dplyr VS Rustler

dplyr

One pandas limitation is that it has its own API that does not quite map back to relational algebra. Ibis is such a library that's literally built by people who built pandas to provide a sane expressions system that can be mapped back to multiple SQL backends. Ibis takes inspiration from the dplyr R package to build a new expression system that can easily map back to relational algebra and thus compile to SQL. It also is declarative in style, enabling us to apply database style optimizations on the complete logical plan or the expression. Ibis is a key component for enabling composability as highlighted in the excellent composable codex.

That's great feedback, thanks!

This tool definitely comes from a place of personal need - beyond just handling large files, I've also never really gelled well with the Excel/Google Sheet model of changing data in place as if you were editing text. I'm a Data Scientist and always preferred the chained data transforms you see in things like dplyr (https://dplyr.tidyverse.org/) or Polars (https://pola.rs/) and I feel this tool maps very closely to the chained model.

Also, thank you for the feature requests! Those would all be very useful - we'll put them on the roadmap.

There's an example of how to use zzz.R with a .onload() function to set options in the dplyr code base: https://github.com/tidyverse/dplyr/blob/bbcfe99e29fe737d456b0d7adc33d3c445a32d9d/R/zzz.r

Look at the tidyverse, especially the case_when or mutate functions.

Before diving into advanced machine learning algorithms or statistical models, we need to start with the basics: collecting and organizing data. Fortunately, both Python and R offer a wealth of libraries that make it easy to collect data from a variety of sources, including web scraping, APIs, and reading from files. Key libraries in Python include requests, BeautifulSoup, and pandas, while R has httr, rvest, and dplyr.

It looks like your syntax is wrong. I think you’re trying to calculate a new variables in your data frame, or alter an existing column in a data frame. Have a look at the select() function in this reference for the proper syntax to use. https://dplyr.tidyverse.org/ Does that help?

A lot of populat libraries have their own logos. Maybe one of them would be good. Check out dplyr for example: https://dplyr.tidyverse.org/

I hope you give it a try when you get a chance: https://dplyr.tidyverse.org/

You can read up on the dplyr-verbs here, which I strongly suggest for your exam! In the code examples, you can simply click on any function you don't understand and it will take you directly to the documentation. Good Luck!

Thank you!!

From an extremely brief scan, it appears that Erlang wrappers around SQLite should be able to use the Graft SQLite extension just fine.

Alternatively, it would be reasonably straight forward to wrap Graft Client (Rust library) directly in an Erlang NIF using something like https://github.com/rusterlium/rustler

Let's make it happen! :)

Rust (for languages such as Erlang or Dart)

Currently, Pulumi Gestalt supports C and Wasm/Rust. Next week, I'll be working on native Rust support, which will also pave the way for other languages like Dart and Erlang.

Yep, I think you'd be hard-pressed to find the actual Elixir code be the bottleneck in a real-life application. But if you do encounter that, you can use something like Rustler[0] for the CPU-intensive bottleneck, as Discord did[1] while working on a data structure they needed. Slow DB queries are something else to look out for.

[0] https://github.com/rusterlium/rustler

[1] https://github.com/discord/sorted_set_nif

For performance intensive tasks, you could rely on Rust NIFs, there is this great project: https://github.com/rusterlium/rustler

My last project with Elixir was using Elixir merely as an orchestrator of static binaries (developed in golang) which were talking in JSON via stdin/stdout.

From the moment we discovered Tauri, we really felt like this was the perfect fit. The API is really solid, the configuration files are minimal and easy to understand, and the usage of Rust makes it way easier to add new functionalities and think about interesting ways of interoperating with Elixir via the Rustler library.

Elixir/Rust is the new Python/C++, and Rustler makes the communicating between the 2 languages super easy: https://github.com/rusterlium/rustler

Rustler is so awesome for this. Write Elixir NIFs in Rust? Yes, please!

But really, I would suggest thinking about what you want to build before "how" or "with which tool" - one of the signs of a person becoming a good engineer is having an array of tools at their disposal and being able to choose a correct tool for the correct task. Rust also excels in integrating with other languages - with JS via WebAssembly (a bit of self-promotion, for example), with Elixir via Rustler, with Python via PyO3 and PyOxidizer, etc. So you absolutely can start writing a frontend app with JS, or a distributed system with Elixir, or a data processing/ML app with Python and use Rust to speed up critical parts of those. Or, in reverse, you can start with Rust & add new capabilities to whatever you're building, that being a frontend, a resilient chat interface, or an ML model.