#rpackages — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rpackages, aggregated by home.social.
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✨ One of my favorite non-spatial R packages: beepr 🔔
Plays sounds when your R scripts finish running.
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✨ One of my favorite non-spatial R packages: beepr 🔔
Plays sounds when your R scripts finish running.
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✨ One of my favorite non-spatial R packages: beepr 🔔
Plays sounds when your R scripts finish running.
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✨ One of my favorite non-spatial R packages: beepr 🔔
Plays sounds when your R scripts finish running.
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✨ One of my favorite non-spatial R packages: beepr 🔔
Plays sounds when your R scripts finish running.
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🎉 New in #typeR 0.2.0: typeRun()!
⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmdPerfect for live teaching & demos!
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🎉 New in #typeR 0.2.0: typeRun()!
⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmdPerfect for live teaching & demos!
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🎉 New in #typeR 0.2.0: typeRun()!
⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmdPerfect for live teaching & demos!
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🎉 New in #typeR 0.2.0: typeRun()!
⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmdPerfect for live teaching & demos!
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🎉 New in #typeR 0.2.0: typeRun()!
⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmdPerfect for live teaching & demos!
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Meixiang Gao et al. investigated the use of R and its packages in 125 494 scholarly articles published in 40 #EcologyJournals from 2008 to 2023.
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Check out https://r-multiverse.org - this a brilliant new approach to #rstats #RPackages in the whole development cycle from Beta to release to, and that is great, regular snapshots of the whole repo.
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Check out https://r-multiverse.org - this a brilliant new approach to #rstats #RPackages in the whole development cycle from Beta to release to, and that is great, regular snapshots of the whole repo.
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Check out https://r-multiverse.org - this a brilliant new approach to #rstats #RPackages in the whole development cycle from Beta to release to, and that is great, regular snapshots of the whole repo.
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Check out https://r-multiverse.org - this a brilliant new approach to #rstats #RPackages in the whole development cycle from Beta to release to, and that is great, regular snapshots of the whole repo.
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@feld good question!
i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)
the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work
check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*
that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR
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@feld good question!
i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)
the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work
check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*
that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR
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@feld good question!
i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)
the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work
check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*
that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR
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@feld good question!
i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)
the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work
check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*
that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR
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@feld good question!
i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)
the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work
check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*
that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR
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1. write a function
2. write roxygen documentation
3. procrastinate for a hour
4. lunch
5. write unit tests -
1. write a function
2. write roxygen documentation
3. procrastinate for a hour
4. lunch
5. write unit tests -
1. write a function
2. write roxygen documentation
3. procrastinate for a hour
4. lunch
5. write unit tests -
1. write a function
2. write roxygen documentation
3. procrastinate for a hour
4. lunch
5. write unit tests -
In my opinion, #R is very suitable for #MachineLearning. With R, machine learning can be easily integrated into usual #rstats data analysis workflows. #RPackages provide access to virtually all relevant machine learning algorithms like #NeuralNetworks, Support Vector machines (#SVM), #RandomForests, Extreme Gradient Boosting (#XGBoost), #WEKA algorithms, etc.
Does anyone of the @rstats group have further recommendations?
See reply for sources: 4 books on machine learning.
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In my opinion, #R is very suitable for #MachineLearning. With R, machine learning can be easily integrated into usual #rstats data analysis workflows. #RPackages provide access to virtually all relevant machine learning algorithms like #NeuralNetworks, Support Vector machines (#SVM), #RandomForests, Extreme Gradient Boosting (#XGBoost), #WEKA algorithms, etc.
Does anyone of the @[email protected] group have further recommendations?
See reply for sources: 4 books on machine learning.