iiinova — where your digital self keeps evolving

Reposted by Jessica Moore

Jon Harmon (he/him/his) @jonthegeek.com · 24/10/2025

I only need *5* more dataset curations (and to review those 5 plus the open PR) to be done curating #TidyTuesday for 2025! I can definitely do at least one myself, so I just need 4 of you to curate a dataset! Check out github.com/rfordatascie... for #RStats instructions.

github.com

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Reposted by Jessica Moore

Voilà: Francis Gagnon @chezvoila.com · 08/10/2025

This is really one of the best charts by @ourworldindata.org 📊 Amazing how much research and work goes into creating a chart like this. And it's such a good insight into society.

What Americans die from and the causes of death the US media reports on.

4 stacked bar charts. showing in short that while heart diseases and cancer constitutes 55% of the causes of death, they receive about 7% of the media coverages. Homicide is under 1% but receives between 42% and 52%. Terrorisme barely registers in the causes of death, but gets between 11% and 18%.

The first stacked bar is causes of death in the US in 2023
Heart diseases 29%
Cancer 26%
Accidents 9.5%
Stroke 6.9%
Lower respiratory diseases (6.2%)
Alzheimer's disease (4.8%)
Diabetes (4.0%)
Kidney failure (2.4%)
Liver disease (2.2%)
Suicide (2.1%)
COVID-19 (2.1%)
Influenza/Pneumonia (1.9%)
Drug overdose (1.8%)
Homicide (<1%)
Terrorism (<0.001%)

Media coverage of these causes of death in 2023 in...
New York Times
Heart disease (2.8%)
Cancer (4.1%)
Accidents (9.7%)
Suicide (3.8%)
COVID-19 (5.3%)
Drug overdose (7.5%)
Homicide (42%)
Terrorism (18%)

Washington Post
Heart disease (2.9%)
Cancer (4.7%)
Accidents (5.9%)
Suicide (3.3%)
COVID-19 (7.9%)
Drug overdose (9.5%)
Homicide (46%)
Terrorism (12%)

Fox News
Heart disease (2.3%)
Cancer (3.8%)
Accidents (6.1%)
Suicide (4.1%)
COVID-19 (6.0%)
Drug overdose (9.8%)
Homicide (52%)
Terrorism (11%)

Note: Based on the share of causes of death in the US and the share of mentions for each of the causes in the New York Times, the Washington Post and Fox News. All values are normalized to 100%, so the shares are relative to all deaths caused by the 12 most common causes + drug overdoses, homicides and terrorism. These causes account for more than 75% of deaths in the US.
A "media mention" is a published article in one of the outlets which mentions the cause (e.g. "influenza) or related keywords (e.g. "flu") least twice.
Data sources: Media mentions from Media Cloud (2025); deaths data from the US CDC (2025) and Global Terrorism Index.

Fox News

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Jessica Moore @jessimoore.bsky.social · 03/10/2025

#tidytuesday Arrival of the Cranes to Hornborgasjön in Spring (2014-2024).

Scatterplot of white text and points on a sky-blue background. Shows that the Spring arrival of cranes to Hornborgasjön begins around early-mid March, with the most number arriving around April 1st, and ends around mid-late April. The scatterplot points are in the shape of small white crosses with a slight shadow to give the appearance of a flock of birds.

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Reposted by Jessica Moore

Mitsuo Shiota @mitsuoxv.bsky.social · 23/09/2025

My submission for #TidyTuesday, Week 38 on FIDE Chess Player Ratings. I explore rating change from August to September 2025 by sex and k factor. Code: github.com/mitsuoxv/tid...

Scatterplots by sex and k factor; x-axis is ratings in August, and y-axis is ratings in September 2025. Each point is colored with year of birth of a player.

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Reposted by Jessica Moore

lls-d.bsky.social @lls-d.bsky.social · 23/09/2025

Had some fun with today's #TidyTuesday dataset! Here is an annotated streamgraph displaying chess players' age versus title.

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Reposted by Jessica Moore

bpiros.bsky.social @bpiros.bsky.social · 23/09/2025

This week #TidyTuesday explores the FIDE Chess Player Ratings. I selected the top 10 male and top 10 female players who saw the biggest jump in ratings and rankings from August to September and then presented them in a table with the great_tables library. #pydytuesday #dataviz

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Reposted by Jessica Moore

the surprise knock @everythingis42.bsky.social · 23/09/2025

it's midnight somewhere (ok, the next timezone over), so here is my first-ever #pydytuesday / #tidytuesday effort! I had maaaaaybe a little bit too much fun, given I've been trying to develop my charting style for stuff I'm doing at work.

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Reposted by Jessica Moore

Steven Ponce @sponce1.bsky.social · 21/09/2025

📊 #TidyTuesday – 2025 W38 | FIDE Chess Player Ratings . 🔗: stevenponce.netlify.app/data_visuali... . #rstats | #r4ds | #dataviz | #ggplot2

Multi-panel visualization showing chess player activity and achievements from FIDE data (August-September 2025). The top panel displays four histograms of game activity levels, showing that most players are casual (1-3 games) while fewer are highly active (16+ games). Bottom left shows top 20 rating improvements, led by Plzak, David, with +362 points, with a median of 255.5. Bottom right shows countries with the most titled players, led by Germany (1,004), Spain (702), and Russia (469), with a median of 391 players.

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Reposted by Jessica Moore

Nicholas Vietto @nvietto.bsky.social · 22/09/2025

#TidyTuesday Week 38 - FIDE Took what I know about ELO ratings and made a beeswarm 🐝 to compare Masters and players (like me) near the 1000 ELO mark. Also added a reference section to my script to 'cite' what scripts I looked at. Maybe it'll catch on #Rstats #dataviz Code github.com/nvietto/Tidy...

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Reposted by Jessica Moore

Calle Börstell @cborstell.bsky.social · 22/09/2025

Which countries have must rated chess players per age group? ♟️ India rising to the top in the youngest age groups. #TidyTuesday github.com/borstell/tid... #R4DS #DataViz #ggplot2

A plot titled "FIDE chess players by country & birth year: Ranking of the top International Chess Federation (FIDE) countries by the number of rated players (Elo rating ⩾1400) per age group (year of birth). Number of players shown under each flag (percentage of age group in brackets). Numbers under the flags show the number of players (with percentage of totals in brackets)". The plot resembles a chessboard, with a grayish purple background and the rankings being displayed as country flags on top of the chessboard's squares. In the oldest age brackets (left side), European countries are dominating with Germany, Spain and France having the most players. On the right side with the younger age groups, India is quickly rising to the top, in the youngest age group (2010—2021), Sri Lanka is also up-and-coming. Data: FIDE (September 2025) via TidyTuesday; Packages: {ggtext, tidyverse}; Visualization: C. Börstell

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Reposted by Jessica Moore

Nicola Rennie @nrennie.bsky.social · 22/09/2025

For this week's #TidyTuesday chess player rating data, I made an annotated barcode plot to show the distribution of age by title ♟️ It was hard to set a good transparency level for the lines since there's such a difference between the number of male and female players 📊 #RStats #DataViz #ggplot2

Barcode plots showing the distribution of age of grandmaster, international master, fide master, and candidate master for male and female chess players. Age seems to be less related to title for male players.

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Reposted by Jessica Moore

Libby Heeren @libbyheeren.bsky.social · 14/09/2025

I need you to know that your contribution, your voice, your perspective, your way of explaining something, YOUR whatever, is valuable. Just because "it's been done before" doesn't mean you shouldn't do it. Nothing is new, everything's been done! But not by you. Yet. #databs #rststs #python

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Reposted by Jessica Moore

Manasseh @manasseh6.bsky.social · 05/09/2025

Australian Frogs 🐸🐸 for #TidyTuesday, Week 35. #rstats #dataviz #ggplot2 #figma

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Jessica Moore @jessimoore.bsky.social · 02/09/2025

Frog calling patterns (as recorded by app users) for this week's #tidytuesday Highly recommend curating data if you haven't already - I thought it would be tricky but it was straightforward! Just follow these steps dslc-io.github.io/tidytuesdayR... Code: jessjep.github.io/blog/posts/t... #frogID

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Jessica Moore @jessimoore.bsky.social · 01/09/2025

#Tidytuesday on the last day of the week.. I explored the gender composition of Billboard #1 songwriters and artists over time. Code: jessjep.github.io/blog/posts/t... #figma #dataviz

Two stacked column charts are shown side by side with titles and legend in between.

The left chart shows the changing gender composition of number one songwriting teams over time - around 80% of teams were all-male from the 50s to the 90s, decreasing to under 50% since the 90s.

The right chart shows the changing gender composition of artists (who have achieved number one song) over time. Around 75% were all male in the 50s, down to around 50% in the 2020s.

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Reposted by Jessica Moore

Information is Beautiful @infobeautiful.bsky.social · 30/08/2025

Updated every month: Access the raw datasets behind every #dataviz we've ever released online. The IIB Data Room. 500+ sheets, 1200+ datasets. Continually updated. informationisbeautiful.net/data/

A table listing various article titles and their corresponding bit.ly links, sorted by publication date from August 2024 to May 2023. The table has four columns: Title, bit.ly link, Published date, and Updated (empty). Topics include 'Per Second', 'WaterWorld', several articles about plastics, 'Two Years of the Russia-Ukraine War', among others. Most links are either geni.us or Google Docs spreadsheet URLs. Dates range from most recent (01 Aug 2024) to oldest (24 May 2023)

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Jessica Moore @jessimoore.bsky.social · 22/08/2025

Learned a bit of Gaelic for this week's #tidytuesday. There are many names and variations of names for "mountain", "hill", "peak", "point", etc. I used this site to help me classify them: cuhwc.org.uk/resources/me... Code: jessjep.github.io/blog/posts/t... #ggplot2 #dataviz

One chart shows the height distributions of Scottish Munros and Munro Tops by their Gaelic name (Stob, Carn, Beinn, Sgurr and Meall). Stobs are tallest, on average, but have a wide distribution of different heights. In comparison, mealls are the shortest and tend to all be of a similar height.

The second chart shows the number of munros/munro tops by each name type. Beinns are most common and are usually munros rather than munro tops. Carns are second most common and are around 50% munros and 50% munro tops.

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Jessica Moore @jessimoore.bsky.social · 07/06/2025

#TidyTuesday week 22 - Project Gutenberg. A bar plot showing the authors with ebooks in the most number of languages. Code: jessjep.github.io/blog/posts/t... #ggplot2 #rstats #projectgutenberg

A bar plot showing the authors on Project Gutenberg who have works in the most number of languages. William Shakespeare is at the top of the plot with works in 14 languages, followed by Jules Verne at around 10. The bars are filled according to the century the author was active in (ranging from 8th century BCE (Homer) to the 19th century, which is the majority of the bars).

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Jessica Moore @jessimoore.bsky.social · 28/05/2025

It was nice to find some time for #TidyTuesday since starting a new job last month and having far less free time. As usual I may have overcomplicated things, but I plotted strength vs intelligence by alignment, and made an interactive version: jessjep.github.io/blog/posts/t...

Nine scatter plots of strength vs intelligence arranged in a 3x3 grid of the D&D alignments.
Some observations are:
- Strength and intelligence appear to correlate more for lawful or chaotic monsters than neutral monsters
- Humanoids are mostly true neutral
- Dragons can be good or evil, but are rarely neutral
- Neutral monsters tend to be smaller

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Jessica Moore @jessimoore.bsky.social · 21/04/2025

#TidyTuesday Week 16 - 420 Data. In the grand scheme of things, April 20 seems unimportant. Code: jessjep.github.io/blog/posts/t... #dataviz #rstats #ggplot2

A line plot showing the change in number of car accident fatalities in the USA from 1992 to 2017. The trend line has a wave pattern where there is an increase during the warmer months of the year (where the line is red) and a decrease during the colder months (where the line transitions to blue). Overall, the line is relatively flat. It increases very gradually until around 2006, where it then decreases to the lowest point in 2011, then gradually increasing again to similar levels as 1992.

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Jessica Moore @jessimoore.bsky.social · 09/04/2025

#30DayChartChallenge a little bit late for Day 7 - Outliers. All chess GMs are outliers really, but among his compatriots, Magnus Carlsen is truly leagues ahead. Tried out #tidyplots for this one :)

The image shows a boxplot overlaid with a beeswarm of data points in the shape of a violin. Each point represents a FIDE-rated chess player in Norway, placed according to their standard rating (the y axis). Where the points taper off at the top of the plot, there is a notable gap of around 200 rating points between the top point (Magnus Carlsen) and the next highest rated player.
The colors of the plot reflect the Norweigan flag: red, navy blue, and white.

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Jessica Moore @jessimoore.bsky.social · 06/04/2025

#30DayChartChallenge Day 6 - Florence Nightingale. Inspired by a family member's recent trip to the ER. This data is probably better presented as a line chart, but I got to practice using {patchwork} and coord_radial.

The image is titled "It's an Emergency! Please Wait..." and shows 8 circular bar charts, one for each Australian state and territory. The charts show the change in median wait time at emergency departments from 2020 to 2024. In WA and the NT, waiting times have approximately doubled, while in the ACT they have halved. In other states, they have remained relatively stable, with the shortest wait times in NSW and QLD.

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Jessica Moore @jessimoore.bsky.social · 05/04/2025

#30DayChartChallenge Day 5 - Ranking. Reposting a Tidy Tuesday from a couple of weeks ago :)

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Jessica Moore @jessimoore.bsky.social · 04/04/2025

#30DayChartChallenge Day 4 - Big or Small? First Nations people in Australia make up a small percentage of the total population. But their level of day-to-day psychological distress is disproportionately large.

A stacked bar chart comparing the proportion of First Nations Australians experiencing high to very high levels of psychological distress to that of the total population. Around a third of the First Nations bar is in maroon/red (high/very high distress) compared to about one-sixth of the All Australians bar.

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Jessica Moore @jessimoore.bsky.social · 03/04/2025

#30DayChartChallenge Day 3 - Circular. A follow-up to my slope chart. Women have a higher percentage of self-reported mental health problems, but they also more commonly seek help.

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Jessica Moore @jessimoore.bsky.social · 01/04/2025

#TidyTuesday - Week 13 - Pokemon. Not sure where I was going with this - I tried to get creative! The second image is an attempt at data art. It's the same data ordered by descending HP. Code: github.com/jessjep/tidy... #rstats #ggplot2 #dataart #pokemon

A plot titled "Pokemon Colors and Types". Seven rows, each representing a generation of pokemon, form a square. Each row is composed of colored lines (one for each pokemon), which are colored according to the type of pokemon. These lines are grouped by color, from cooler colors on the left (blue, purple, green) to warmer colors on the right (red, orange, pink, yellow).

A colourful square made up of seven rows, each showing the colours of a generation of pokemon. The colours blend between each row in a diamond-shaped transition effect.

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Jessica Moore @jessimoore.bsky.social · 27/03/2025

#TidyTuesday - Week 12 - Amazon Annual Reports. I looked at the frequency of common 3-word phrases (trigrams) over time. Code: github.com/jessjep/tidy... #rstats #dataviz #amazon

The image shows a line plot, titled "Term Frequency in Amazon's Annual Reports". It shows the relative term frequency (expressed as a percentage of total number of terms) of the trigrams "stock based compensation" and "foreign exchange rates" over time. Stock based compensation, in dark blue, starts at the top left of the plot (from 0.3% in 2005) and gradually declines to the bottom right (to under 0.1% in 2023), with the biggest decrease from 2015 to 2018. Foreign exchange rates, in yellow, starts at the bottom left (from under 0.05% in 2005) and increases sharply between 2014 and 2015, slightly increasing from there to 2023 (around 0.15%).

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Jessica Moore @jessimoore.bsky.social · 19/03/2025

#TidyTuesday - Week 11 - Palm Trees. I tried getting out of my comfort zone and creating a dendrogram. It took a lot of Googling and tutorials, but I'm happy with the result! Code: github.com/jessjep/tidy... #rstats #ggraph #dataviz

Title: "Palm Trees". Subtitle: "Over 2500 species of palms exist worldwide, categorized into 29 tribes across 5 subfamilies: Arecoideae, Calamoideae, Ceroxyloideae, Coryphoideae, and Nypoideae. In this diagram, the leaves are made up of thousands of lines, each representing a species of palm. Each leaf represents a palm tribe, and each colour a subfamily."

There is a circular shaped image over a mint-green background, with palm-shaped leaves branching out from the centre. Half of the leaves are a darker green, representing the Arecoideae subfamily. This is followed by the Calamoideae and Coryphoideae subfamilies, in different shades of green. The Ceroxyloideae and Nypoideae subfamilies make up the smallest portions, in teal and yellow-green, respectively.

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Jessica Moore @jessimoore.bsky.social · 13/03/2025

#TidyTuesday - Week 10 - Pixar. Fun Dataset! Code: github.com/jessjep/tidy... #rstats #ggplot2 #dataviz #pixar

"Pixar Film Rankings" shows a scatte rplot of the PIxar films on a baby blue background. The films are listed vertically on the left, ordered by highest ranked (Toy Story) to lowest (Cars 2). Each film has a horizontal line drawn from its title to its point (circle) on the plot, with longer lines representing higher rated films. The color and size of the circles represent the release date and run time of the films, respectively. These are colored from dark purple, to pink, to white. The Toy Story franchise stands out at the top of the list, while the bottom four films are Lightyear, Elemental, Cars 3, and Cars 2. Luca and Elemental stand out as the films with the longest duration (largest circles).

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Jessica Moore @jessimoore.bsky.social · 04/03/2025

#TidyTuesday - Week 9 - Long Beach Animal Rescue. I looked at the number of and reasons for owner surrender of their cats and dogs. Code: github.com/jessjep/tidy... #rstats #ggplot2 #dataviz

Titled "Reasons for Pet Surrender to Long Beach Animal Shelter". The plot shows two bars representing the number of dogs and cats that were surrendered by their owners. The dog bar is about a third longer than the cat one, with over 1300 dogs surrendered, compared to over 900 cats. The bars are subdivided by colors showing the number according to the reason they were surrendered. The largest portion in both bars is brown, representing 'unknown reasons', followed by light blue for 'owner-related reasons', and pink for 'other pet or pets'. For dogs, red for 'behavioral reasons' is the forth largest portion.

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Jessica Moore @jessimoore.bsky.social · 27/02/2025

#TidyTuesday - Week 8 - Racial Disparities in Reproductive Medicine Research Findings. I made a "caterpillar" plot showing effect sizes from studies that looked at mortality and morbidity. Code: github.com/jessjep/tidy... #rstats #ggplot2 #dataviz

Titled "Racial Disparities in Reproductive Medicine Research: White vs. Non-White Outcomes". The plot shows point estimates of odds ratios, risk ratios, and hazard ratios, along with confidence intervals, for each estimate from a total of 33 studies. These are represented as dots (point estimate) and a vertical line connecting the lower confidence interval to the upper confidence interval. The estimates and vertical lines are presented in ascending order of the point estimates. A dashed horizontal line shows where y = 1, which indicates that odds/risk/hazards were equal for the compared groups. The majority of the estimates shown are above this line, indicating greater risk for the comparison group. Statistical significance is shown in dark blue (non-significant is light blue), with a small number of dark blue lines towards the left of the plot (lower estimates, greater risk for White reference group), a larger number of light blue lines towards the left-middle of the plot, and an even larger number of darker blue lines curving upwards towards the right of the plot (above 1).

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Jessica Moore @jessimoore.bsky.social · 20/02/2025

#TidyTuesday - Week 7 - FBI Crime Data API. Some patterns emerge within states in terms of early/late agency adoption of the NIBRS. code: github.com/jessjep/tidy... #ggplot2 #dataviz

Titled "Agency Adoption of the National Incident-Based Reporting System (NIBRS)", subtitled "The NIBRS is the USA's national standard for law enforcement crime data reporting. It captures up to 57 data elements for individual crime incidents, allowing for a comprehensive understanding of crime in the country." The image shows a map of mainland USA over a medium-grey background, with scattered points representing the crime agencies across the country. The points are colored by the agencies' adoption date of the NIBRS, from dark blue/black (1991) to light blue/white (2023). Agencies that have not yet adopted the system are colored in pink. Clustered points show Tennessee, Virginia, South Carolina, Iowa, and Michigan to be early adopters of the system. Pink points cluster over New York, Connecticut, Pennsylvania, and Florida.

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Jessica Moore @jessimoore.bsky.social · 19/02/2025

I made my first #Tableau dashboard, looking at competitive chess players across the world. It is interactive and best viewed on desktop. 🔗: public.tableau.com/shared/3N45M... #fide #chess

The image shows a static version of an interactive Tableau dashboard. Titled 'Chess Players by Federation', there are three components to the dashboard: 1) a world map with the countries colored according do the number of active chess players they have (India, Central Europe, and Russia stand out in darker, warmer colors, as they have the most players); 2) a bar chart showing the number of titled players per country, colored by title and sorted by most to least (Germany, Spain, Russia, USA, India, France, Serbia, Hungary); 3) a bar chart showing the number of female players per country, sorted from most to least (India, Russia, France, Germany). The dashboard is cream colored, and chart colors are bright, summery greens, yellows, and pinks.

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Jessica Moore @jessimoore.bsky.social · 11/02/2025

#TidyTuesday - Week 6 - CDC Datasets. I looked at the categories of datasets that have been archived and visualised the counts using {treemapify}. code: github.com/jessjep/tidy... #rstats #dataviz #ggplot2

A visualisation titled "CDC Datasets backed up during Trump Administration". Shows a group of rectangles, which are sized, grouped, and colored according to the number of datasets in each category. The largest is in red and labelled "National Notifiable Disease Surveillance System (NDSS): 293". This is followed by "National Center for Health Statistics: 199", "National Institute for Occupational Safety and Health: 108", "Vaccinations: 78", "Public Health Surveillance: 68", and "500 Cities & Places: 57". As the number of datasets becomes smaller, the color changes in a sunset gradient from red to yellow to blue.

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Jessica Moore @jessimoore.bsky.social · 07/02/2025

#TidyTuesday - week 5 - The Simpsons: most spoken words by character. These colours hurt my eyes 😅 code: github.com/jessjep/tidy...

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Jessica Moore @jessimoore.bsky.social · 03/02/2025

I learned the basics of web scraping with R using the rvest package a few weeks ago. This visualisation was the result. Code: github.com/jessjep/Good... #ggplot2 #rstats #goodreads #fantasy

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Jessica Moore @jessimoore.bsky.social · 31/01/2025

#TidyTuesday - week 4 - Water Insecurity - and my first time sharing what I've made online. Plot was made using ggplot2, and I fine-tuned the graphics in Illustrator. Might share my code on GitHub later (after I clean it up a bit) :D #rstats #ggplot2

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