#rdatatable — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rdatatable, aggregated by home.social.
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As a #RDataTable translator and, more importantly, as a former #GNOME translator, I'm happy with the 1.0 release of gettext, an incredibly good internationalization and localization toolkit. If your software project is not using gettext yet, do consider changing that.
https://lists.gnu.org/archive/html/info-gnu/2026-01/msg00007.html
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Anyone knows about a #rstats #rdatatable skills file for claude?
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Anyone knows about a #rstats #rdatatable skills file for claude?
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The #fcase function from #rdatatable is such a pleasure to work with.
https://rdatatable.gitlab.io/data.table/reference/fcase.html
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The #fcase function from #rdatatable is such a pleasure to work with.
https://rdatatable.gitlab.io/data.table/reference/fcase.html
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Exploring a parallel syntax with #RDataTable
```
data |>
_[, .(x = async(long(x))), by = .(group1, group2)] |>
collect_async()
```Is syntax sugar for
```
data |>
_[, .(x = list(future::future(long(x)))), by = .(group1, group2)] |>
_[, x := future::value(x[[1]]), by = .(group1, group2)] |>
_[]
``` -
Exploring a parallel syntax with #RDataTable
```
data |>
_[, .(x = async(long(x))), by = .(group1, group2)] |>
collect_async()
```Is syntax sugar for
```
data |>
_[, .(x = list(future::future(long(x)))), by = .(group1, group2)] |>
_[, x := future::value(x[[1]]), by = .(group1, group2)] |>
_[]
``` -
data challenge: rolling median
library(data.table)
set.seed(108)
x = rnorm(1e8)
n = 1000
frollmedian(x, n) |> system.time()
# user system elapsed
# 8.439 0.727 3.212 -
data challenge: rolling median
library(data.table)
set.seed(108)
x = rnorm(1e8)
n = 1000
frollmedian(x, n) |> system.time()
# user system elapsed
# 8.439 0.727 3.212 -
CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
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CW: unpopular opinion about Tidyverse
@kernpanik Usually, I also try to stick to base #rstats or lightweight packages (#tinyplot, #tinytable, #rdatatable, ...). Methinks, since most tutorial promote the tidyverse, some do not know base equivalent. However, base data frame operations may require more careful handling of row order, factor levels, and preserving the data frame structure. dplyr maintains a consistent behavior across grouped operations.
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@pglpm The only reason I don't use {collapse} is because usually what I want is already covered by #RDataTable
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data.table giving bizarre results on my system when compiled with the intel compiler. This is just a simple mean by time. The the GForce version goes all wacky. Using base::mean() returns to sanity.
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data.table giving bizarre results on my system when compiled with the intel compiler. This is just a simple mean by time. The the GForce version goes all wacky. Using base::mean() returns to sanity.
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I see the hardware is of course an important aspect.
You could also try fread from #rdatatable and see if this works better for you.
Since I have quite powerful laptop, I rarely come to the limits. But this was different in the past. I did my doctoral thesis (R package development included) in part on an #EeePC. That was fun.
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I see the hardware is of course an important aspect.
You could also try fread from #rdatatable and see if this works better for you.
Since I have quite powerful laptop, I rarely come to the limits. But this was different in the past. I did my doctoral thesis (R package development included) in part on an #EeePC. That was fun.
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Gráficos feitos com #RStats, usando #RDataTable para ler e tabular os dados, e #TinyPlot (@gmcd) para plotar
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#rdatatable is a pleasure to work with. Sometimes mind bending (why does it not work? 🤔 … ahh, lists 🤦♂️)
but most of the time great.I use it in combination with #tinyplot. I had situations where my code got faster and shorter by doing this.
Enjoy
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#rdatatable is a pleasure to work with. Sometimes mind bending (why does it not work? 🤔 … ahh, lists 🤦♂️)
but most of the time great.I use it in combination with #tinyplot. I had situations where my code got faster and shorter by doing this.
Enjoy
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Friends near Madrid, check out this upcoming #rdatatable event!
A Zoom option is also available, if you want to join from afar.
https://www.meetup.com/grupo-de-usuarios-de-r-de-madrid/events/306199926/
(Presentation will be in English.)
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Friends near Madrid, check out this upcoming #rdatatable event!
A Zoom option is also available, if you want to join from afar.
https://www.meetup.com/grupo-de-usuarios-de-r-de-madrid/events/306199926/
(Presentation will be in English.)
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#rdatatable would be sufficient for most of my demands I guess.
Therefore, I can unfortunately not offer a better comment.
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#rdatatable would be sufficient for most of my demands I guess.
Therefore, I can unfortunately not offer a better comment.
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Pela primeira vez, fiz revisão de "pull request" no GitHub. A mudança sendo revista era uma atualização da tradução do #RDataTable por @rafaelff
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#rstats what wouldn't I give for a package that would bring #rdatatable syntax to #polars
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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#tinyverse starter pack
|> pipeOp {base}
#tinyplot https://doi.org/10.32614/CRAN.package.tinyplot
#rdatatable https://doi.org/10.32614/CRAN.package.data.table
#poorman https://doi.org/10.32614/CRAN.package.poormanWhat did I forget?
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@johnmackintosh My 1/2 a cent as heavy user of #rdatatable for the package name: short is not so important (you only write it once). I would have gone with data.table.utils or data.table.extras to make it absolutely obvious. There is probably some room for a left_join, inner_join, full_join, asof_join wrapper as well (should you look for features)
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Someone said she felt undatable and I had to read twice, thrice because I thought it was un-data.table! 😅 #RDataTable
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Today @rafaelff, a colleague and I make our first contribution to an R package: we translated {data.table} to Brazilian Portuguese!
https://github.com/Rdatatable/data.table/commit/d02907d786ad11330c3f51e3d2d53067edf8cb00
Our work was greatly encouraged by an NSF grant for the development of the community around the package, which included translation:
1/
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"The Past, Present, and Future of data.table" - @healthandstats speaking at #UseR2024 !
Why isn't Tyson in the picture, you ask?
Because the main room was filled up right away, and I am in the overflow room! 😱
So much #rdatatable love here. 🥰
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Now giving a tutorial on data.table at #useR2024 with @paocorrales
🥳
#RStats #RDataTable -
Installing {data.table} on Windows or Mac: one binary package is installed, no dependencies.
On Linux, by default: one source package is compiled, no dependencies
On Debian, with {bspm} and APT: literally hundreds of binary packages are installed because of recommendations, suggestions.
Context: I just moved to #Debian because I wanted more consistent support for binary package in #RStats, such as #RDataTable
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Starting with #RMarkdown is a great way to introduce students to the world of #rstats. It's not just about teaching programming skills, but also about explaining other technologies like #LaTeX, #YAML, #Rnw, #markdown & #Rscript! 💻 And I'm convinced that we should focus on building a small solid foundation (#smallR 🙂 ). Think of base R (aggregate, subset, …), #rdatatable and graphics (#tinyplot) instead of diving straight into popular packages like #tidyverse. Let's keep it simple! Who's with me?
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Next stop: #useR2024 @useR_conf in Salzburg, Austria 🇦🇹
👩🏫 A full day data.table workshop from @paocorrales and @eliocamp
🦭 Talks from @healthandstats, @kellybodwin and Doris Amoakohene about #rdatatable development and the NSF grant
🍸 A community happy hour and dev sprint, with raffle giveawaysStay tuned!
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We're on LinkedIn now! Join us if that's your jam:
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Very late to celebrate, but my talk about multilingual documentation in #RStats got accepted for #useR2024! Also @paocorrales and I will be giving an #RDataTable tutorial! 🎉🎉
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📢 News! A new version update of #biopixR is now available 🎉
🔍 biopixR - Package for analysis of bioimage image data: Make your bioimaging workflow easier and more efficient with this tool.
🌐 Just a few days ago, biopixR made its first public appearance on CRAN! Check it out here: <https://cran.r-project.org/package=biopixR>
🤝 We've already received valuable feedback from the #rdatatable community with a pull request. Thanks! 🙏
Let's keep this project growing and evolving together! 🚀 #BioImaging #rstats
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Assigning values to columns looks and feels different in {data.table} compared to base R data frames.
Why, and how does this make {data.table} so much faster? 🤔
Toby breaks it down for us in the latest Tips & Tricks blog post:
🔗 https://rdatatable-community.github.io/The-Raft/posts/2024-02-18-dt_particularities-toby_hocking/
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💬 "I would like to use data.table but I prefer a pipeline workflow"
Is this you? You're in luck!
Check out @eliocamp's guest blog on The Raft about how to pipe in data.table:
🔗 https://rdatatable-community.github.io/The-Raft/posts/2024-01-28-piping_data_tables-elio_campitelli/
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We have our first new data.table feature release in years! 🎉🎉🎉
🔗 Blog post about the process and the new release: https://bit.ly/3Owg0c6
Huge thanks to all the hard working open-source devs who came together to make this happen. 💛