squarebrackets: Subset Methods as Alternatives to the Square Brackets Operators for Programming
Provides subset methods (supporting both atomic and recursive S3
classes) that may be more convenient alternatives to the [ and [<-
operators, whilst maintaining similar performance.
Some nice properties of these methods include, but are not limited to, the following:
- The
[and[<-operators use different rule-sets for different data.frame-like types (data.frames, data.tables, tibbles, tidytables, etc.). The ‘squarebrackets’ methods use the same rule-sets for the different data.frame-like types. - Performing dimensional subset operations on an array using
[and[<-, requires a-priori knowledge on the number of dimensions the array has. The ‘squarebrackets’ methods work on any arbitrary dimensions without requiring such prior knowledge. - When selecting names with the
[and[<-operators, only the first occurrence of the names are selected in case of duplicate names. The ‘squarebrackets’ methods always perform on all names in case of duplicates, not just the first. - The
[<-operator only supports copy-on-modify semantics for most classes. The ‘squarebrackets’ methods provides explicit pass-by-reference and pass-by-value semantics, whilst still respecting things like binding-locks and mutability rules. - ‘squarebrackets’ supports index-less sub-set operations, which is
more memory efficient (and better for the environment) for
long vectorsthan sub-set operations using the[and[<-operators.
To get started see the Get Started page.
One can install ‘squarebrackets’ from GitHub like so:
remotes::install_github("https://github.com/tony-aw/squarebrackets")Special care has been taken to make sure the function names are clear, and that the function names are unlikely to conflict with core R, the recommended R packages, the rstudioapi package, or major packages from the fastverse. So one can attach the package - thus exposing its functions to the namespace - using:
library(squarebrackets)