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rajodm.bsky.social
Rajo
@rajodm.bsky.social
120 followers 190 following 83 posts
#rstats | #dataviz
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rajodm.bsky.social
#TidyTuesday 2026, week 04

Most Brazilian enterprises cluster in the middle capital bracket, regardless of size in the CNPJ data.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
Treemap showing Brazilian enterprises grouped by size (Micro, Small, Other) and capital level. Box sizes represent company counts. Most enterprises across all size categories are in the middle capital bracket, with 36,433 micro, 19,262 small, and 17,169 other enterprises.
January 30, 2026 at 4:45 PM
Reposted by Rajo
albertocairo.com
The Open Visualization Academy launches on FRIDAY! openvisualizationacademy.org

More information in our newsletter: openvisualizationacademy.beehiiv.com/p/the-open-v...

#dataViz #dataVisualization #infographics #dataJournalism
Logo of the Open Visualization Academy, designed by Vinicius Sueiro
January 27, 2026 at 6:07 PM
rajodm.bsky.social
Tracking the most common terms in Astronomy Picture of the Day titles ✨ for week 3 of #TidyTuesday.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
A dot plot showing seasonal patterns in Astronomy Picture of the Day archive title terms. The y-axis lists frequent astronomical terms (fifteen most popular by month). The x-axis shows months January through December. Each dot represents usage frequency (larger dots = more appearances) and color indicates the most recent year of publication (ranging from yellow for 2019 to dark blue/black for 2024-2025). Notable patterns include: Geminids appearing prominently in December, Perseids peaking in August, and terms like 'Moon,' 'Nebula,' 'Stars,' and 'Galaxies' appearing consistently across all months with large dots. Annotations highlight the Perseids meteor shower peaking August 12-13, Comet Neowise's closest approach July 3, 2020, and Geminids meteor shower peaking December 14.
January 19, 2026 at 5:02 PM
rajodm.bsky.social
#TidyTuesday 2026, week 02.

Mapping the dominant language family in each African country.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
A choropleth map of Africa showing the dominant language family by country. Niger-Congo (blue) dominates most of sub-Saharan Africa, particularly in West, Central, and Southern regions. Afro-Asiatic (orange) is prevalent across North Africa and the Horn of Africa. Nilo-Saharan (green) is found in parts of East-Central Africa including Chad, South Sudan, and Uganda. Madagascar shows Austronesian (pink), the only country with this family. Small island nations show European language dominance: Cape Verde (Portuguese), Seychelles (French), and Mauritius (Indo-European). Some countries show no data (gray).
January 13, 2026 at 10:41 AM
rajodm.bsky.social
#TidyTuesday 2026, Week 01: Tracking mpox search trends in Madagascar with {gtrendsR}.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
Line graph showing Google Trends search interest for mpox-related terms in Madagascar from December 25, 2025 to January 9, 2026. Three lines track searches for 'mpox' (blue), 'variole du singe' (green), and 'monkeypox' (orange). Search interest began rising around December 28, peaked on December 30 ('variole du singe' and 'mpox'), then declined after the first officially confirmed cases on December 31, 2025 (marked by a vertical dashed line). Interest rose again in early January. Below the graph, three treemaps show top related search queries organized by the three main terms, with related searches including 'symptômes', 'traitement', 'vaccin', 'transmission', 'virus', and 'madagascar'.
January 9, 2026 at 9:36 PM
Reposted by Rajo
jonthegeek.com
Happy 2026! 🎆

My resolution: put @dslc.io on a sustainable footing.

I’m launching a Patreon to cover costs (Zoom, hosting, etc). If you love #TidyTuesday, our Slack help, or our book clubs, please consider helping us keep the lights on!

patreon.com/DSLC

#RStats #DataBS
Data Science Learning Community | Patreon
Tools to learn data science, e.g. weekly TidyTues.day dataset
patreon.com
January 5, 2026 at 2:29 PM
rajodm.bsky.social
Combined Day 20 (water) and Day 24 (places and their names) for this one.

Code: github.com/rajodm/30Day...

#30DayMapChallenge #dataviz #rstats #rayshader
Bathymetric map of the Great Barrier Reef (located in northeastern Australia and an UNESCO world heritage site) showing underwater topography through color gradients and contour lines. The map clearly shows the continental shelf edge where the reef platform drops dramatically into the deep Coral Sea.
November 24, 2025 at 9:45 AM
rajodm.bsky.social
#30DayMapChallenge - Day 18: Out of This World

Some planets in a galaxy far, far away 🌌

Code: github.com/rajodm/30Day...

#dataviz #rstats #ggplot2
Star Wars galaxy map showing major planets like Tatooine, Coruscant, and Hoth (represented by white dots), connected by hyperlane routes (yellow lines) across different quadrants (the interior, new territories, the slice, trailing sector, western reaches and the unknown regions)
November 17, 2025 at 8:36 PM
rajodm.bsky.social
Santa Claus' hometown for #30DayMapChallenge day 14 - Data: OpenStreetMap.

Code: github.com/rajodm/30Day...

#dataviz #rstats #ggplot2
Map of Rovaniemi, Finland showing street network in dark gray, waterways in blue-gray, and green spaces in light green on a warm off-white background.
November 16, 2025 at 12:44 PM
Reposted by Rajo
dgkeyes.com
Post is up! #rstats

rfortherestofus.com/2025/11/quar...
November 12, 2025 at 11:31 PM
Reposted by Rajo
jakubnowosad.com
tmap or ggplot2 for maps? 🗺️

David O’Sullivan breaks down the trade-offs in a blog post.

URL: dosull.github.io/posts/2024-1...

#RStats #RSpatial #Maps #tmap #ggplot2
tmap vs. ggplot2 for mapping – Geospatial Stuff
For me at least the choice between ggplot2 and tmap is an ongoing question. Here are my latest thoughts on the subject (with code).
dosull.github.io
November 12, 2025 at 2:02 PM
rajodm.bsky.social
Re-up because of an error in the legend 😅.

Late entry for Day 10 of the #30DayMapChallenge - Air.
Mapping average wind speed from November 5, 2025.

Code: github.com/rajodm/30Day...

#dataviz #rstats #ggplot2
World map showing average wind speed from November 5, 2025, with directional lines (arrows) in blue gradients. Wind speeds ranging from less than 5 m/s (darker blue) to around 20 m/s (lighter).
November 12, 2025 at 7:00 AM
Reposted by Rajo
heimarthur.bsky.social
rstudio.github.io/cheatsheets/...

Cheers
rstudio.github.io
November 8, 2025 at 12:36 PM
Reposted by Rajo
davisvaughan.bsky.social
We are looking for #rstats community feedback on 3 new dplyr functions!

We're aiming to expand the `filter()` family:

- `filter()` to keep rows
- `filter_out()` to drop rows
- `when_any()` and `when_all()` as modifiers

Read more and leave feedback here:
github.com/tidyverse/ti...
Example of using `filter_out()` on the `penguins` dataset, showing how it is much easier than `filter()`, especially with `NA`s
November 7, 2025 at 4:03 PM
rajodm.bsky.social
3D Tanaka contour map of Scotland with #rayshader for Day 6 of the #30DayMapChallenge - Dimensions.

Code: github.com/rajodm/30Day...

Thanks @milos-makes-maps.bsky.social for the helpful tutorial!

#dataviz #rstats #ggplot2
A 3D relief map of Scotland using Tanaka contours, with elevation shown in warm colors ranging from green in the lowlands, through yellow in mid-elevations, to deep browns in the Scottish Highlands.
November 7, 2025 at 10:40 AM
rajodm.bsky.social
Here we go, #30daymapchallenge Day 1: points.
Health facilities across Sub-Saharan Africa.
Code: github.com/rajodm/30Day...

#dataviz #rstats
Map of Sub-Saharan Africa showing location and type of 96,395 health facilities from 2019. Each facility is represented by a colored dot: dark pink dots are maternal and child health centers, light pink dots are referral hospitals, purple dots are district hospitals, green dots are other hospitals, and light blue dots are primary care facilities (most numerous).
November 1, 2025 at 3:26 AM
rajodm.bsky.social
#30daymapchallenge is in 2 days and I'm exited to see what everyone creates! Check out this @ansgarw.bsky.social 's feed to keep up with the challenge.

bsky.app/profile/did:...
October 29, 2025 at 1:54 PM
rajodm.bsky.social
#TidyTuesday week 42

Had some fun experimenting with the {gt} package for this week data from the UK Met Office on historical weather and climate.

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
A table summarizing five UK climate zones identified from historical Met Office data (1981–2010). Clusters were derived using k-means analysis based on temperature, rainfall, sunshine, and number of air frost days variables. Each row represents a cluster: the first column shows station locations, the second lists the number of stations, and the remaining six columns present key weather characteristics.
October 23, 2025 at 7:06 PM
Reposted by Rajo
tjukanov.bsky.social
It’s happening again in November. 30 days and 30 themes for maps. Take part, learn something new and have fun. That’s what #30DayMapChallenge is about 🌎
October 1, 2025 at 4:15 PM
rajodm.bsky.social
#TidyTuesday week 37
Data: allrecipes.com, {tastyR} package
Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
Alluvial plot showing monthly recipe publications on allrecipes.com by cuisine origin in 2024. The chart shows steady growth throughout the year, with a dramatic spike in November (313 recipes, 42% of yearly total). 'Other' cuisines dominate with 526 recipes, followed by French (40), Soul Food (31), Indian (31), German (31), Filipino (30), Jewish (29), and Cajun and Creole (29).
September 19, 2025 at 9:22 PM
rajodm.bsky.social
#TidyTuesday week 36
Data: Henley Passport Index

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
A scatter plot showing passport strength by world region, with visa-free access count on the x-axis and seven regions (Africa, Americas, Asia, Caribbean, Europe, Middle East, Oceania) on the y-axis. Each country is represented by a circular flag icon. Vertical lines mark the median global mobility at 70 countries and the threshold for highest global mobility at 172 countries. European countries cluster toward the right with highest access, while African, Asian and Middle Eastern countries concentrate on the left with lower access. Caribbean and American countries show wide variation across the mobility spectrum.
September 11, 2025 at 7:21 PM
rajodm.bsky.social
#TidyTuesday week 33
Data: Scottish Munros

Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2
Topographic map of Scotland showing the locations of its three highest peaks: Ben Nevis (1344.53m), Ben Macdui (1309m), and Braeriach (1296m), with elevation represented by color gradients from light blue to dark brown.
August 23, 2025 at 7:08 PM
Reposted by Rajo
jonthegeek.com
Hello #dataBS (& especially #TidyTuesday) fam! I'm trying to organize a thing to help me keep TidyTuesday running smoothly, but first I need to get a bit of a runway. Every week I curate a TT dataset, and it's wearing me down. Please see github.com/rfordatascie... for some ways you can help! #RStats
August 15, 2025 at 11:23 AM
Reposted by Rajo
laracrc.bsky.social
Happy Friday 🌍! We are giving away one copy of 'Spatial Linear Models for Environmental Data'. To enter, simply follow me and re-post this by the end of Sunday, August 17. #spatial #environment # #data #StatsSky routledge.com/9780367183349 not associated with bsky
Book cover of Spatial Linear Models for Enviornmental Data, dark blue background with white pattern with a picture of caribou and data plots
August 15, 2025 at 9:56 AM
Reposted by Rajo
weare.rladies.org
My #TidyTuesday Week 32: Extreme Weather Attribution studies. thanks for curating @rajodm.bsky.social !!
A treemap chart titled 'Extreme Weather Attribution Studies' showing the breakdown of 620 studies by their conclusions about human climate influence. The chart is divided into two main sections: 'More severe or more likely N = 554' (left side, larger) and 'Less severe or less likely N = 66' (right side, smaller). In the larger left section, the biggest segments are Heat (196 studies, shown in orange), Rain & flooding (110 studies, in blue), Drought (74 studies, in yellow), Storm (46 studies, in gray), Compound events (32 studies, in light pink), Impact (30 studies, in coral), Wildfire (27 studies, in red), and Oceans (23 studies, in dark blue). In the smaller right section, the segments are Cold, snow & ice (37 studies, in light blue), Rain & flooding (20 studies, in blue), Storm (3 studies, in gray), Impact (2 studies, in coral), River flow (2 studies, in blue), and Drought (1 study, in yellow). The chart includes explanatory text stating that most extreme weather attribution studies conclude human influence has made extreme events more severe or likely, with Heat, Rain & flooding, and Drought being the most frequently reported. Data source is listed as Carbon Brief, dated TidyTuesday Week 32 2025
August 14, 2025 at 11:05 PM