A two-panel data visualization titled "Repair Isn't the Problem — The System Is." The left panel is a scatter plot showing product categories by median repairability score (x-axis, 1–10) versus percentage successfully repaired (y-axis, 0–100%). A shaded amber region marks the "system constraint" zone — categories rated easy to fix (score ≥6) but repaired less than 50% of the time. Two rust-colored points, Computer Equipment/Phones and Display and Sound Equipment, fall in this zone near the 50% threshold. Slate-colored points for Textiles and Tools Non-Electric sit above 85% success despite similar repairability scores. The right panel is a horizontal bar chart showing the share of recorded failure reasons. "Spare parts unavailable" dominates at 65% (rust bar), followed by "Failure unidentified" at 26% and "Insufficient time" at 9% (gray bars). Together, the panels show that repair failure is driven by systemic constraints — primarily missing parts — rather than by volunteer skill or product difficulty. Data source: Repair Monitor (repaircafes.org).

📊 #TidyTuesday – 2026 W14 | Repair Cafes Worldwide
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

A scatter plot titled “Taller palm trees have more leaves” showing the relationship between maximum leaf number (x-axis) and maximum height in meters (y-axis). Each point represents a palm tree species. The points are widely scattered but show an upward trend, with a red regression line sloping upward from left to right. Most observations cluster at lower leaf counts (around 5–25 leaves) and lower heights (under 20 meters), with a few taller and leafier outliers reaching above 60 meters and around 70 leaves. A note in the top left reports a moderate positive correlation (R = 0.42, p < 0.001). A footer reads “TidyTuesday 2025 Week 11 | Data from ‘palmtrees’ R package.”

Day 4: Slope

Taller palm trees have more leaves

#30DayChartChallenge #TidyTuesday

year <- 2025
week <- 11

jenrichmond.github.io/charts26/202...

Marimekko (mosaic) chart titled "Africa's Linguistic Mosaic" with subtitle "Share of language family by region." The chart displays five horizontal rows representing African regions: North, West, Central, East, and Southern. Each row is subdivided into colored rectangular segments whose widths represent the proportional share of language-family-to-country pairs within that region. The color-coded language families, shown in a legend at the bottom, are: Ubangian (dark purple), Other families (medium purple), Nilo-Saharan (steel blue), Niger-Congo (teal/dark cyan), Kx'a (bright teal), Khoe-Kwadi (light green), Indo-European (yellow-green), and Afroasiatic (golden yellow). Row heights vary: West is the tallest, suggesting the largest share of language-country pairs; North is the shortest. Key patterns visible across rows: Niger-Congo (teal) dominates West, Central, and Southern Africa, occupying the largest segment in each. In the North row, Afroasiatic (yellow), Niger-Congo, and Nilo-Saharan (blue) share roughly equal thirds. East Africa shows a four-way split among Afroasiatic, Niger-Congo, Nilo-Saharan, and Other families. The Southern row features small segments for Afroasiatic, Indo-European, Khoe-Kwadi, and Kx'a on the left before a large Niger-Congo block. Nilo-Saharan appears as a notable segment in North, West, Central, and East rows. A source note at bottom right reads: "Data: Languages of Africa · TidyTuesday 2026-01-13. Row width represents the share of language-country pairs. #30DayChartChallenge 2026 · Ilya Kashnitsky @ikashnitsky.phd."

DAY 3 -- mosaic #30DayChartChallenge
I'm visualizing the diversity of African linguistic families 🌍
This was a #TidyTuesday dataset in week 3 of 2026.
🔗 #rstats code: github.com/ikashnitsky/...
🧙‍♂️ pplx: www.perplexity.ai/search/day-3...

Coastal Ocean Temperature by Depth for #TidyTuesday, wk 13.

#Rstats #Dataviz #ggplot2

A stacked bar chart titled “Gender Pay Gaps in Australia” showing that of 1,105 occupations, only 7% pay women more than men. The single vertical bar is mostly dark blue (men earn more than women) with a small red section at the top (women earn more than men). The y-axis is labeled “Number of occupations” and ranges up to about 1,250. The red portion represents a small minority compared to the large blue majority. A legend on the right distinguishes “Women > Men” (red) and “Men > Women” (dark blue). A footer notes “TidyTuesday 2018 Week 4 | Data from data.gov.au.

This year for the #30DayChartChallenge I am going to pick a #TidyTuesday dataset at random and use it to make a chart with #ggplot and then see if I can reproduce it with #tidyplots.

Day 1: Part-to-whole, Australian Salary data

year <- 2018
week <- 4

jenrichmond.github.io/charts26/202...

Heatmap of Temperatures in Nova Scotia Ocean

Quick Heatmap #TidyTuesday #PydyTuesday

Code here: pgdatavizandstats.netlify.app/data_visuali...

#TidyTuesday 2026-03-31 Coastal Ocean Temperature by Depth.
Code: gitlab.com/karel_fiser/...

#DataViz #RStats #ggplot2

My submission for #TidyTuesday this week was actually an interactive presentation via #Quarto.

The .qmd file, along with all the tables and scripts are now uploaded to my repo:

github.com/afrikaniz3d-...

#DataViz #Echarts4R

Inspired by this @karaman.is #TidyTuesday example to use his format to plot elevation rather than depth!

Here are three years of air temperature data across 1500 m of elevation in #ACG #CostaRica from sea level (outer band) to cloud forest (centre)

🙏 for the example!!

@gdfcf.bsky.social

A grid of small charts on a light grey background titled "Nova Scotia's Ocean Pulse." Rows represent years and columns represent months. Each panel shows ocean depth as concentric rings (shallower on the outside, deeper toward the centre) and mean water temperature as color using a green-yellow-red diverging palette. Summer months show warmer temperatures in the outer (shallow) rings fading to cooler temperatures at the centre. Winter months are predominantly cooler throughout. A small legend in the upper left explains that depth increases inward. Subtitle reads "Seven years of daily coastal temperature measurements at depths of 2, 5, 10, 15, 20, 30, and 40 metres, from the Centre for Marine Applied Research." Caption reads "Source: Centre for Marine Applied Research · Graphic: Georgios Karamanis."

This week's #TidyTuesday dataset is seven years of daily ocean temperature measurements from Nova Scotia's coastline, recorded at depths down to 40 metres by the Centre for Marine Applied Research

Code: github.com/gkaramanis/t...

#RStats #dataviz

It's #TidyTuesday y'all! Show us what you made on our Slack at https://dslc.io
#RStats #PyData #JuliaLang #RustLang #DataViz #DataScience #DataAnalytics #data #tidyverse #DataBS

A graph showing how the temperature in Nova Scotia's coastal waterways changes throughout the season and according to the depth of the measurement.

In this week’s #TidyTuesday data analysis project, I analyzed a set of ocean temperature readings from Nova Scotia and observed a really cool pattern: deeper water can actually be warmer than the surface during the winter. #RStats #DataViz

Happy Tuesday - Tidy Tuesday
#TightyWhities #bulge #muscledaddy #TidyTuesday

A multi-line time-series plot showing yearly ocean temperatures (in Celsius) across different ocean depths. The depths range from 2 meters up to 40 meters. The graph shows the at the beginning of the year (in Winter) the temperature of the ocean is relatively similar across different depths (approx 5 degrees Celsius). However, as we get closer to late Spring / early Summer (around May) the lines on the graph begin to diverge. Temperatures begin to rise more rapidly at shallower depths and (when compared to deeper depths) become hotter. For example, depths of less than 10 meters can reach temperatures of around 18 degrees Celsius, whereas depths of between 30 - 40 meters only reach temperatures of around 10 degrees Celsius.

#TidyTuesday explores ocean temperatures 🌊

The ocean doesn’t warm evenly - surface waters heat up quickly, while deeper layers lag behind.

🔗Code: github.com/C-Monaghan/t...

#rstats #dataviz #ggplot2

Timeline for variations in costal ocean temperature across depths of sensor in Canada from 2018 to 2026. There is prominent difference in temperature as we go deeper during summers.

The timeline of coastal ocean temperatures shows a prominent gradient during summers.

An interactive 3D graph using Apache Echarts.

Notebook: dataviz.manishdatt.com/posts/2635

#TidyTuesday #dataviz #Javascript

Line chart with color denoting year faceted by depth; x-axis is dates from January to December, and y-axis is temperature in Celsius degree.

My submission for #TidyTuesday, Week 13 on Coastal Water Temperature in Depth. I explore seasonal changes by year and depth.

Code: github.com/mitsuoxv/tid...

A graphic titled “Winter Labrador current inversions contrast with summer surface warming” presents findings from ocean temperature data (2018 - 2025). It includes two charts: a heatmap showing the average temperature difference between surface (2 m) and deeper (40 m) waters across months, where blue indicates warmer deeper water and red indicates cooler surface water; winter months (December - January) show the strongest inversions, while mid-year months trend warmer at the surface. The second chart is a line graph comparing temperature by depth for January and April, intersecting at 3.75 °C and 10 m, illustrating seasonal thermal inversion. Project and author details appear at the bottom.

This week's #TidyTuesday looks at ocean temperature readings off the coast of Nova Scotia.

Got to learn about the Labrador Current and that the ocean "breathes" 😮

#DataViz #RStats #Echarts4R

For #tidytuesday 2026-03-17 looking at Norwegian salmon mortality, there seems to be a clear temporal pattern when excess deaths occur for each species.

Cheeky title inspiration from the legendary @alex4salmon.bsky.social 🙂

#rstats #ggplot #dataviz

Bear with me as I go through a backlog of #tidytuesday after being on vacation.

2026-03-10 - probability phrases

#rstats #ggplot #dataviz

#TidyTuesday week 12 #Rstats

Distribution of first n digits of pi

code: github.com/b3m3bi/tidyt...

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 photo of the ocean from the shore. Small waves are breaking on a rocky beach in the foreground. The sun is shining from behind a few clouds on the horizon in an otherwise clear blue sky.

@dslc.io welcomes you to week 13 of #TidyTuesday! We're exploring Coastal Ocean Temperature by Depth!

📂 https://tidytues.day/2026/2026-03-31
📰 https://data.novascotia.ca/stories/s/a25g-piws

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

I point my students to the #tidytuesday tag and repo github.com/rfordatascie...

Scatter chart titled "Pi Filling" showing the first hundred digits of pi. Each digit (0–9) is plotted at its position along the x-axis and its value on the y-axis, with points colored by digit and sized by how often that digit appears in the first million digits of pi. All ten digits are nearly equally distributed in the full million, ranging from about 99,548 occurrences (digit 6) to 100,359 (digit 5). The points are scattered across all digit values with no discernible pattern, reflecting pi's apparent randomness.

For #TidyTuesday, a (somewhat experimental) visualization of the distribution of the first hundred digits of pi.

tidytuesday.seanlunsford.com/...

Two-panel visualization titled 'The Ocean Has a Memory.' Top panel: a heatmap showing multi-year average daily ocean temperature by depth (2m to 40m) and day of year at a Nova Scotia coastal monitoring station. Cold blue tones dominate winter and deeper depths; warm yellow tones appear at the surface from June through September, with stratification clearly visible as shallower depths warm faster. Bottom panel: a time series from 2018 to 2026 showing thermocline depth — the point of steepest temperature gradient — which shoals to 5–10 metres each summer and deepens or disappears in winter when the water column mixes. Scatter points at low opacity show daily variability; a loess smooth highlights the repeating annual pattern. An annotation notes that the thermocline shoals to approximately 5–10 metres in late summer as surface waters warm.

📊 #TidyTuesday – 2026 W13 | Coastal Ocean Temperature by Depth
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Bar chart; x-axis is digit from 0 to 9, y-axis is counts of 5 or more repeated same digits in one million digits of pi, and filled with repetition length from 5 to 7.

My submission for #TidyTuesday, Week 12 on One Million Digits of Pi. I explore digit distribution in 5 or more repeated same digits like 3333333.

Code: github.com/mitsuoxv/tid...

Let's try this again

What do the first 1000 digits of pi look like (I couldn't do 1 million because I prefer my laptop unmelted)

Code here: pgdatavizandstats.netlify.app/data_visuali...

#rstats #TidyTuesday

A horizontal bar chart on a light grey background showing how many digits of pi are needed for different purposes, on a log scale. Bars are light blue except the TidyTuesday dataset bar, which is orange. From top to bottom: Everyday math (5), Voyager 1 trajectory with error less than a finger width (16), Observable universe circumference within one hydrogen atom (38), Max any practical science ever needs (100), This TidyTuesday dataset (1 million), Google world record by Emma Haruka Iwao in 2019 (31.4 trillion), Guinness World Record by KIOXIA and Linus in 2025 (300 trillion), Unofficial record by StorageReview in 2025 (314 trillion). Each bar is labeled with its value. Title reads "HOW MANY DIGITS OF PI DO WE NEED?"

This week's #TidyTuesday dataset is a million digits of π, which is far more than anyone will ever use. The plot shows how many digits we actually need. Spoiler: science caps out way before the computers do

Code: github.com/gkaramanis/t...

#RStats #dataviz

#TidyTuesday 2026-03-24 One Million Digits of Pi.
Code: karel_fiser.gitlab.io/tidytuesday_...

#DataViz #RStats #ggplot2

3D bar chart of 2,000 digits of π arranged in a spiral, rendered with the rayshader r-package. Each bar's height and color represent a digit from 0 to 9. Data source: tidytuesday 2026 week 12 - One Million Digits of Pi (Eve Anderson collection)

I wanted to create a maze using #TidyTuesday 2026 w10 data about π digits at first but ended up having fun with #rayshader

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

For #TidyTuesday this week, I explored different ways of estimating the number of digits of pi you would need to be confident that it contains every combination of n-digit sequences. Look at that relationship!

markmichael.dev/posts/2026-0...