The {container} package offers an enhanced version of base R’s list
with a carefully designed set of extract, replace, and remove operations that make it easier and safer to work with list-like data structures.
Why use {container}?
{container} objects work similar to base R lists and on top provide
- safe and flexible operations to
- extract (custom default values, no unintended
NULL
) - add and replace (mixed indices, no unintended overrides)
- remove (loose or strict deletion, remove by index or value)
- extract (custom default values, no unintended
- compact printing
- optional reference semantics
In addition, {container} provides specialized data structures Deque, Set, and Dict and a special class dict.table
, designed to extend data.table by container operations to safely Manage data columns with dict.table.
Installation
# Install release version from CRAN
install.packages("container")
# Install development version from GitHub
devtools::install_github("rpahl/container")
Usage
library(container)
co <- container(colors = c("Red", "Green"), numbers = c(1, 2, 3), data = cars)
co
# [colors = ("Red" "Green"), numbers = (1 2 3), data = <<data.frame(50x2)>>]
Use like a base R list
co[["colors"]] <- c("Blue", "Yellow")
co[["colors"]]
# [1] "Blue" "Yellow"
co[2:1]
# [numbers = (1 2 3), colors = ("Blue" "Yellow")]
Safe extract
at(co, "colours") # oops
# Error: index 'colours' not found
at(co, "colors")
# [colors = ("Blue" "Yellow")]
Safe remove
co <- delete_at(co, "colours") # oops
# Error: names(s) not found: 'colours'
co <- delete_at(co, "colors")
co
# [numbers = (1 2 3), data = <<data.frame(50x2)>>]
Flexible peek
at(co, "colors") # oops
# Error: index 'colors' not found
peek_at(co, "colors")
# []
peek_at(co, "colors", .default = c("black", "white"))
# [colors = ("black" "white")]
Safe replace
co <- replace_at(co, num = 1:10) # oops
# Error: names(s) not found: 'num'
co <- replace_at(co, numbers = 1:10)
co
# [numbers = (1L 2L 3L 4L ...), data = <<data.frame(50x2)>>]
When not to use {container}
Don’t bother using the {container} framework when speed is of high importance. An exception is the dict.table
class, which is very fast as it is based on data.table. Other than that, if computation speed is critical for your application, we refer you to using base R lists or packages that were optimized for performance, such as the collections or cppcontainers package.