iiinova — where your digital self keeps evolving

Reposted by Josephine Kaminaga

Hadley Wickham @hadley.nz · 06/11/2025

Do you teach #rstats? Do your students complain about how lame and old-fashioned dplyr is? Don't worry: I have the solution for you: github.com/hadley/genzp.... genzplyr is dplyr, but bussin fr fr no cap.

github.com

GitHub - hadley/genzplyr: dplyr but make it bussin fr fr no cap

dplyr but make it bussin fr fr no cap. Contribute to hadley/genzplyr development by creating an account on GitHub.

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Reposted by Josephine Kaminaga

Calle Börstell @cborstell.bsky.social · 03/11/2025

Flint water samples #TidyTuesday Bootstrapped water samples for estimating proportion of samples with dangerous levels of lead Code: github.com/borstell/tid... #R4DS #DataViz #ggplot2

A plot with the title "Lead concentration in Flint water samples in 2015", consisting of a distribution plot (left) and a map of Michigan with Flint marked, with a water pipe coming out of Flint ending in a faucet emoji. The subtitle contains a longer text explaining "In 2015, the Michigan Department of Environment (MDEQ) collected 71 water samples in Flint to evaluate the concentration of poisonous lead in the drinking water. The Lead and Copper Rule posits that if more than 10% of samples are above 15 parts per billion (ppb), action is required by officials. The MDEQ originally excluded two samples with high readings, thus resulting in below-threshold readings overall. Suspicious of the results, a citizen science project coordinated by Prof. Marc Edwards and colleagues at Virginia Tech collected 271 new samples, which pointed to generally higher levels of lead concentration than the official MDEQ data had suggested. The plot below shows the distribution of samples in relation to the 10% threshold when each dataset is resampled 10,000 times using bootstrapping (the MDEQ data bootstrapped both with and without the samples originally excluded). When including the originally excluded samples, both datasets would indicate that the Flint drinking water is likely to contain dangerous levels of lead at a rate which would require official action for public safety. In 2014, changes made to the water supply source in Flint, Michigan resulted in a public health crisis with many residents being exposed to dangerous levels of lead in their drinking water. Data: MDEQ & Virginia Tech in Loux & Gibson (2018) via TidyTuesday | Packages: {tidyverse, ggarrow, ggdist, marquee, patchwork, raturalearth, rsample, scales}| Visualization: C. Börstell"

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Rodrigo Barreiro @rodrigo404.bsky.social · 24/10/2025

#tidytuesday, on friday?! -- So many things to do with this week dataset! #r4ds #ggplot2 #dataviz

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Damie Pak @damiepak.bsky.social · 21/10/2025

Can you predict when Lou Bega's greatest hit, Mambo No. 5, was released based on the names of all the women mentioned in the song? Weirdly yes. yawpr.substack.com/p/project-a-... #databs #rstats

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Reposted by Josephine Kaminaga

Manasseh @manasseh6.bsky.social · 20/10/2025

Historic UK Meteorological & Climate Data for #TidyTuesday, Week 42. #rstats #dataviz #ggplot2

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bpiros.bsky.social @bpiros.bsky.social · 21/10/2025

This week #TidyTuesday explores meteorological data from the UK Met Office. I calculated yearly averages and the overall average for each variable, identified anomalies and created barcode charts to visualize the anomalies, where each bar represents a year." #pydytuesday #dataviz

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Reposted by Josephine Kaminaga

Nicola Rennie @nrennie.bsky.social · 20/10/2025

Here's the more traditional #DataViz plot! The data is this week's #TidyTuesday data from the @metoffice.gov.uk, where I focused on hours of sunshine per day in Leuchars! 🌤️ #RStats #ggplot2

Annotated barcode plot showing temperature in Leuchars for the 12 months of the year, with more variability in summer.

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Josephine Kaminaga @jkaminaga.bsky.social · 21/10/2025

Despite midterms, grad school apps, & research, I somehow found time to do #tidytuesday on time this week!! Looking at maximum mean temp. variation in the UK's hottest and coldest weather stations over the last 10 years, & practicing annotation! 🔗: github.com/jkaminags/TidyTuesday #rstats #dataviz

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Calle Börstell @cborstell.bsky.social · 20/10/2025

UK weather data #TidyTuesday Temperature changes over time and some individual temperature extremes highlighted on map 🌡🇬🇧 Code: github.com/borstell/tid... #DataViz ##ggplot2

A line chart showing temperature changes (daily min and max readings) from 22 weather stations across the UK. The line charts include model fit lines showing average temperatures across stations over time (1970 to 2024), showing the estimated average increase at the right margin (about +1.5 degrees Celsius). On the right of the line chart, there is map of the UK showing the location of each weather station. Four of them are marked with annotations showing the highest (Cambridge, 2008) and lowest (Newton Rigg, 2010) monthly means and the hottest January (Yeovilton, 1990) and coldest July (Lerwick, 1993). Data: UK Met Office via TidyTuesday; Packages: {tidyverse, geomtextpath, ggarrow, ggtext, glue, rnaturalearth, scales}; Visualization: C. Börstell.

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Josephine Kaminaga @jkaminaga.bsky.social · 18/10/2025

Suuuper late #tidytuesday - quick plot looking at trends in undernourishment & cereal-based diets around the world! Code on my github: github.com/jkaminags/TidyTuesday #rstats #dataviz

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Calle Börstell @cborstell.bsky.social · 06/10/2025

Euroleague Basketball #TidyTuesday A mini dataset, so decided to find a way to plot stadium capacity in an interesting way: went with points along the trajectory of a basketball shot! Swoosh! 🏀 Code: github.com/borstell/tid... #R4DS #DataViz

A plot that says Euroleague Basketball teams by stadium capacity on a offwhite background and black/gray text. Each data point is plotted as a basketball (emoji) and text label with the name of the team(s) playing at the arena, along a parabola simulating the trajectory of a basketball shot towards a schematic basketball hoop located in the bottom right corner. A caption reads: "There are 20 teams in Euroleague Basketball, playing at 19 unique arenas with a median capacity of 12,700 spectators. The biggest arena by capacity is Belgrade Arena which hosts the teams Crvena zvezda Meridianbet and Partizan and seats up to 18,386 people. The smallest arena is Salle Gaston Médecin which hosts Monaco and has a capacity of 5,000. Data: EuroLeague & Wikipedia via {TidyTuesday}; Packages: {tidyverse, ggrepel, ggtext, glue, scales} ; Visualization: C. Börstell."

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Reposted by Josephine Kaminaga

Manasseh @manasseh6.bsky.social · 01/10/2025

Crane Observations for #TidyTuesday, Week 39. Used a feather-inspired visualization 🪶, with each vane representing the average number of cranes observed daily in spring (7 March – 30 April) from 1994 to 2024. #rstats #dataviz #ggplot2

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Josephine Kaminaga @jkaminaga.bsky.social · 01/10/2025

This week's #tidytuesday - ridgeline plots with an attempt at spring and autumn-themed color gradients, showing the distribution of crane numbers at Lake Hornborgasjon between 2004 and 2024! Code at my github as always. #Rstats #dataviz

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Nicola Rennie @nrennie.bsky.social · 30/09/2025

A quick heatmap for #TidyTuesday this week, where we're looking at data on crane sightings in Sweden! Here, I'm using annotations instead of a traditional legend 📊 Thanks to @jenrichmondphd.bsky.social for curating the data! Code: github.com/nrennie/tidy... #RStats #ggplot2 #DataViz

Heatmap with year on x-axis, and dates from Mar-May on y-axis. Colour shows number of cranes observed, showing increasing number of cranes and earlier in the year.

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Nicola Rennie @nrennie.bsky.social · 02/09/2025

Super fun #TidyTuesday data all about frogs this week! 🐸 I decided to try to visualise the scientific names of the different frogs using a sunburst diagram 📊 Thanks to {ggiraph} - it's also interactive! Code: github.com/nrennie/tidy... #RStats #DataViz #ggplot2

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Josephine Kaminaga @jkaminaga.bsky.social · 30/08/2025

so deep into my graduate school applications that I started searching up what phd regalia looks like for the schools I'm applying to... maybe I need to take a break?

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Max Roser @maxroser.bsky.social · 22/08/2025

At Our World in Data, we spend much of our time counting deaths. But it’s just as important to know the number of lives saved — even though it is harder to estimate and involves much larger uncertainty. My Data Insight today includes this chart of some estimates.

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Josephine Kaminaga @jkaminaga.bsky.social · 20/08/2025

too busy with work stuff for tidytuesday this week… wish I could be my cat sometimes

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Thiago Krause @thiagokrause.bsky.social · 14/08/2025

As always, Ted Chiang is great in this interview. cdh.princeton.edu/blog/2025/08...

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Rodrigo Barreiro @rodrigo404.bsky.social · 13/08/2025

time for some #tidytuesday code at: barreiro-r.github.io/tidytuesday/ #dataviz #r4ds #ggplot2

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Josephine Kaminaga @jkaminaga.bsky.social · 13/08/2025

this week's #TidyTuesday - how has the burden of climate change-related extreme weather events been distributed across continents in the last 10 years? 🔗: [Github code](github.com/jkaminags/Ti...) #Rstats #dataviz

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Josephine Kaminaga @jkaminaga.bsky.social · 13/08/2025

US currently experiencing a summer wave as well :( stay safe and mask up 😷

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Adam Kucharski @adamjkucharski.bsky.social · 12/08/2025

"Whereas a small change in transmissibility conditions is enough to stop flu in summer, Sars-CoV-2 can basically evolve its way over this barrier" – I spoke to The Guardian about why COVID doesn't seem that constrained by season: www.theguardian.com/world/2025/a...

theguardian.com

What do we know about the Covid-19 virus five years on?

As the virus continues to evolve, experts assess its latest variants, seasonal prevalence and levels of vaccine uptake

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Georgios Karamanis @karaman.is · 10/08/2025

Income inequality for this week's #TidyTuesday. How do countries redistribute income through taxes and transfers? Code: github.com/gkaramanis/t... #RStats #dataviz

A horizontal line chart showing income inequality before and after government redistribution across 30 countries, organized by world regions. Each horizontal line represents one country, starting from market income inequality (right side) and ending at disposable income inequality after taxes and transfers (left side). The x-axis shows Gini coefficients from 0.2 to 0.7, where higher values indicate more inequality.

Countries are grouped into regions: Europe with 20 countries, North America (3), Asia (2), and one each from Oceania, South America, and Africa. The lines are colored by the ratio of redistribution effectiveness, with darker colors indicating countries that achieve greater inequality reduction.

Notable patterns include: Belgium achieves the most dramatic reduction, cutting inequality nearly in half from 0.49 to 0.26. In contrast, Dominican Republic shows minimal change from 0.52 to 0.52 (rounded). European countries generally show stronger redistribution effects than other regions. South Africa has both the highest initial inequality (0.71) and final inequality (0.62) despite some redistribution. Most countries reduce inequality to some degree, but the magnitude varies significantly even among countries with similar starting inequality levels.

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Josephine Kaminaga @jkaminaga.bsky.social · 08/08/2025

I heard it’s #InternationalCatDay? heres to my wonderful cat einstein (who is not as smart as his name implies)

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Josephine Kaminaga @jkaminaga.bsky.social · 05/08/2025

super simple shiny app for today's #TidyTuesday: how has post-tax income inequality reduction changed over time for 2020's most & least unequal countries? (really simple graphics, but wanted to practice shiny apps) code: bit.ly/40U8L45 shiny: jkaminaga.shinyapps.io/ginivisualiz... #dataviz #Rstats

Screenshot of a Shiny app displaying a dumbbell plot that compares pre and post tax Gini coefficients in 2020 for Colombia, Brazil, Paraguay, Mexico, Bulgaria, Sweden, Netherlands, Austria, Norway, and Belgium, as well as the mean overall Gini score for that year.

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Andrew Marder @andrewmarder.net · 03/08/2025

I'm excited to check these out, thank you! I don't have any personal recommendations, but you might enjoy this tool for exploring NPR's best books: andrewmarder.net/posts/books/ #dataviz

andrewmarder.net

Finding Good and Short Books

A data-driven approach to finding good (short) books.

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Josephine Kaminaga @jkaminaga.bsky.social · 02/08/2025

happy august! I did a little data visualization project this week trying to see what genres of books I've read and liked the most so far in 2025. (if you, like me, are a big fan of scifi + fantasy + dark academia, please give me book recs!) 🔗📚: github.com/jkaminags/bo... #ggplot2 #dataviz #Rstats

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Maarten van Smeden @maartenvsmeden.bsky.social · 18/11/2024

I made one for stats papers

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Josephine Kaminaga @jkaminaga.bsky.social · 29/07/2025

Had the great opportunity to do a #TidyTuesday at work - chose the cheese dataset from 2024 because I'm nothing if not a cheese lover & worked with maps in ggplot for the first time ever! Hoping to upgrade my visualizations in future TidyTuesday events. 🔗: bit.ly/4fgXWyU #Rstats #ggplot2 #dataviz

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