nicdex @nicdex

2 posts2 participants0 posts today

**Nicola Rennie** @nrennie@fosstodon.org · 1d

Nicola Rennie @nrennie@fosstodon.org

For this week's #TidyTuesday, we're looking at healthcare efficiency in the USA I wanted to create a very minimalist data-art plot, with tooltips that give more information

Map grid from {geofacet}
Interactive tooltips with {ggiraph}
Styling with CSS

A square tile map of the United States shows how timely and effective emergency medical care is for stroke symptoms. Each square represents a U.S. state, shaded in varying tones of purple to indicate the percentage of stroke patients who received brain scan results within 45 minutes of arrival, between April 2023 and March 2024. The national average is 66%. A tooltip highlights New Mexico with 41%, noting this is below the national average. Data is from the Centers for Medicare and Medicaid Services, and the graphic is by @nrennie.

#DataViz #RStats #ggplot2

**Jon Harmon** @jonthegeek@fosstodon.org · 3d

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

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.

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

**Nicola Rennie** @nrennie@fosstodon.org · Apr 2

Apr 2

Nicola Rennie @nrennie@fosstodon.org

We're looking at Pokemon data for #TidyTuesday this week!

Stream plot showing speed and colour using {ggstream}
Image added with {ggimage}
Aiming for a minimalist, fun, and arty chart

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-04-01

Stream chart showing number of pokemon of different colour and the speeds they have

#RStats #DataViz #ggplot2

**Tan G.** @tangandhara.bsky.social@bsky.brid.gy · Apr 1

Apr 1

Tan G. @tangandhara.bsky.social@bsky.brid.gy

#30DayChartChallenge - Day 1: Fractions Everyone's talking about Trump and tariffs but how much longer do we have to put up with his crap? Sadly, we're just under 5% of the way through so have a long way to go. #rstats #dataviz #TidyTuesday

The Orange Peril
"How much of the Trump presidency do we have left?"
"We've survived 4.9% of Trump 2.0"
"But there's still 95.1% of Trump 2.0 remaining"

**Steven Ponce** @sponce1@graphics.social · Mar 31

Mar 31

Steven Ponce @sponce1@graphics.social

#TidyTuesday – 2025 W13 | Pokemon
.
: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_13.html
.
#rstats | #r4ds | #dataviz | #ggplot2

A beeswarm plot showing speed distribution across Pokémon types. Types are ordered by average speed, with Flying and Electric types at the bottom (fastest) and Rock near the top (slowest). Purple diamonds mark the mean speed for each type, and a vertical dashed line at 100 indicates the "Fast" threshold. Most Pokémon fall between 50-100 speed, with outliers reaching up to 150.

**David** @dmendsev@pixelfed.social · Mar 31

Mar 31

David @dmendsev@pixelfed.social

Week 13 of #tidytuesday. #pokemon. I was aiming to show how specific type of pokemons get stronger or weaker in each generation but not super convinced of the result
#rstats

a plot of the change in attack and defense averages thoughout pokemon generations for the pokemon types: fighting, water and steel. You can see how the average changes per generation and some examples of pokemons on those types

#rstats

**Jon Harmon** @jonthegeek@fosstodon.org · Mar 31

Mar 31

Jon Harmon @jonthegeek@fosstodon.org

https://DSLC.io welcomes you to week 13 of #TidyTuesday! We're exploring Pokemon!

https://tidytues.day/2025/2025-04-01
https://medium.com/@hanahshih46/pokemon-data-visualization-and-analysis-with-r-60970c8e37f4

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

Image shows faceted scatterplots of pokemon dataset. Attack is on the x-axis, and defense is on the y-axis. The primary pokemon color is indicated with the color of the points, and the charts are faceted by pokemon type. The breakdown highlights a somewhat linear relationship between attack and defense for most pokemon types. For the most part, they are correlated, with defense and attack increasing together. There is less variance between the two variables when type is "dragon", "electric", or "normal". Noticeable types that break this trend are "rock" type pokemon. In addition, there are a few outliers, such as a "bug" type with a near-zero attack and defense above 200 hp.

#RStats #PyData #JuliaLang

**Dan Oehm** @danoehm.bsky.social@bsky.brid.gy · Mar 27

Mar 27

Dan Oehm @danoehm.bsky.social@bsky.brid.gy

#TidyTuesday week 12: Amazon's Annual Reports References to 'AI' in Amazon's annual reports. #Rstats #Dataviz #ggplot2

A bar chart titled 'References to "AI" in Amazon's annual reports' tracks the frequency of 'AI' mentions per 10,000 words from 2005 to 2023. The chart notes that an average report contains 22,000 words (excluding stop words). Data values are highlighted for 2016 (1.4), 2017 (0.8), 2018 (0.9), 2019 (1.3), 2020 (1.4), 2021 (1.4), 2022 (3.8), and 2023 (18), showing a steep rise in 2023. Creator's social media handles and GitHub link are included at the bottom.

**Nicola Rennie** @nrennie@fosstodon.org · Mar 25

Mar 25

Nicola Rennie @nrennie@fosstodon.org

A simple, minimalist filled slope chart for this week's #TidyTuesday data about words found in Amazon's annual reports

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-03-25

Heavily inspired by this chart from @danoehm: https://raw.githubusercontent.com/doehm/tidytues/refs/heads/main/scripts/2023/week-23-energy/energy.png

The image compares the frequency of the words 'data' and 'websites' in Amazon’s annual reports from 2005 to 2020. 'Data' mentions increased from 33 to 69, while 'websites' dropped from 53 to 8. The graphic, credited to @nrennie, highlights Amazon’s shifting focus over time.

#RStats #DataViz #ggplot2

**Jon Harmon** @jonthegeek@fosstodon.org · Mar 24

Mar 24

Jon Harmon @jonthegeek@fosstodon.org

https://DSLC.io welcomes you to week 12 of #TidyTuesday! We're exploring Text Data From Amazon's Annual Reports!

https://tidytues.day/2025/2025-03-25
https://gregoryvdvinne.github.io/Text-Mining-Amazon-Budgets.html

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

A scatter plot comparing word frequencies in annual reports from 2005–2013 and 2014–2023. Each orange dot represents a word, with its position indicating how often it appeared in the two time periods. A dashed diagonal line represents equal frequency across both periods. Some words, such as 'cash,' 'tax,' and 'billion,' appear more frequently in the later period. Others, such as 'stock' and 'million,' appear less often in the later period. A note explains that 2014–2023 word counts are adjusted to account for the extra year in that period.

#RStats #PyData #JuliaLang

**Arnabp** @Arnabp@fosstodon.org · Mar 23 *

Mar 23 *

Arnabp @Arnabp@fosstodon.org

A long time since I did some basic EDA on the Tidy Tuesday data sets. But this time with Python on Jupyter Notebooks on 2025 Week 3 Himalayan Mountain Ranges.

Coming from knowledge of #RStats these datasets provide a perfect place to develop your data science skills on Python as well.

Take a look at the notebook in the link - https://github.com/arnabpanja/python-core-dev/blob/main/scripts-core/jupyter_notebooks/tt_2025_week_3.ipynb

#TidyTuesday #PydyTuesday #DataScience

**Víctor Gauto** @vhgauto@fosstodon.org · Mar 21

Mar 21

Víctor Gauto @vhgauto@fosstodon.org

#TidyTuesday Nombres más populares de gatos y perros en un refugio de California.

Script: https://vhgauto.github.io/tidytuesday/2025/semana_09.html

Dos figuras con íconos de un perro y un gato en el centro de cada panel. De los íconos surgen los 10 nombres más comunes para cada uno. Gatos: Luna, Oreo, Bella, Leo, Nala, Daisy, Baby, Lucy, Milo y Oliver. Perros: Max, Rocky, Coco, Bella, Luna, Blue, Buddy, Lola, Charlie y Bear.

#RStats #dataviz #datavisualization

**Nicola Rennie** @nrennie@fosstodon.org · Mar 20

Mar 20

Nicola Rennie @nrennie@fosstodon.org

A treemap about palm trees = a palmtreemap!

Data from {palmtrees} via #TidyTuesday
Treemap shows colours found in fruits
Width of treemap = number of species

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-03-18

The image is a treemap comparing fruit colors of palm tree species classified as 'Conspicuous' or 'Cryptic'. The left side represents 739 species with bright colors like red, orange, yellow, and pink, while the right side shows 1,060 species with darker, muted tones like black, brown, green, & grey.

#RStats #DataViz #ggplot2

Continued thread

**Andreas** @AndreasThinks@fosstodon.org · Mar 19

Mar 19

Andreas @AndreasThinks@fosstodon.org

3/
Slightly heart-warming to know my (bad) code went on to be useful to others... so when you're learning, make sure you build cool stuff to help, then share and open-source it to help others! And thanks to the #TidyTuesday community and team at @Posit for making it all so fun along the way.

**Andreas** @AndreasThinks@fosstodon.org · Mar 19

Mar 19

Andreas @AndreasThinks@fosstodon.org

Celebration thread 1/!

Years ago, I released (then immediately forgot about) PydyTuesday, a Python library to easily download #TidyTuesday datasets. Some nice folks recently told me they'd been finding it useful... and looking at the Pypi stats, turns out they're not alone!

**Steven Ponce** @sponce1@graphics.social · Mar 18

Mar 18

Steven Ponce @sponce1@graphics.social

#TidyTuesday – 2025 W11 | Palm Trees
.
: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2025/tt_2025_11.html
.
#rstats | #r4ds | #dataviz | #ggplot2

Alluvial diagram showing growth form patterns across palm subfamilies. The visualization flows from left to right, connecting palm subfamilies (Arecoideae, Calamoideae, Ceroxyloideae, and Coryphoideae) to growth forms (Erect, Non-erect, and both) to stem types (Solitary Stem, Clustered Stems, and both). The diagram reveals distinctive patterns: Arecoideae palms show diverse growth habits, Calamoideae are predominantly erect with clustered stems, Ceroxyloideae show a mix of growth forms, and Coryphoideae are mainly non-erect with solitary stems. The flow width represents the percentage of species with each characteristic.

**Paul Matthews** @paulusm@scholar.social · Mar 17

Mar 17

Paul Matthews @paulusm@scholar.social

#TidyTuesday Decided to add in worldwide box office revenues and costs from WIkidata (accuracy of costs uncertain), to look at margins over time. Declining trend, but still making

Scatter plot of profit margins against release date for Pixar films. Shows a gradual decrease, but perhaps accentuated by the success of first Toy Story. But even the low grossers remain hugely profitable.

**Aleksandra Lazić** @alelazic@mastodon.social · Mar 17

Mar 17

Aleksandra Lazić @alelazic@mastodon.social

Like I don't have better things to do… But I've been meaning to organize my R data viz in one place. So someday, I thought I'd start retroactively sharing them for #TidyTuesday. In the end, I'd have a GitHub repo & a nice wiki. Some were basic, others more creative…

Acceptable, #Rstats? Is #TidyTuesday only for big data or "real-world" datasets?