James Adam @james

2 posts2 participants0 posts today

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

Nicola Rennie @nrennie@fosstodon.org

A quick plot for #TidyTuesday this week - celebrating the addition of the `penguins` data into base R I used {ggdist} to visualise the distribution of body weights with four different chart types in one!

I couldn't quite decide whether I prefer the light or dark version

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-04-15

Three box plot/density plot/rainfall plots showing body weight for three penguin species, with Gentoo significantly higher. Colours also show density, and species. Background is light with dark text.

#RStats #DataViz #ggplot2

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

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

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.

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

**Nicola Rennie** @nrennie@fosstodon.org · Apr 9

Apr 9

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 · 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

**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

**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

**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

**Jon Harmon** @jonthegeek@fosstodon.org · Mar 17

Mar 17

Jon Harmon @jonthegeek@fosstodon.org

Curator: @lydz_gibby
https://DSLC.io welcomes you to week 11 of #TidyTuesday! We're exploring Palm Trees!

https://tidytues.day/2025/2025-03-18
https://www.nature.com/articles/s41597-019-0189-0

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

Collage of 15 different palm trees showing variation in palm growth forms, steam and leaf variation, and fruit variation.

#RStats #PyData #JuliaLang

**Nicola Rennie** @nrennie@fosstodon.org · Mar 12

Mar 12

Nicola Rennie @nrennie@fosstodon.org

Did you know you can now access #TidyTuesday data in #Python as well as #RStats?

Here's a plot of this week's data about Pixar films made with Plotnine!

Import data using PyDyTuesday
Coloured subtitle text added with highlight_text
Fonts installed with pyfonts by Joseph Barbier

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-03-11

The chart compares critic ratings for 13 Pixar films released between 1995 and 2019, using scores from Rotten Tomatoes, Metacritic, and Critics Choice. Each movie is represented by three horizontal bars, showing variation in ratings across different review platforms. The data highlights a dip in scores for Cars 2 before a gradual recovery, with Toy Story and Toy Story 2 achieving the highest Rotten Tomatoes ratings.

#DataViz #ggplot2 #plotnine

**Nicola Rennie** @nrennie@fosstodon.org · Mar 11

Mar 11

Nicola Rennie @nrennie@fosstodon.org

New blog post about data art!

Have you ever wondered:

What is data-driven art?
Why do people make data art?
How do I get started making my own?

Then this blog post is for you! It includes an example using #TidyTuesday data - with code in both #Python and #RStats!

Link: https://nrennie.rbind.io/blog/data-driven-art/

Nicola RennieCreating data-driven art | Nicola RennieIf you've ever wondered what data-driven art is, or why people make it, then reading this blog post should help to answer those questions. It also includes an example of data-driven art, which can be created in Python or R, and talks through the process of getting started with your own art.

#DataViz #DataArt #Rtistry

**Nicola Rennie** @nrennie@fosstodon.org · Mar 11

Mar 11

Nicola Rennie @nrennie@fosstodon.org

I'm super happy to see that several of my data visualisation projects were long-listed at the 2024 Information is Beautiful Awards!

Check out the full longlist here: https://www.informationisbeautifulawards.com/showcase?action=index&award=2024&controller=showcase&page=1&pcategory=long-list&type=awards

Horizon chart showing ratings of Power Rangers over time, with colours chosen to reflect the characters

Stream chart showing the use of different words in horror tale descriptions, with many relating to women and children.

3x2 grid of abstract maps of Edinburgh and Glasgow showing elevation, streets, and buildings

GIF

#RStats #DataViz #ggplot2

**Jon Harmon** @jonthegeek@fosstodon.org · Mar 10

Mar 10

Jon Harmon @jonthegeek@fosstodon.org

Curator: @jonthegeek
https://DSLC.io welcomes you to week 10 of #TidyTuesday! We're exploring Pixar Films!

https://tidytues.day/2025/2025-03-11
https://erictleung.com/pixarfilms/articles/pixar_film_ratings.html

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

#RStats #PyData #JuliaLang

**Nicola Rennie** @nrennie@fosstodon.org · Mar 7 *

Mar 7 *

Nicola Rennie @nrennie@fosstodon.org

This week's #TidyTuesday data looked at animals at Long Beach animal rescue center and I focused in on the patterns of when owners surrender their dogs

Aim: show higher resolution (daily) data alongside aggregate counts
Why: view temporal patterns on different scales at the same time
How: lollipop chart + background heatmap

Code: https://github.com/nrennie/tidytuesday/tree/main/2025/2025-03-04