Introduction to container

When not to use container

Don’t bother using the container framework if 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 list or packages that were optimized for performance, such as the collections package.

When to use container

Consider using the container framework over base list if you are looking for …

feature-rich and safe access, replace, and remove operations for robust code
feature rich specialized data structures beyond lists
lists with reference semantics.

Furthermore consider using dict.table for a flexible and robust way to manage data columns of data.tables.

container in interactive R session

In an interactive R session a container can be used similar to a base R list, but also provides some extra features. For easier typing it’s recommended to use the shorter cont.

library(container)
co = cont(a = 1, b = 1:10)  # same as co = container(a = 1, b = 1:10)

The container print method is designed to be very compact.

print(co)
# [a = 1, b = (1L 2L 3L 4L ...)]

For more verbose output, either convert to a base list …

as.list(co)
# $a
# [1] 1
# 
# $b
#  [1]  1  2  3  4  5  6  7  8  9 10

or use str.

str(co)
# Container of 2 
#  $ a: num 1
#  $ b: int [1:10] 1 2 3 4 5 6 7 8 9 10

Both length and names work as usual.

length(co)
# [1] 2

names(co)
# [1] "a" "b"

names(co)[1] <- "A"
co
# [A = 1, b = (1L 2L 3L 4L ...)]

A container can also be constructed from a list.

l = list(x = (1:2)^1, y = (1:2)^2)
co2 = as.container(l)
co2
# [x = (1 2), y = (1 4)]

Add elements

Elements can be added by concatenation,

c(co, co2)
# [A = 1, b = (1L 2L 3L 4L ...), x = (1 2), y = (1 4)]

or name,

co[["c"]] <- 3
co
# [A = 1, b = (1L 2L 3L 4L ...), c = 3]

and containers can be nested.

co[["co2"]] <- co2
co
# [A = 1, b = (1L 2L 3L 4L ...), c = 3, co2 = [x = (1 2), y = (1 4)]]

In contrast to base R list, elements cannot be added via positional index if it exceeds the container’s length.

co[[5]] <- 5
# Error: index out of range (length = 4): 5

Replace values

Single or multiple value replacement works as usual.

co[[3]] <- 0
co[1:2] <- 0
co
# [A = 0, b = 0, c = 0, co2 = [x = (1 2), y = (1 4)]]

In contrast to base list, containers can take a mix of numeric and character indices.

co[list("A", 2, "c")] <- list(1, 2, "three")
co
# [A = 1, b = 2, c = "three", co2 = [x = (1 2), y = (1 4)]]

Another option for container object is to replace by value.

co[[{"three"}]] <- 3
co
# [A = 1, b = 2, c = 3, co2 = [x = (1 2), y = (1 4)]]

This works for any data type.

co[[{co2}]] <- 3
co
# [A = 1, b = 2, c = 3, co2 = 3]

Extract

The standard operators to access elements also should be familiar to R users. ¹

co[[1]]
# [1] 1
co[1:3]
# [A = 1, b = 2, c = 3]

As another option, you can pass any number of indices, possibly mixed as numeric and character.

co[1, 3, "b"]
# [A = 1, c = 3, b = 2]

co[2:1, "A"]
# [b = 2, A = 1, A = 1]

Invalid indices don’t produce NULLs but are just ignored.

co[1:100]
# [A = 1, b = 2, c = 3, co2 = 3]

Inspect

Count the number of elements.

count(co, 1)
# [1] 1
count(co, 3)
# [1] 2

Use the apply family.

sapply(co, is.numeric)
#    A    b    c  co2 
# TRUE TRUE TRUE TRUE

sapply(co, function(x) x + 1)
#   A   b   c co2 
#   2   3   4   4

container in code development

Next, see vignette Container operations for robust code.