Jessica Moore

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.

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

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.

Honestly, choosing colours and fonts takes me longer than anything, and I’m not sure I always meet accessibility… but am working on it!
I’m sure you’re much better at it than you give yourself credit for ☺️

Wow, thank you so much!! 🥹🥰

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.

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

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.

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...

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

Scatterplot showing rating change (from August to September 2025) of FIDE-rated chess players. Rating gain and loss both peak before the age of 20.

#tidytuesday and #chess data is my favourite combo

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

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.

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.

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

#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...

This is awesome!

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

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

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.

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

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

Passport power by region: shows how many places passport holders can travel to without a visa - relative to how many types of passports can enter their country without one. A scatterplot grouped by world region, with each point plotted by relative travel freedom. The country with the most and least travel freedom for each region is labelled. Oceania: New Zealand has the most and Micronesia the least. Europe: UK has the most, Kosovo the least Americas: Canada the most, Suriname the least Africa: Seychelles the most, Madagascar the least Middle East: UAE the most, Palestinian Territory the least Asia: South Korea the most, Philippines the least Caribbean: St. Kitts and Nevis the most, Haiti the least

Henley passport index data for #tidytuesday week 36.

#dataviz #rstats

Australian Frogs 🐸🐸 for #TidyTuesday, Week 35.

#rstats #dataviz #ggplot2 #figma

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

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.

#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

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)

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/

Thank you!

Thank you so much!

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.

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

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).

#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

I’m sorry the world is like this. Trans people are people and matter as much as anyone else. 🩵

Thanks! I’m sure it could be done!

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

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...

I'm so sorry for everything you've been through and are going through 🩵🤍🩷 I see your posts and feel limited in how to help as a random stranger. I guess anything is better than silence.

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.

#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