An arrow chart showing the 10 biggest improvements and 10 biggest declines in RSF Press Freedom rankings between 2015 and 2025. Blue arrows point leftward — from a higher 2015 rank to a lower 2025 rank — indicating improvement in press freedom. Burgundy arrows point to the right, indicating decline. Top improvers include Gambia (+93 places), Montenegro, and North Macedonia. The biggest decliners include Nicaragua (−98 places), El Salvador, and Hong Kong. The x-axis runs from rank 1 (most free) on the left to rank 180 (least free) on the right. A gray dot marks each country's 2015 starting rank.

📊 #30DayChartChallenge 2026 – day 06
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Comparisons | Data Day — Reporters Without Borders
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Barcode strip plot showing the carbon intensity of electricity (gCO₂ per kWh, 2020–2023 average) for countries grouped by World Bank income classification. Each vertical tick represents one country. A taupe band marks the middle 50% (IQR) and a white-filled dot marks the group median. Groups are sorted from lowest to highest median: High income (250 g), Low income (256 g), Upper middle income (454 g), and Lower middle income (481 g). A dashed vertical line marks the global median. Notable outliers include Norway and Poland (High income), Ethiopia and Syria (Low income), Albania and Turkmenistan (Upper middle income), and Nepal and Uzbekistan (Lower middle income). Within-group variation is wide across all four groups, often rivaling differences between groups.

📊 #30DayChartChallenge 2026 – day 05
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Comparisons | Experimental
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

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

Slope chart showing carbon intensity of electricity generation (gCO₂/kWh) for 20 countries, comparing 2020 to 2024. Three countries are highlighted in green as the largest absolute improvers: Chile (−172 g), Poland (−126 g), and Australia (−88 g). A dashed blue line marks the 100 gCO₂/kWh clean grid benchmark. France, Sweden, Norway, and Brazil already fall below this threshold in both years. All other countries are shown in gray and show modest or no improvement over the period.

📊 #30DayChartChallenge 2026 – day 04
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Comparisons | Slope
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

The mosaic chart shows the electricity generation mix by World Bank income group in 2022. Tile width represents each group's share of global output; tile height shows the energy source mix within that group. Upper-middle-income countries — dominated by China — produce the largest share of global electricity at roughly 48%, with fossil fuels accounting for 31% of global output alone. A dashed vertical line marks the boundary between upper-middle and high-income countries, where the energy mix meaningfully diversifies. High-income countries account for about 39% of global production, with notable shares in wind, solar, hydro, and nuclear. Lower-middle-income countries produce around 12%, still heavily fossil-dependent. Low-income nations collectively produce less than 1% of global electricity and are annotated. Data source: Our World in Data, Energy Institute Statistical Review of World Energy.

📊 #30DayChartChallenge 2026 – day 03
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Comparisons | Mosaic
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Pictogram chart showing the electricity generation mix for 8 countries in 2023, with 10 icons per country, each representing 10% of generation. Icon type indicates the energy source — droplets for hydro, flames for gas, factory icons for coal, and others. Countries are ordered from cleanest to most fossil-heavy. Norway leads with 98% renewable energy (mostly hydro), while Saudi Arabia and India rely on 99% and 75% fossil fuels, respectively. France stands out mid-table with 65% nuclear electricity. Denmark shows a wind-dominant mix, and China and the United States display mixed fossil and clean profiles.

📊 #30DayChartChallenge 2026 – day 02
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Comparisons | Pictogram
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Waffle chart showing U.S. federal spending for fiscal year 2025, with 100 equal squares representing approximately $70 billion each, totaling $7.0 trillion. Social Security is the largest category at $1,560 billion (22 squares), followed by Medicare at $1,090 billion, Net Interest at $1,050 billion, Defense at $933 billion, Other Mandatory at $747 billion, Medicaid at $648 billion, Other Discretionary at $647 billion, and Veterans Affairs at $325 billion. The federal deficit was $1.8 trillion. Data source: Congressional Budget Office, Monthly Budget Review FY2025

📊 #30DayChartChallenge 2026 – day 01
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Comparisons | Part-to-Whole
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

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

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

A circle formed by many small tiles (as pixels) in the gold to orange color spectrum, resembling a sun, with the Pi symbol vaguely visible at the center.

1 million decimals of pi
#TidyTuesday

Polar coordinates of each digit as ordered tiles, digit value corresponding to color 🟡🌞
#R4DS #DataViz #ggplot2

Code: github.com/borstell/tid...

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

A dark navy background shows the random walk of π's first 10,000 digits as a gold fractal-like path. Each digit (0–9) maps to one of ten compass directions, producing a wandering trail with no apparent pattern. The path begins near the center-right (marked "Start – 3.14159…") and ends in the upper-left (marked "Step 10,000"), colored from near-black at the start to bright gold at the end. An inset in the upper-right shows the full 1,000,000-step walk as a dense cloud — drifting nowhere in particular, confirming π's statistical randomness at scale.

📊 #TidyTuesday – 2026 W12 | One Million Digits of Pi
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

A three-panel chart titled "Norway's Farmed Salmon Mortality Shows Little Improvement Since 2020." Panel A shows national median monthly mortality for Atlantic salmon from 2020 to 2025 with an interquartile range ribbon. The trend fluctuates seasonally but hovers near the 2020 baseline of roughly 0.5%, with no sustained downward movement. Panel B is a horizontal lollipop chart of average median mortality by Norwegian county. Vestland, Agder & Rogaland, and Møre og Romsdal sit above the national average in burgundy; Nordland, Troms, and Finnmark fall well below in slate blue; Trøndelag sits near the national average in gray. Panel C shows stacked area charts of monthly loss composition for Atlantic Salmon and Rainbow Trout from 2020 to 2025. Dead fish in burgundy dominate both species, accounting for roughly 75–90% of losses, with discarded, escaped, and other losses forming a smaller but persistent share at the base.

📊 #TidyTuesday – 2026 W11 | Salmonid Mortality
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

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

A black background with white/gray text with the title "Likelihood of likelihood phrases: As judged by 5000+ people in Kucharski (2026)". The proportional fill of each word corresponds to its median %-likelihood, from "Will Happen" and "Almost Certain" which are completely filled, to "Remote Chance", "Highly Unlikely" And "Almost No Chance" which are all almost completely unfilled. Data: Kucharski (2026); Packages: {tidyverse, marquee}; Visualization: C. Börstell.

Going very minimal for this week's #TidyTuesday looking at judged likelihood of likelihood-expressing phrases in English.

Code: github.com/borstell/tid...

#R4DS #ggplot2 #DataViz

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

A horizontal range chart titled "Lost in the Middle" showing how 5,174 respondents assigned numerical probabilities (0–100%) to 19 common probability phrases. Each row displays a phrase ordered from lowest to highest median estimate. The thick bar represents the middle 50% of responses (IQR) and thin lines show the 10th–90th percentile range. Phrases at the extremes — such as "Will Happen" (median 100%), "Almost Certain" (95%), and "Almost No Chance" (2%) — show narrow, tightly clustered bars in gray, indicating strong agreement. In contrast, "Realistic Possibility" (highlighted in deep burgundy, IQR spanning 25–75%) and three semantically similar phrases — "Might Happen," "May Happen," and "Could Happen" (shown in muted rose, all with median 40%) — display wide bars indicating substantial disagreement. "About Even" stands out as a single gray dot with zero spread, the only phrase on which respondents achieved perfect consensus at 50%.

📊 #TidyTuesday – 2026 W10 | How likely is 'likely'?
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Title: Female tortoises were healthier in places with fewer males Subtitle: Average body condition of female and male Hermann's tortoises for three locations at Lake Prespa, North Macedonia: Beach (teal), Konjsko (yellow), and Plateau (brown) locations (Summer records only; no 2013 data). A higher score indicates a healthier animal. Circle size shows how many individuals were measured per timepoint. Notably, Plateau males consistently had the most records. Figure: Two horizontal line plots showing average body condition (y-axis) by year (x-axis), each with three lines corresponding to Beach (teal), Konjsko (yellow), and Plateau (brown) locations. The annual record counts is depicted as a circle overlaid on each line. Female body condition (upper plot) values for Konjsko are consistently higher than those for Beach and Plateau, demonstrated by the yellow line that is above and distinct from the teal and brown lines. Male body condition values for Konjsko lack this trend, demonstrated by the overlap between the lines for the three locations. The record count for males at Plateau are very large across all time points. Caption: Source: Sex Ratio Bias Triggers Demographic Suicide in a Dense Tortoise Population (Arsovski et al. 2026) | Graphic: [bluesky icon] morgangray [github icon] morethangray

A time series for #TidyTuesday showing annual trends in body condition for female and male tortoises

Code: github.com/morethangray...

#dataviz | #ggplot2 | #r4ds | #rstats

A three-panel data visualization titled "Too Many Males, Too Few Females" examines Hermann's tortoise captures from Golem Grad island (Lake Prespa) over 16 years. Panel A shows a line chart of the percentage of female captures on the island from 2008 to 2023, consistently hovering near 5–10% — far below the 50% parity reference line. Panel B displays violin plots comparing the body condition index of mainland versus island females; mainland females (median: 8.1, n=976) show notably higher body condition than island females (median: 6.3, n=268). Panel C presents a dot plot of clutch size by locality; island females average 3 eggs per clutch (n=22) compared to a mainland average of 6 eggs per clutch (n=31). Together, the panels illustrate a cause-to-consequence chain: extreme male bias on the island is associated with poorer female body condition and reduced reproductive output.

📊 #TidyTuesday – 2026 W09 | Golem Grad Tortoise Data
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

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

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 line plot showing the amount of funding (in € millions) Science Foundation Ireland contributed to Higher Education Authorities in Ireland from 2000 to 2025. A map of Ireland is overlayed on the plot as the background. Funding starts a little below €100m in 2000, peaks above €400m in 2020, and drops to €0 in 2025 after Science Foundation Ireland was dissolved and merged with the Irish Research Council to form Taighde Éireann - Research Ireland.

@dslc.io welcomes you to week 8 of #TidyTuesday! We're exploring Science Foundation Ireland Grants Commitments!

📁 https://tidytues.day/2026/2026-02-24
📰 data.gov.ie/dataset/science-foundati...

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

A two-panel time series (2001–2024) exploring Science Foundation Ireland's legacy. The top panel shows annual grant commitments as a teal area chart, peaking at €469M in 2019 before a sharp 2024 drop reflecting SFI's July dissolution. The bottom panel shows new institutions funded each year as a bar chart, with 2013–2017 highlighted in teal, during which 59 new institutions entered the ecosystem. Together, the panels argue that while SFI's funding fluctuated, its institutional reach grew steadily until the end. Note: totals reflect commitments by grant start year, not annual expenditure; 2024 is a partial year.

📊 #TidyTuesday – 2026 W08 | Science Foundation Ireland Grants Commitments
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

My first-ever #TidyTuesday! 🎉

Sheep ratio decreased. Crop production increased instead!

The plot tracks changes in production volume for each category, alongside their Compound Annual Growth Rate (CAGR). 📈🚜

Code here:
github.com/more-mm/tidy...
#MyFirstTidyTuesday #DataViz #r4ds #DSLC

A graph with the title "Sheep to people ratio in Aotearoa/NZ: Data from 1935–2024 by 5-year averages" on a light blue background with a pale green area resembling land. The x-axis shows years by 5-year increments and the y-axis shows the rounded number of sheep per people. Between 1935 and 1980, the ratio is about 20:1, but then rapidly dropping to about 5:1 in the 2020s. The top corner shows an outline of Aotearoa. Caption reads: "Packages: {tidyverse, marquee, patchwork, rnaturalearth, rvest}; Data: StatsNZ & Statista via TidyTuesday & Wikipedia; Visualization: C. Börstell"

Sheep to people ratio in Aotearoa/NZ for #TidyTuesday

🐑:👤 🇳🇿🌿

#R4DS #DataViz #ggplot2

Code: github.com/borstell/tid...

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.

Line chart of New Zealand's sheep-to-people ratio from 1950 to 2023. The ratio peaks at about 22 sheep per person in 1981 ("peak sheep") and then steadily declines, falling below 5 in 2022 and reaching 4.6 in 2023. Image credit: "https://sherwood.news/world/new-zealands-sheep-to-people-ratio-fell-again-in-2023/"

@dslc.io welcomes you to week 7 of #TidyTuesday! We're exploring Agricultural Production Statistics in New Zealand!

📁 https://tidytues.day/2026/2026-02-17
🗞️ www.rnz.co.nz/news/country/560252/gap-...

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

Line chart showing planted area in hectares for four New Zealand horticultural crops from 1982 to 2024. Wine grapes grew dramatically from 5,300 to 37,600 hectares, becoming the largest planted crop. Kiwifruit peaked in the late 1980s, then declined, only partially recovering to 14,500 hectares. Apples peaked in the mid-1990s and settled around 9,500 hectares. Avocados grew steadily from near zero to 4,300 hectares.

📊 #TidyTuesday – 2026 W07s | Agricultural Production Statistics in New Zealand
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

Grouped bar chart comparing medal event share by hour between Opening Days (gray bars) and Finals Week (teal bars) of the 2026 Winter Olympics. Finals Week shows dramatically higher medal event concentration in evening hours, with 4pm, 5pm, and 10pm reaching 100% medal events, while Opening Days shows lower, more scattered distribution throughout the day.

📊 #TidyTuesday – 2026 W06 | Winter Olympics!
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🔗: stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

A scatter plot in the shape of a curling sheet (lane) showing the number of medal events at the 2026 Winter Olympics. Each discipline is shown as a curling stone, in either red (snow) or yellow (ice), with its discipline name labeled next to it, and numbered by the number of medal events within the discipline. Freestyle Skiing is at the top with 45 events, followed by Speed Skating (42) and Cross-Country Skiing (35).

2026 Winter Olympics for #TidyTuesday

Which disciplines have the most medal events, and are they on ice or snow?
🧊❄️🏒⛷️🏂⛸️🛷

Weirdly tall plot for curling feel 🥌🎯 #R4DS #DataViz #ggplot2

Code: github.com/borstell/tid...

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 table of event counts by day. Each row is a sport, each column represents a date. The body of the table counts the number of events for each sport on a given day.

@dslc.io welcomes you to week 6 of #TidyTuesday! We're exploring This week we're getting ready for the 2026 Winter Olympics!!

📁 https://tidytues.day/2026/2026-02-10
🗞️ www.olympics.com/en/milano-cortina-2026/s...

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

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.

Minimalist map of Europe showing nine locations taking part in GROW projects, including locations in Scotland, Ireland, Portugal, Spain, Austria, Luxembourg, The Netherlands, Hungary, and Greece.

@dslc.io welcomes you to week 5 of #TidyTuesday! We're exploring Edible Plants Database!

📂 https://tidytues.day/2026/2026-02-03
📰 https://www.dundee.ac.uk/projects/grow-observatory

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