Why?

Use defaultlist objects if you would like to:

• avoid having to initialise for every entry
• avoid having to check whether an entry exists before accessing

What’s in the box?

• dlist(..., .default) - used to construct a new defaultlist. Same syntax as list() but needs an additional argument to set the default value
• empty_dlist(.default) - create an empty list with the given default.
• as.dlist() - identical to as.list() except it requires an additional argument to set the default value

Installation

You can install from GitHub with:

# install.package('remotes')
remotes::install_github('coolbutuseless/defaultlist')

Basic Example

In this example a defaultlist is created which will return FALSE for any access to an unknown element. Besides this, it will bevave identically to a normal list() object.

haystack <- empty_dlist(FALSE)
haystack$surprise <- TRUE

haystack[['hello']]
[1] FALSE
haystack$mcfly
[1] FALSE
haystack[['surprise']]
[1] TRUE
haystack$surprise
[1] TRUE

haystack[11:13]
[[1]]
[1] FALSE

[[2]]
[1] FALSE

[[3]]
[1] FALSE

[.default = FALSE]
haystack[c('x', 'y')]
$x
[1] FALSE

$y
[1] FALSE

[.default = FALSE]

Example: Counters

When counting from a stream of unknown objects, using a normal list can get a little messy as for each object pulled from the stream there needs to be an explicit check to see if the value is already in the list or not.

Using a defaultlist lets you avoid lots of checking, and assume that for any object not in the list, the count is zero.

census <- empty_dlist(0)

population <- c('andy', 'bob', 'carol', 'carol')

for (name in population) {
  census[[name]] <- census[[name]] + 1
}

census

$andy
[1] 1

$bob
[1] 1

$carol
[1] 2

[.default = 0]

census$mike

[1] 0

Extra Credit Example - using nested defaultlists

In this example, nested defaultlists are used to determine the most common pairing of elements in a stream of unknown values.

This implementation does not need to know anything about the possible values in the stream beforehand, and does not need to explicitly test for list membership for every new token plucked from the stream.

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Create a stream of characters heavily weighted towards 'a' and 'e'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
set.seed(1)
stream <- sample(letters[1:5], 1000, replace = TRUE, prob = c(5, 1, 1, 1, 4))
head(stream)

[1] "a" "a" "e" "d" "a" "d"

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Create nested defaultlists
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
counter <- empty_dlist(empty_dlist(0))

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Count the pair of characters (prev, this)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
for (idx in 2:length(stream)) {
  this <- stream[[idx]]
  prev <- stream[[idx  - 1]]
  counter[[prev]][[this]] <- counter[[prev]][[this]] + 1
}

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The most probable letter pair in the stream is: a-a
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
sort(unlist(counter), decreasing = TRUE)

a.a a.e e.a e.e a.b b.a b.e d.a a.c e.b a.d c.e c.a e.c e.d d.e b.b c.b c.c d.b 
178 138 137 108  41  39  37  35  32  32  30  30  30  28  27  19   8   8   8   7 
d.c b.d b.c d.d c.d 
  7   7   5   4   4 

Related Software

• Python’s defaultdict
• R’s collections package

Acknowledgements

• R Core & CRAN maintainers for giving me a playground
• Hadley Wickham for testthat which allowed test-driver-development, without which this package never would have worked.