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Reposted by Mehmet Göktuğ Öztürk

Jakub Nowosad @jakubnowosad.com · 16d

🌍Exploring Sentinel-2 with the Copernicus Data Space Ecosystem (CDSE)?

Hugh Graham’s guide with vrtility in #rstats. Includes authentication & query of imagery and building cloud-free composites.

permian-global-research.github.io/vrtility/art...

#RSpatial #RemoteSensing #EarthObservation #GIS

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Jakub Nowosad @nowosad.fosstodon.org.ap.brid.gy · 5d

Starting out with geographic data in Python? 🌍🐍

Chapter 1 of Geocomputation with Python introduces vector & raster models + core tools (geopandas, shapely, rasterio).

👉 https://py.geocompx.org/01-spatial-data

#geopython #python #gischat

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HU Functional Ecology Lab. @funecolhu.bsky.social · 6d

Our Fieldwork in Beytepe🌿
To identify the distribution of Ailanthus altissima, examine its changes over the years, and predict its future dynamics, we carried out fieldwork at Hacettepe University’s Beytepe Campus
Together with our intern team, we collected data in the pilot area of our research. 🌸

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Tom Hengl @tomhengl.bsky.social · 25d

Had a privilege and pleasure to teach terra pkg (created by @roberthijmans.bsky.social et al.) and spatial sampling with R together with Alexandre Wadoux at the #EOSummerSchool2025 at WICC. You can follow the terra tutorial from here: differ.blog/p/processing...
📹: youtu.be/q9sQkg7BeAc?...

Processing large rasters using terra package for R: combining tiling and time-series modeling

differ.blog

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Nicola Rennie @nrennie.bsky.social · Aug 18

If you're interested in learning #JuliaLang and are currently an #RStats user (or even if you're not!), then here's a little introduction to getting started! 📊

Blog post: nrennie.rbind.io/blog/introdu...

#QuartoPub #Julia @tidierjl.bsky.social

Introduction to Julia for R users – Nicola Rennie

This blog post will give R users an overview of what Julia is and why they might want to use it, introduce some data processing Julia packages, and show how they can incorporate Julia into existing R ...

nrennie.rbind.io

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Grant McDermott @gmcd.bsky.social · Aug 10

I had a blast at #useR2025.

Here are my slides on `tinyplot`. grantmcdermott.com/tinyplot/vig...

(Rendering of the title slide is a bit off on mobile. I'll fix when when I get sec.)

tinyplot –

grantmcdermott.com

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Dirk Eddelbuettel @eddelbuettel.com · Apr 20

The "Getting Started in R - Tinyverse Edition" eight-page pdf guide now has a 2nd edition using, appropriately, `tinyplot` as the plotting package.

See github.com/eddelbuettel... for more.

#rstats

Screenshot of the top parts of pages 5 and 6 of 'Getting Started in R - Tinyverse Edition' at https://github.com/eddelbuettel/gsir-te/

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Posit @posit.co · Aug 6

Check out Positron’s Plots Pane!

Preview, resize, copy, and save your #Python and #RStats plots directly in your IDE. Never guess what your exported plot will look like ever again 🤩

Learn more about Positron’s layout: positron.posit.co/layout.html#... #Positron

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econmaett @econmaett.github.io · Jul 25

Bias Variance Tradeoff interactive visualization by Jared Wilber & Brent Werness 😃
mlu-explain.github.io/bias-variance/
#dataviz #stats

At different levels of complexity, a sample of model realizations alongside their corresponding prediction error decompositions
Source: https://mlu-explain.github.io/bias-variance/

The bias variance tradeoff for LOESS may be controlled for via the smoothness parameter. When the smoothness is small, the amount of data we consider is insufficient for an accurate fit, resulting in large variance. However, if we make the smoothness too high (i.e. over-smoothed), we trade local information for global, resulting in large bias.

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Etienne Bacher @etiennebacher.bsky.social · Jul 23

#rstats polars 1.0.0 is now available!

polars provides bindings to the eponymous Rust library, a very efficient dataframe library.

This is the outcome of a 1-year long rewrite led by @eitsupi to refactor the internals and match the API of Python polars.

pola-rs.github.io/r-polars/

Details ⬇️

Polars R Package

pola-rs.github.io

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Jakub Nowosad @jakubnowosad.com · Jul 23

Have you read Geocomputation with R?📘
If you found it helpful, we’d really appreciate a quick review — it helps others find the book too!

Goodreads: www.goodreads.com/book/show/21...
Amazon: www.amazon.com/dp/1032248882
Book: r.geocompx.org

#rstats #gis #geocompx #rspatial

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Mehmet Göktuğ Öztürk @mehmetgoktug.bsky.social · Jul 11

This award means a lot to me, and I’m truly grateful to my advisors, @ctavsanoglu.bsky.social and @fire-ecologist.bsky.social, for their invaluable guidance and support throughout this journey.

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Mehmet Göktuğ Öztürk @mehmetgoktug.bsky.social · Jul 11

I'm thrilled to share some wonderful news with you today!
My master’s thesis, “Spatiotemporal Patterns of Lightning-Induced Wildfires in Turkey”, has received the Best Master's Thesis Award from the Ecology and Evolutionary Biology Society of Türkiye - EkoEvo!

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Jakub Nowosad @jakubnowosad.com · Jul 9

🚨 Final part of our Spatial ML with R series ! 🚨

We explore spatial cross-validation with sperrorest & blockCV — tools outside the usual ML frameworks 📦

URL: geocompx.org/post/2025/sm...

#rstats #SpatialML #rspatial

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juli g pausas @jgpausas.bsky.social · Jul 9

"Aspen (Populus tremuloides) slows fire progression: as aspen cover on the landscape increased, daily area burned and linear spread rate decreased." Harris et al. #EcolAppl @esajournals.bsky.social 🧪🌍🔥🌳🪴
#Flammability matters!

esajournals.onlinelibrary.wiley.com/doi/10.1002/...

GrumpyUncleSean🔥 @grumpyunclesean.bsky.social · Jul 9

1/4

Our research team just published a new paper: "Aspen impedes wildland fire spread in southwestern United States landscapes".

Aspen forests are thought to be more resistant to burning compared to conifer forests, but this has not been well studied.
🧪🔥

Aspen impedes wildfire spread in southwestern United States landscapes

Aspen (Populus tremuloides) forests are generally thought to impede fire spread, yet the extent of this effect is not well quantified in relation to other vegetation types. We examined the influence ...

doi.org

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Ben Bolker @bbolker.bsky.social · Jan 26

I like this paper too, but (horn-tooting alert) alternatively, consider not doing model selection at all? doi.org/10.3390/e260... (FWIW I do feel a bit bad about publishing in an MDPI special issue [!!] - long story, I agreed to do it and then felt uncomfortable backing out - wouldn't do it again)

Multimodel Approaches Are Not the Best Way to Understand Multifactorial Systems

Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple work...

doi.org

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Jonathan Carroll @jonocarroll.fosstodon.org.ap.brid.gy · Jun 29

New post: https://jcarroll.com.au/2025/06/29/counting-digits-quickly/

What if you could just wave a magic wand over your R #rstast :rstats: code and have it transform into something that ran as fast as or faster than C? @t_kalinowski's {quickr} 'R to Fortran Transpiler' does that for you! […]

Original post on fosstodon.org

fosstodon.org

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Charlie Gao @shikokuchuo.net · Jun 13

Bleeding edge update for the #tidyverse purrr package with even more seamless #rstats parallel maps.

Introducing our shiniest new adverb: `in_parallel()`. Just wrap your function to take advantage of blazing fast parallel processing via mirai.

pak::pak("tidyverse/purrr")

purrr.tidyverse.org/dev/

Functional Programming Tools

A complete and consistent functional programming toolkit for R.

purrr.tidyverse.org

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Jakub Nowosad @jakubnowosad.com · May 28

New blog post: Spatial Machine Learning with tidymodels 🌍🧠📦

This post shows how to apply the tidymodels framework to spatial data workflows in R. Part 3 in a series about #sml.

🔗 geocompx.org/post/2025/sm...

#rstats #rspatial #tidymodels #gischat

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Jakub Nowosad @jakubnowosad.com · Jun 11

Spatial prediction meets machine learning with mlr3 💡🌍
This blog post by Marvin Ludwig also includes tips on autocorrelation, extrapolation & more.

🔗 geocompx.org/post/2025/sm...

#mlr3 #rstats #gischat #rspatial

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Hadley Wickham @hadley.nz · Jun 10

Is #rstats dead? I don’t think so.

Line chart titled ‘Weekly Runs of RStudio IDE’ showing usage data from 2023 to 2025. The y-axis ranges from 2,000,000 to 6,000,000 weekly runs. The chart displays a cyclical pattern with regular peaks around 5,000,000-6,000,000 runs and dramatic drops to approximately 2,000,000 runs that occur periodically during holiday periods.

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Jakub Nowosad @jakubnowosad.com · Apr 30

🔍 Interested in spatial machine learning with R?🌍
We compare caret, tidymodels, and mlr3 for spatial tasks — and show how their workflows differ.

Read it here: geocompx.org/post/2025/sm...

#MachineLearning #SpatialAnalysis #RStats #rspatial

A possible workflow of the spatial machine learning task.

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Francisco Rodriguez-Sanchez @frodsan.bsky.social · Jan 21

Actual code sharing remains terribly low in ecology (source: doi.org/10.1002/ece3...)

Temporal trends of open-access publications (open publication), open data, and code sharing (open code) between 2010 and 2022. Sample size was 77 papers per year, 1001 total. The lines show trends in each open-science component over time, with dashed lines representing non-significant trends, and solid lines indicating significance. Open data (p = 1.48e-06) and code sharing (p = .00157) increased significantly over time, while open-access publications (p = .926) had no statistically significant relationship.

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Hadley Wickham @hadley.nz · Feb 27

What advice do folks have for organising projects that will be deployed to production? How do you organise your directories? What do you do if you're deploying multiple "things" (e.g. an app and an api) from the same project?

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Jakub Nowosad @jakubnowosad.com · Mar 2

Thank you to everyone who has already supported our work on open-source spatial analysis resources like Geocomputation with R/Python! 🙏🌍

If you’ve found our books helpful, consider supporting future updates and free resources: buff.ly/3OMFPUU

<span class="text-blue-600 dark:text-sky-400">#rstats #rspatial #geopython #geocompx

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