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container

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)
  • 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)>>]

Get started

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.