I just learned of the existence of the tidypolars package (from the R polars readme itself), which uses polars under the hood to "enable users to keep their existing tidyverse code while using polars in the background to benefit from large performance gains." That way, you can avoid going back and forth converting to tibble.
No idea. The base pipe has some limitations. Like not working with the dot operator. So I stick with what works. No need to complicate it by switching without a benefit (and possibly breaking code)
@@BusinessScience Well, the library() command loads an R *package* (e.g., the 'polars' package), which must be available in a library (tree) and which may link to (shared/dynamic/static) libraries, as explained in the introduction to the official R documentation "Writing R Extensions": "A package is a directory of files which extend R, a source package (the master files of a package), or a tarball containing the files of a source package, or an installed package, the result of running R CMD INSTALL on a source package. On some platforms (notably macOS and ‘x86_64’ Windows) there are also binary packages, a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources. A package is not1 a library. The latter is used in two senses in R documentation. A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it is sometimes referred to as a library directory or library tree (since the library is a directory which contains packages as directories, which themselves contain directories). That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL. The concept of a shared library (dynamic library on macOS) as a collection of compiled code to which a package might link is also used, especially for R itself on some platforms. On most platforms these concepts are interchangeable (shared objects and DLLs can both be loaded into the R process and be linked against), but macOS distinguishes between shared objects (extension .so) and dynamic libraries (extension .dylib)." cran.r-project.org/doc/manuals/R-exts.html
Yes, I really like Polars now and made me dislike Pandas, and that's because Polars has more readable syntaxes than Pandas, no I never like Pandas from the start. Maybe you can try tidypolars.
I just learned of the existence of the tidypolars package (from the R polars readme itself), which uses polars under the hood to "enable users to keep their existing tidyverse code while using polars in the background to benefit from large performance gains." That way, you can avoid going back and forth converting to tibble.
I was looking forward to this library. Thank you for sharing.
@@RenatoVargas you got it!
Is there any advantage over data.table package?
Hello! Thank you for the content! I’m curious about which is the rstudio theme you are using. Could you share it? Thanks!
Thanks! I believe it’s one of the dark themes that comes with RStudio
You can't tell me they didn't name it "PolaRs"
They missed the opportunity of doing something extremely funny 😭😭😭😭
lol what a missed opportunity 😅
😢😂
Doesn't Hadley recommend the base pipe instead now? |>
No idea. The base pipe has some limitations. Like not working with the dot operator. So I stick with what works. No need to complicate it by switching without a benefit (and possibly breaking code)
🔥🔥🔥
Warning in install.packages :
package ‘polars’ is not available (for R version 3.5.3) :( unlucky
Polars is not on CRAN. You’ll need to install with GitHub.
BTW: ‘polars’ is a *package*, not a library…
No it’s a library.
@@BusinessScience Well, the library() command loads an R *package* (e.g., the 'polars' package), which must be available in a library (tree) and which may link to (shared/dynamic/static) libraries, as explained in the introduction to the official R documentation "Writing R Extensions": "A package is a directory of files which extend R, a source package (the master files of a package), or a tarball containing the files of a source package, or an installed package, the result of running R CMD INSTALL on a source package. On some platforms (notably macOS and ‘x86_64’ Windows) there are also binary packages, a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.
A package is not1 a library. The latter is used in two senses in R documentation.
A directory into which packages are installed, e.g. /usr/lib/R/library: in that sense it is sometimes referred to as a library directory or library tree (since the library is a directory which contains packages as directories, which themselves contain directories).
That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL. The concept of a shared library (dynamic library on macOS) as a collection of compiled code to which a package might link is also used, especially for R itself on some platforms. On most platforms these concepts are interchangeable (shared objects and DLLs can both be loaded into the R process and be linked against), but macOS distinguishes between shared objects (extension .so) and dynamic libraries (extension .dylib)."
cran.r-project.org/doc/manuals/R-exts.html
@@BusinessScience to be more precise, a NAMESPACE and a module
Yes, I really like Polars now and made me dislike Pandas, and that's because Polars has more readable syntaxes than Pandas, no I never like Pandas from the start. Maybe you can try tidypolars.