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#tidyverse

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Public
shikokuchuo

Posit::conf(2025)

"Purrrfectly parallel, purrrfectly distributed"

The slides from the talk are available at: shikokuchuo-posit2025.share.co

See why we now have parallel purrr, powered by mirai, and how to leverage distributed computing to run compute on other machines.

This can be used to (i) replace local compute (ii) extend local compute to (many) other machines, or (iii) direct only certain types of compute e.g. those requiring GPU to machines with a GPU.

shikokuchuo-posit2025.share.connect.posit.cloudPurrrfectly parallel Purrrfectly distributed
#positconf2025#tidyverse
Public
shikokuchuo

tidyverse blog: mirai 2.5.0

tidyverse.org/blog/2025/09/mir

This milestone release delivers enhanced observability through OpenTelemetry, reproducible parallel RNG, and key user interface improvements.

mirai is built on current communication technologies and continues to evolve as the foundation for asynchronous and parallel computing across the R ecosystem, powering everything from async Shiny applications to parallel map in purrr to hyperparameter tuning in tidymodels.

www.tidyverse.orgmirai 2.5.0mirai - minimalist async evaluation framework for R - brings production-grade parallel and distributed computing to the ecosystem.
#rstats#tidyverse
Public *
Stefan Siegert

Generative calligraphy with random walks, smoothing splines and aesthetic mapping in R.

See also: mastodon.social/@safest_intege

```
library(tidyverse)
n = 20
x = cumsum(rnorm(n))
y = runif(n)
xx = predict(smooth.spline(x, spar=.005), seq(1, n, .01))$y
yy = predict(smooth.spline(y, spar=.005), seq(1, n, .01))$y
ggplot(tibble(x=xx,y=yy)) +
geom_path(aes(x=x, y=y, lwd=-c(0,diff(y))), show.legend=FALSE) +
theme_void()
```

#rstats#rtistry#generativeart
Public *
shikokuchuo

tidyverse blog: Breaking down language barriers in data science

tidyverse.org/blog/2025/09/nan

nanonext is a messaging and concurrency toolkit, connecting R to polyglot data science pipelines involving modules written in Python, Rust, Go and C++ etc.

It facilitates a shift toward more flexible, performance-oriented data science workflows. Rather than be locked into a single language, build systems where each component uses the best tools for the job.

#RStats#tidyverse
Public
Data Science

Do you need better performance than what the standard #tidyverse functions have? {collapse} might be worth a look: sebkrantz.github.io/collapse/ #rstats #optimization

sebkrantz.github.ioAdvanced and Fast Data Transformation in RA large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R, fast functions for data transformation and common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It seamlessly supports base R objects/classes as well as units, integer64, xts/ zoo, tibble, grouped_df, data.table, sf, and pseries/pdata.frame.
Public
Erik-Jan

I was annoyed that there is no "expand_grid()" function in :python: #Python as in :rstats: #RStats #tidyverse

So I just published a small package on #PyPI !

Introducing polarsgrid
pypi.org/project/polarsgrid/

Using the excellent #polars 🐻‍❄️ package, easily create a table with product of factors:

from polarsgrid import expand_grid
expand_grid(a=[1, 2, 3], b=["x", "y"])

Yields all combinations of its inputs as a #DataFrame

It can also produce a #LazyFrame for streaming extra-big tables to disk

pypi.orgClient Challenge
Public
shikokuchuo

#tidyverse purrr 1.1.0 is out - your functional programming toolkit now has parallel processing!

Production-ready parallel and distributed computing comes courtesy of mirai, scaling your workflows reliably on your local machine, local network or HPC cluster.

Huge thanks to the #rstats community for testing this out pre-release and helping shape what it is.

Amazing collective effort by @hadleywickham @lionel @davis.

Read all about it at:
tidyverse.org/blog/2025/07/pur

www.tidyverse.orgParallel processing in purrr 1.1.0The functional programming toolkit for R gains new capabilities for parallel processing and distributed computing using mirai.
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