipper and bill length' showing the relationship between flipper length (x-axis, in mm) and bill length (y-axis, in mm) for three penguin species at Palmer Station LTER. Adelie penguins are represented by orange circles with a slight positive trend line. Chinstrap penguins are shown with purple triangles, clustering higher on the bill length axis and a steeper trend line. Gentoo penguins are shown with dark green squares, positioned farther right along the flipper length axis, with their own upward trend line. A legend at the bottom right indicates the color and shape used for each species. The image is from the palmerpenguins R package.

#RStats #PyData #JuliaLang

**Steven Ponce** @sponce1@graphics.social · Apr 13

Apr 13

Steven Ponce @sponce1@graphics.social

A scatter plot showing the relationship between NBA players' defensive contributions (steals plus blocks per game) and 3-point shooting percentages. Four distinct clusters identified through K-means clustering reveal player archetypes: Two-Way Stars (upper right), Defensive Specialists (upper left), Offensive Specialists (lower right), and Role Players (lower center). Notable players like Victor Wembanyama stand out as elite two-way performers. Dashed lines indicate league averages for both metrics, creating quadrants with annotations describing each player archetype's characteristics.

**Steven Ponce** @sponce1@graphics.social · Apr 12

Apr 12

Steven Ponce @sponce1@graphics.social

A line graph showing Empirical Cumulative Distribution Functions (ECDF) of Annual Average Daily Traffic across ten California counties. The x-axis shows traffic volume from 100 to over 100,000 vehicles on a logarithmic scale. Horizontal dashed lines mark the 25th, 50th, and 75th percentiles. Urban counties like Los Angeles (LA) and Orange (ORA) show curves shifted right, indicating higher traffic volumes. In contrast, rural counties like Tulare (TUL) show curves shifted left, indicating lower traffic volumes.

**DSLC Videos** @DSLCvids@fosstodon.org · Apr 11

Apr 11

DSLC Videos @DSLCvids@fosstodon.org

Recent @DSLC club meetings:

R for Data Science: Iteration https://youtu.be/WOhgvtegPXU #RStats #R4DS

From the @DSLC chives:

"Introduction to Probability: Introduction" https://youtu.be/QoMwTfmHaYE #RStats

"ISLR: Unsupervised Learning" https://youtu.be/bfOfGLEO_GQ #RStats

"Generative AI Handbook: Chapters 9, 10" https://youtu.be/D5gPrijHQhA #RStats

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

**Steven Ponce** @sponce1@graphics.social · Apr 11

Apr 11

Steven Ponce @sponce1@graphics.social

A dotplot showing the distribution of assist-to-turnover ratios for NBA teams in the 2024 season. Teams are sorted vertically by median ratio, with Jazz at the top and Celtics at the bottom. Purple circles represent players, with larger circles indicating more players with that specific ratio value. Most players cluster between ratios of 1.0-2.5. Alternating gray background stripes highlight each team row. A vertical dashed line marks the league median.

**Steven Ponce** @sponce1@graphics.social · Apr 10

Apr 10

Steven Ponce @sponce1@graphics.social

Ridge plot showing the distribution of offensive rebounding ratios across NBA positions. Multi-modal patterns are visible, with annotations highlighting different player types: Defensive-focused and Balanced Power Forwards, Perimeter and Inside Forwards, and Traditional and Offensive-focused Centers. Centers show highest ratios around 0.4, while guards have lower ratios centered around 0.2.

**Steven Ponce** @sponce1@graphics.social · Apr 9

Apr 9

Steven Ponce @sponce1@graphics.social

A diverging bar chart titled "NBA 2023-24: Players with the Highest and Lowest Impact" showing Plus/Minus statistics for NBA players. The horizontal bars extend right (green) for positive impact and left (red) for negative impact. Nikola Jokić leads with +682, while Miles Bridges has the lowest at -633. The visualization includes 50 players who played minimum 15 minutes per game for at least 40 games.

**DataScienceLearningCommunity** @DSLC@fosstodon.org · Apr 8

Apr 8

DataScienceLearningCommunity @DSLC@fosstodon.org

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/114297010745251048

#RStats #PyData #JuliaLang

**Steven Ponce** @sponce1@graphics.social · Apr 8

Apr 8

Steven Ponce @sponce1@graphics.social

A histogram showing the distribution of points per game for NBA players in the 2023-24 season, comparing starters versus bench players. Bench players (orange bars) are concentrated at lower scoring ranges with a mean of 5.3 PPG, while starters (blue bars) show a broader distribution with higher scoring averages and a mean of 14.5 PPG. Vertical dashed lines indicate the mean values for each group.

**Jon Harmon** @jonthegeek@fosstodon.org · Apr 7

Apr 7

Jon Harmon @jonthegeek@fosstodon.org

Curator: @jonthegeek
https://DSLC.io welcomes you to week 14 of #TidyTuesday! We're exploring Timely and Effective Care by US State!

https://tidytues.day/2025/2025-04-08
https://www.visualcapitalist.com/mapped-emergency-room-visit-times-by-state/

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

Infographic titled 'Emergency Room Visit Times by State' from the Visual Capitalist, showing a map of the U.S. with each state color-coded based on average ER visit times, ranging from 1 hour 50 minutes for North Dakota to 5 hours 14 minutes for the District of Colombia. At the top, diverse individuals are seated in a waiting room. The source is the U.S. Centers for Medicare & Medicaid Services, 2024. More details about the graphic are available at https://www.voronoiapp.com/healthcare/People-in-DC-Spend-Over-5-Hours-On-Average-In-the-Emergency-Room--3590

#RStats #PyData #JuliaLang

**Steven Ponce** @sponce1@graphics.social · Apr 6

Apr 6

Steven Ponce @sponce1@graphics.social

A Florence Nightingale-inspired rose diagram comparing shooting efficiency metrics (Field Goal %, 3-Point %, Free Throw %, and True Shooting %) among three NBA stars: Joel Embiid (red), Nikola Jokic (blue), and Stephen Curry (teal). The diagram highlights Curry's superior free throw percentage (92.3%), Jokic's leading field goal percentage (57.8%) and true shooting percentage (64.7%), and Curry's top 3-point percentage (40.6%).

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