For today's #30DayChartChallenge, I used the {marimekko} package 📦.
Day 0️⃣3️⃣ | Comparisons | Mosaic
Code available here: github.com/KittJonathan...
#RStats #tidyverse #code #datavis
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Hashtag
#Tidyverse
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Second day of the 2026 #30DayChartChallenge, tried the {ggpop} package.
Day 0️⃣2️⃣ | Comparisons | Pictogram
Code available here : github.com/KittJonathan...
#RStats #tidyverse #code #datavis
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The 2026 #30DayChartChallenge kicks off today!
For this year's challenge, I'll use the Palmer Penguins dataset as often as possible.
Day 0️⃣1️⃣ | Comparisons | Part-to-whole
Code available here : github.com/KittJonathan...
#RStats #tidyverse #code #datavis
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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
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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
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Fun video on the history of R:
m.youtube.com/watch?v=FjSP...
Only the forbidden B and F word are missing. May the chaos reign!
#rstats #tidyverse #baseR #python
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I want to make sure the certified #tidyverse and #shiny folks are still recognized.
If you've found yourself on this website in the past year or so, why did you visit it? What were you looking for?
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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
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A nested facet of fictious sales trends for departments (main groups, here Office, Services, and Tech) and product categories (sub group, here Paper, Pens, Cloud, Laptops, and Phones).
A set of 9 line charts with "shadow data" to enable comparison across small multiples. The line chart shows timelines of popularity for selected female names.
An nested facet with advanced styling: separator lines, table-like column headers, and color-encoding that matches the bars (showing population by country) that are colore by continent.
The announcement flyer for our MeetUp event, happening tomorrow 5 pm CET.
Sneak Peek Part 2 💜 💙
Only 1 day left until my talk for #RLadies Abuja!
I’ll use examples from our "#ggplot2 uncharted" course to showcase some tricks to create and refine small multiples 🧱
Sign up for the talk tomorrow (5 pm CET) 👇 www.meetup.com/rladies-abuj...
#rstats #dataviz #tidyverse
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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 collection of gauge charts each representing one digit from 0 to 9. Each gauge shows the probability that a digit is immediately followed by itself in the first million digits of pi. Arc length encodes the self-transition rate, with a white line marking the expected 10.00% baseline. The arc appears to range from 9% to 11%. All ten digits fall within 0.20 percentage points of the expected rate, supporting the conjecture that pi is a normal number.
@dslc.io welcomes you to week 12 of #TidyTuesday! We're exploring One Million Digits of Pi!
📂 https://tidytues.day/2026/2026-03-24
📰 www.jpl.nasa.gov/edu/news/how-many-decima...
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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A heatmap showing average ratings for the first 27 seasons of the Simpsons with several call-outs and annotations.
A scatter plot using the mpg data that features an R logo in the title and a colorful icon in the subtitle and as point indicators for the top 25% values in a scatter plot.
A linechart of Orange tree grwoth with titles styled as textboxes and a call-out box inside the panel. Direct labels next to the line replace a legend and make the chart easier to read.
The announcement flyer for our MeetUp event.
Sneak peek at my talk for #RLadies Abuja! 💜 💙
In the first section, I’ll use examples from our "#ggplot2 uncharted" course to showcase some tricks to enrich your charts with meaningful titles, annotations, and call-outs 📣
Sign up here: www.meetup.com/rladies-abuj...
#rstats #dataviz #tidyverse
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The tidyverse pipeline for energy data:
read_csv() → filter() → select() → mutate() → group_by() → summarise() → ggplot()
From raw OWID data to finished chart in ~20 lines.
energtx.com/visualizations
#RStats #tidyverse #ggplot2 #DataViz #TidyTuesday
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170,000+ energy records visualized with R.
ggplot2 handles it effortlessly. Filter, aggregate, plot — the tidyverse pipeline just works.
energtx.com/datasets
#RStats #ggplot2 #tidyverse #DataScience #TidyTuesday #rstats
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🎥 New recording available!
What’s new in {dplyr} 1.2.0?
In this R-Ladies Rome session, Isabella Velásquez shows when to use the new features and how they improve real data workflows.
Watch the talk 👇
rladiesrome.org/talks/2026/m...
#RLadiesRome #rstats #DataScience #Tidyverse #WomenInTech
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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.
Dozens of salmon swim in netted cage. Photo: "Rudolf Svensen"
@dslc.io welcomes you to week 11 of #TidyTuesday! We're exploring Salmonid Mortality Data!
📁 https://tidytues.day/2026/2026-03-17
📰 www.vetinst.no/arrangementer/lansering-...
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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💪 The mighty R library, ggmap, helps you visualize data geospatially.
👇 We teach you how to make great looking data with Stadia Maps.
docs.stadiamaps.com/tutorials/ge...
#RStats #DataViz #Geospatial #Tidyverse
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If you regularly create visualizations in R, the tidyplots package is worth exploring.
The figure below shows examples from the tidyplots website: tidyplots.org
I recently released a new Statistics Globe Hub module about tidyplots: statisticsglobe.com/hub
#rstats #datascience #tidyverse
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Looking to visualize distributions clearly and effectively? The ggdensity package enhances ggplot2 in R by simplifying the creation of appealing density plots.
Example taken from the official package website: jamesotto852.github.io/ggdensity/
#tidyverse #datastructure #dataviz #rstats #ggplot2
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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 chart from github.com/adamkucharski showing the distribution of estimated probabilities for different phrases, ranked by mean. The y-axis is labeled with the individual phrases, and the x-axis shows the probabily percent from 0 to 100%. Each point is an individual response, with the mean for each phrase shown as a hollow red circle, and the median for each phrase shown as a red diamond. 'Will Happen' is top with a median 100% and mean 98% probability, and 'Almost No Chance' is bottom with a median 2% and mean about 3.5% probability. 'Realistic Possibility', 'May Happen', 'Might Happen', and 'Could Happen' each have points spanning roughly the entire range from 0% to 100%.
@dslc.io welcomes you to week 10 of #TidyTuesday! We're exploring How likely is 'likely'?!
📂 https://tidytues.day/2026/2026-03-10
📰 https://adamkucharski.github.io/CAPphrase/
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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But, of course, anyone can learn anything! And tidyverse makes data science in R much easier.
Base R and tidyverse are not excludent, they are complementary.
#rstats #tidyverse
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Announcement for ICPSR Summer Program's online workshop on Social Science Data and Model Visualization 101 using R, scheduled May 18-22.
New to #DataVisualization? Learn R, #Tidyverse, and #Ggplot2 from scratch! Create stunning plots, maps, and #ModelVisualization for papers, presentations, and the web. No experience needed—just bring your laptop. For more info: myumi.ch/8qdx3
#SumProg26 #ICPSR #RStats
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mirai 2.6.1 is now on CRAN
Launching daemons on Posit Workbench wasn't working for you with mirai 2.6.0? We've fixed it! `http_config()` now authenticates correctly for all Workbench installation types.
Scale seamlessly from laptop to enterprise with mirai.
mirai.r-lib.org
#Rstats #Tidyverse
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So when you dput a tibble there is something that is *not* an attribute that is called row.names and that is a vector of length 2 where the first value always seems to be NA and the second usually seems to the -1 * nrow. What is that??? Is there a way to access it? #rstats #tidyverse
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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 photograph of a Hermann's tortoise, featuring a severe injury in her shell after a fall from 20 to 30 m high cliffs. She stands on rocky cliffs, with treetops in the background.
@dslc.io welcomes you to week 9 of #TidyTuesday! We're exploring Golem Grad Tortoise Data!
📁 https://tidytues.day/2026/2026-03-03
📰 https://onlinelibrary.wiley.com/doi/10.1111/ele.70296
#RStats #PyData #JuliaLang #DataViz #tidyverse #r4ds
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I'm thrilled to be a fellow at the #NICAR26 data journalism conference this year!
I'll also be coaching a hands-on session on #RStats data analysis + plotting using #tidyverse packages. 📊
Maybe I'll see you there! 👇🏽
schedules.ire.org/nicar-2026/#...
#ddj @ire.org
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The data.table equivalent to pipes is chaining which is just putting operations behind each other: dt[…][…]. You can read more about data.table and pipes here: www.r-bloggers.com/2024/01/pipi... #Rstats #Rladies #tidyverse #magrittr
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