#rstats — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rstats, aggregated by home.social.
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📣 The R+AI 2026 Call for Proposals is open!
How are you using R with AI, machine learning, LLMs, agents, coding assistants, data products, or responsible AI?
Share your practical workflows, research, and lessons learned.
Talks, lightning talks, workshops, and panels are welcome. Lend your voice to the R community!
📅 Submit by September 7
💻 Online November 10–11 -
📣 The R+AI 2026 Call for Proposals is open!
How are you using R with AI, machine learning, LLMs, agents, coding assistants, data products, or responsible AI?
Share your practical workflows, research, and lessons learned.
Talks, lightning talks, workshops, and panels are welcome. Lend your voice to the R community!
📅 Submit by September 7
💻 Online November 10–11 -
📣 The R+AI 2026 Call for Proposals is open!
How are you using R with AI, machine learning, LLMs, agents, coding assistants, data products, or responsible AI?
Share your practical workflows, research, and lessons learned.
Talks, lightning talks, workshops, and panels are welcome. Lend your voice to the R community!
📅 Submit by September 7
💻 Online November 10–11 -
📣 The R+AI 2026 Call for Proposals is open!
How are you using R with AI, machine learning, LLMs, agents, coding assistants, data products, or responsible AI?
Share your practical workflows, research, and lessons learned.
Talks, lightning talks, workshops, and panels are welcome. Lend your voice to the R community!
📅 Submit by September 7
💻 Online November 10–11 -
📣 The R+AI 2026 Call for Proposals is open!
How are you using R with AI, machine learning, LLMs, agents, coding assistants, data products, or responsible AI?
Share your practical workflows, research, and lessons learned.
Talks, lightning talks, workshops, and panels are welcome. Lend your voice to the R community!
📅 Submit by September 7
💻 Online November 10–11 -
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
#CulturalDataScience #DigitalHumanities #RStats
Cultural Data Science -
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
#CulturalDataScience #DigitalHumanities #RStats
Cultural Data Science -
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
#CulturalDataScience #DigitalHumanities #RStats
Cultural Data Science -
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
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A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
-
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
-
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
-
A small milestone for my book Cultural Data Science: An Introduction to R: the Springer page has now passed 10,000 accesses. Great to see such strong interest in bringing data science, digital methods and visual humanities together.
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Boosted Configuration (_neural_) Networks for classification
https://thierrymoudiki.github.io/blog/2022/07/21/r/misc/boosted-configuration-networks -
gettz 0.0.6 on CRAN: Maintenance
Get the Timezone Information Fallback
https://dirk.eddelbuettel.com/blog/2026/08/25#gettz_0.0.6
#rstats -
gettz 0.0.6 on CRAN: Maintenance
Get the Timezone Information Fallback
https://dirk.eddelbuettel.com/blog/2026/08/25#gettz_0.0.6
#rstats -
gettz 0.0.6 on CRAN: Maintenance
Get the Timezone Information Fallback
https://dirk.eddelbuettel.com/blog/2026/08/25#gettz_0.0.6
#rstats -
gettz 0.0.6 on CRAN: Maintenance
Get the Timezone Information Fallback
https://dirk.eddelbuettel.com/blog/2026/08/25#gettz_0.0.6
#rstats -
gettz 0.0.6 on CRAN: Maintenance
Get the Timezone Information Fallback
https://dirk.eddelbuettel.com/blog/2026/08/25#gettz_0.0.6
#rstats -
GLMNet in Python: Generalized Linear Models
https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post -
GLMNet in Python: Generalized Linear Models
https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post -
GLMNet in Python: Generalized Linear Models
https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post -
GLMNet in Python: Generalized Linear Models
https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post -
GLMNet in Python: Generalized Linear Models
https://thierrymoudiki.github.io/blog/2024/11/18/python/r/GLMNet-post -
Coworking and Office Hours next week!
Theme: Getting to Know SORTEE
Tuesday September 1st 14:00 Europe Central (12:00 UTC)
Join Ed Ivimey-Cook and @steffilazerte
- General coworking
- Visit SORTEE (https://sortee.org/)
- Cowork independently on something related to R
- Chat with Ed and other attendees and discuss our theme! -
Coworking and Office Hours next week!
Theme: Getting to Know SORTEE
Tuesday September 1st 14:00 Europe Central (12:00 UTC)
Join Ed Ivimey-Cook and @steffilazerte
- General coworking
- Visit SORTEE (https://sortee.org/)
- Cowork independently on something related to R
- Chat with Ed and other attendees and discuss our theme! -
Coworking and Office Hours next week!
Theme: Getting to Know SORTEE
Tuesday September 1st 14:00 Europe Central (12:00 UTC)
Join Ed Ivimey-Cook and @steffilazerte
- General coworking
- Visit SORTEE (https://sortee.org/)
- Cowork independently on something related to R
- Chat with Ed and other attendees and discuss our theme! -
Coworking and Office Hours next week!
Theme: Getting to Know SORTEE
Tuesday September 1st 14:00 Europe Central (12:00 UTC)
Join Ed Ivimey-Cook and @steffilazerte
- General coworking
- Visit SORTEE (https://sortee.org/)
- Cowork independently on something related to R
- Chat with Ed and other attendees and discuss our theme! -
Coworking and Office Hours next week!
Theme: Getting to Know SORTEE
Tuesday September 1st 14:00 Europe Central (12:00 UTC)
Join Ed Ivimey-Cook and @steffilazerte
- General coworking
- Visit SORTEE (https://sortee.org/)
- Cowork independently on something related to R
- Chat with Ed and other attendees and discuss our theme! -
It's time for this week's #TidyTuesday which is looking at country music lyrics 🎶
I decided to explore which colours are mentioned in song lyrics 🎨 with a chart styled as an audio equalizer display 📊
Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-08-25
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It's time for this week's #TidyTuesday which is looking at country music lyrics 🎶
I decided to explore which colours are mentioned in song lyrics 🎨 with a chart styled as an audio equalizer display 📊
Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-08-25
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It's time for this week's #TidyTuesday which is looking at country music lyrics 🎶
I decided to explore which colours are mentioned in song lyrics 🎨 with a chart styled as an audio equalizer display 📊
Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-08-25
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It's time for this week's #TidyTuesday which is looking at country music lyrics 🎶
I decided to explore which colours are mentioned in song lyrics 🎨 with a chart styled as an audio equalizer display 📊
Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-08-25
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It's time for this week's #TidyTuesday which is looking at country music lyrics 🎶
I decided to explore which colours are mentioned in song lyrics 🎨 with a chart styled as an audio equalizer display 📊
Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-08-25
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Most R projects only use a handful of functions from each package.
So why validate the entire package?
Litmus helps teams in regulated environments focus validation on the functions their code actually depends on, with risk scoring, audit reporting, custom testing and ongoing monitoring.
See how Litmus works: https://www.jumpingrivers.com/litmus/
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Most R projects only use a handful of functions from each package.
So why validate the entire package?
Litmus helps teams in regulated environments focus validation on the functions their code actually depends on, with risk scoring, audit reporting, custom testing and ongoing monitoring.
See how Litmus works: https://www.jumpingrivers.com/litmus/
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Most R projects only use a handful of functions from each package.
So why validate the entire package?
Litmus helps teams in regulated environments focus validation on the functions their code actually depends on, with risk scoring, audit reporting, custom testing and ongoing monitoring.
See how Litmus works: https://www.jumpingrivers.com/litmus/
-
Most R projects only use a handful of functions from each package.
So why validate the entire package?
Litmus helps teams in regulated environments focus validation on the functions their code actually depends on, with risk scoring, audit reporting, custom testing and ongoing monitoring.
See how Litmus works: https://www.jumpingrivers.com/litmus/
-
Most R projects only use a handful of functions from each package.
So why validate the entire package?
Litmus helps teams in regulated environments focus validation on the functions their code actually depends on, with risk scoring, audit reporting, custom testing and ongoing monitoring.
See how Litmus works: https://www.jumpingrivers.com/litmus/
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Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @milesmcbain #rstats #pipes #debugging
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Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @milesmcbain #rstats #pipes #debugging
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Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @milesmcbain #rstats #pipes #debugging
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Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @milesmcbain #rstats #pipes #debugging
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Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @milesmcbain #rstats #pipes #debugging
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RE: https://mastodon.social/@rmflight/117146883938393856
@bioinfhotep @Psy_Fer_ @rmflight
We need retries (e.g. ntry()) in different scenario, but at the same time it can be scary. We certainly don't want to keep retrying forever, risking ending up in an infinite loop (💲💲💲) => we need a retry policy conditioned on type of error/failure
My horror scenario is when we run something that brings down a compute node, and then we keep retrying on others. In the worst case, we take down a full system 😱
There's been some prior work on this in #RStats CRAN
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RE: https://mastodon.social/@rmflight/117146883938393856
@bioinfhotep @Psy_Fer_ @rmflight
We need retries (e.g. ntry()) in different scenario, but at the same time it can be scary. We certainly don't want to keep retrying forever, risking ending up in an infinite loop (💲💲💲) => we need a retry policy conditioned on type of error/failure
My horror scenario is when we run something that brings down a compute node, and then we keep retrying on others. In the worst case, we take down a full system 😱
There's been some prior work on this in #RStats CRAN
-
RE: https://mastodon.social/@rmflight/117146883938393856
@bioinfhotep @Psy_Fer_ @rmflight
We need retries (e.g. ntry()) in different scenario, but at the same time it can be scary. We certainly don't want to keep retrying forever, risking ending up in an infinite loop (💲💲💲) => we need a retry policy conditioned on type of error/failure
My horror scenario is when we run something that brings down a compute node, and then we keep retrying on others. In the worst case, we take down a full system 😱
There's been some prior work on this in #RStats CRAN
-
RE: https://mastodon.social/@rmflight/117146883938393856
@bioinfhotep @Psy_Fer_ @rmflight
We need retries (e.g. ntry()) in different scenario, but at the same time it can be scary. We certainly don't want to keep retrying forever, risking ending up in an infinite loop (💲💲💲) => we need a retry policy conditioned on type of error/failure
My horror scenario is when we run something that brings down a compute node, and then we keep retrying on others. In the worst case, we take down a full system 😱
There's been some prior work on this in #RStats CRAN
-
RE: https://mastodon.social/@rmflight/117146883938393856
@bioinfhotep @Psy_Fer_ @rmflight
We need retries (e.g. ntry()) in different scenario, but at the same time it can be scary. We certainly don't want to keep retrying forever, risking ending up in an infinite loop (💲💲💲) => we need a retry policy conditioned on type of error/failure
My horror scenario is when we run something that brings down a compute node, and then we keep retrying on others. In the worst case, we take down a full system 😱
There's been some prior work on this in #RStats CRAN
-
There's room for improvement in base #RStats around memory management (e.g. classed out-of-memory errors, pre-allocation hooks, …) but also via the package ecosystem.
Standardization how we handle this helps. It's a multi-year challenge to get there, but there are some low hanging fruits, e.g. a cross-platform freeMemory() that respects different limits set on different operating systems.
If you're interested in this, please see https://www.futureverse.org/roadmap/resources.html and how to connect and contribute.
6/🧵
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There's room for improvement in base #RStats around memory management (e.g. classed out-of-memory errors, pre-allocation hooks, …) but also via the package ecosystem.
Standardization how we handle this helps. It's a multi-year challenge to get there, but there are some low hanging fruits, e.g. a cross-platform freeMemory() that respects different limits set on different operating systems.
If you're interested in this, please see https://www.futureverse.org/roadmap/resources.html and how to connect and contribute.
6/🧵
-
There's room for improvement in base #RStats around memory management (e.g. classed out-of-memory errors, pre-allocation hooks, …) but also via the package ecosystem.
Standardization how we handle this helps. It's a multi-year challenge to get there, but there are some low hanging fruits, e.g. a cross-platform freeMemory() that respects different limits set on different operating systems.
If you're interested in this, please see https://www.futureverse.org/roadmap/resources.html and how to connect and contribute.
6/🧵
-
There's room for improvement in base #RStats around memory management (e.g. classed out-of-memory errors, pre-allocation hooks, …) but also via the package ecosystem.
Standardization how we handle this helps. It's a multi-year challenge to get there, but there are some low hanging fruits, e.g. a cross-platform freeMemory() that respects different limits set on different operating systems.
If you're interested in this, please see https://www.futureverse.org/roadmap/resources.html and how to connect and contribute.
6/🧵
-
There's room for improvement in base #RStats around memory management (e.g. classed out-of-memory errors, pre-allocation hooks, …) but also via the package ecosystem.
Standardization how we handle this helps. It's a multi-year challenge to get there, but there are some low hanging fruits, e.g. a cross-platform freeMemory() that respects different limits set on different operating systems.
If you're interested in this, please see https://www.futureverse.org/roadmap/resources.html and how to connect and contribute.
6/🧵
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An alternative is to set the Unix 'RLIMIT_AS' memory limit, which #RStats can handle:
$ ulimit -v 128000; Rscript -e "tryCatch(x <- rnorm(10e6), error=identity)"
<simpleError: cannot allocate vector of size 76.3 Mb>From within #RStats
> void <- unix::rlimit_as(100e6)
> tryCatch(x <- rnorm(10e6), error=identity)
<simpleError: cannot allocate vector of size 76.3 Mb>reset
> void <- unix::rlimit_as(Inf)
> tryCatch(x <- rnorm(10e6), error=identity)This is just Unix and there's much more. 5/🧵
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An alternative is to set the Unix 'RLIMIT_AS' memory limit, which #RStats can handle:
$ ulimit -v 128000; Rscript -e "tryCatch(x <- rnorm(10e6), error=identity)"
<simpleError: cannot allocate vector of size 76.3 Mb>From within #RStats
> void <- unix::rlimit_as(100e6)
> tryCatch(x <- rnorm(10e6), error=identity)
<simpleError: cannot allocate vector of size 76.3 Mb>reset
> void <- unix::rlimit_as(Inf)
> tryCatch(x <- rnorm(10e6), error=identity)This is just Unix and there's much more. 5/🧵
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An alternative is to set the Unix 'RLIMIT_AS' memory limit, which #RStats can handle:
$ ulimit -v 128000; Rscript -e "tryCatch(x <- rnorm(10e6), error=identity)"
<simpleError: cannot allocate vector of size 76.3 Mb>From within #RStats
> void <- unix::rlimit_as(100e6)
> tryCatch(x <- rnorm(10e6), error=identity)
<simpleError: cannot allocate vector of size 76.3 Mb>reset
> void <- unix::rlimit_as(Inf)
> tryCatch(x <- rnorm(10e6), error=identity)This is just Unix and there's much more. 5/🧵
-
An alternative is to set the Unix 'RLIMIT_AS' memory limit, which #RStats can handle:
$ ulimit -v 128000; Rscript -e "tryCatch(x <- rnorm(10e6), error=identity)"
<simpleError: cannot allocate vector of size 76.3 Mb>From within #RStats
> void <- unix::rlimit_as(100e6)
> tryCatch(x <- rnorm(10e6), error=identity)
<simpleError: cannot allocate vector of size 76.3 Mb>reset
> void <- unix::rlimit_as(Inf)
> tryCatch(x <- rnorm(10e6), error=identity)This is just Unix and there's much more. 5/🧵
-
An alternative is to set the Unix 'RLIMIT_AS' memory limit, which #RStats can handle:
$ ulimit -v 128000; Rscript -e "tryCatch(x <- rnorm(10e6), error=identity)"
<simpleError: cannot allocate vector of size 76.3 Mb>From within #RStats
> void <- unix::rlimit_as(100e6)
> tryCatch(x <- rnorm(10e6), error=identity)
<simpleError: cannot allocate vector of size 76.3 Mb>reset
> void <- unix::rlimit_as(Inf)
> tryCatch(x <- rnorm(10e6), error=identity)This is just Unix and there's much more. 5/🧵