``` {r echo = FALSE, results = "hide"} knitr::opts_chunk$set(error = FALSE) human_no <- function(x) { s <- log10(floor(x + 1)) p <- c(0, thousand = 3, million = 6, billion = 9, trillion = 12) i <- s > p j <- max(which(i)) str <- names(p)[j] if (nzchar(str)) { paste(signif(x / 10^p[[j]], 3), str) } else { as.character(x) } } set.seed(1)

The `ids` package provides randomly generated ids in a number of
different forms with different readability and sizes.

## Random bytes

The `random_id` function generates random identifiers by generating
`bytes` random bytes and converting to hexadecimal (so each byte
becomes a pair of characters).  Rather than use R's random number
stream we use the `openssl` package here.
``` {r }

All ids functions take n as the first argument to be the number of identifiers generated:


The default here is 16 bytes, each of which has 256 values (so 256^16 = 2^128 = 3.4e38 combinations). You can make these larger or smaller with the bytes argument:

ids::random_id(5, 8)

If NULL is provided as n, then a generating function is returned (all ids functions do this):

f <- ids::random_id(NULL, 8)

This function sets all arguments except for n



The above look a lot like UUIDs but they are not actually UUIDs. The uuid package provides real UUIDs generated with libuuid, and the ids::uuid function provides an interface to that:


As above, generate more than one UUID:


Generate time-based UUIDs:

ids::uuid(4, use_time = TRUE)

and optionally drop the hyphens:

ids::uuid(5, drop_hyphens = TRUE)

Adjective animal

Generate (somewhat) human readable identifiers by combining one or more adjectives with an animal name.


The list of adjectives and animals comes from, via

Generate more than one identifier:


Use more than one adjective for very long idenfiers

ids::adjective_animal(4, 3)

``` {r echo = FALSE, results = "hide"} n1 <- length(ids:::gfycat_animals) n2 <- length(ids:::gfycat_adjectives)

There are `r n1` animal names and `r n2` adjectives so each one you
add increases the idenfier space by a factor of `r n2`.  So for 1,
2, and 3 adjectives there are about `r human_no(n1 * n2)`,
`r human_no(n1 * n2^2)` and `r human_no(n1 * n2^3)` possible combinations.

This is a much smaller space than the random identifiers above, but
these are more readable and memorable.

Note that here, the random nunbers are coming from R's random
number stream so are affected by `set.seed()`.

Because some of the animal and adjective names are very long
(e.g. a _quasiextraterritorial hexakosioihexekontahexaphobic
queenalexandrasbirdwingbutterfly_), in order to generate more
readable/memorable identifiers it may be useful to restrict the
length.  Pass `max_len` in to do this.
``` {r }
ids::adjective_animal(4, max_len = 6)

A vector of length 2 here can be used to apply to the adjectives and animal respectively:

ids::adjective_animal(20, max_len = c(5, Inf))

Note that this decreases the pool size and so increases the chance of collisions.

In addition to snake_case, the default, the punctuation between words can be changed to:


ids::adjective_animal(1, 2, style = "kebab")

ids::adjective_animal(1, 2, style = "dot")


ids::adjective_animal(1, 2, style = "camel")


ids::adjective_animal(1, 2, style = "pascal")


ids::adjective_animal(1, 2, style = "constant")

or with spaces, lower case:

ids::adjective_animal(1, 2, style = "lower")


ids::adjective_animal(1, 2, style = "upper")

Sentence case

ids::adjective_animal(1, 2, style = "sentence")

Title Case

ids::adjective_animal(1, 2, style = "title")

Again, pass n = NULL here to create a generating function:

aa3 <- ids::adjective_animal(NULL, 3, style = "kebab", max_len = c(6, 8))

...which can be used to generate ids on demand.


Random sentences

The sentence function creates a sentence style identifier. This uses the approach described by Asana on their blog. This approach encodes 32 bits of information (so 2^32 ~= 4 billion possibilities) and in theory can be remapped to an integer if you really wanted to.


As with adjective_animal, the case can be changed:

ids::sentence(2, "dot")

If you would rather past tense for the verbs, then pass past = TRUE:

ids::sentence(4, past = TRUE)


"proquints" are an identifier that tries to be information dense but still human readable and (somewhat) pronounceable; "proquint" stands for PRO-nouncable QUINT-uplets. They are introduced in

ids can generate proquints:


By default it generates two-word proquints but that can be changed:

ids::proquint(5, 1)
ids::proquint(2, 4)

Proquints are formed by alternating consonant/vowel/consonant/vowel/consonant using a subset of both (16 consonants and 4 vowels). This yields 2^16 (65,536) possibilities per word. Words are always lower case and always separated by a hyphen. So with 4 words there are 2^64 combinations in 23 characters.

Proquints are also useful in that they can be tranlated with integers. The proquint kapop has integer value 25258


This makes proquints suitable for creating human-pronouncable identifers out of things like ip addresses, integer primary keys, etc.

The function ids::int_to_proquint_word will translate between proquint words and integers (and are vectorised)

w <- ids::int_to_proquint_word(sample(2^16, 10) - 1L)

and ids::proquint_word_to_int does the reverse


whille ids::proquint_to_int and ids::int_to_proquint allows translation of multi-word proquints. Overflow is a real possibility; the maximum integer representable is only about r human_no(.Machine$integer.max) and the maximum floating point number of accuracy of 1 is about r human_no(2 / .Machine$double.eps) -- these are big numbers but fairly small proquints:

ids::int_to_proquint(.Machine$integer.max - 1)
ids::int_to_proquint(2 / .Machine$double.eps)

But if you had a 6 word proquint this would not work!

p <- ids::proquint(1, 6)

Too big for an integer: ``` {r error = TRUE} ids::proquint_to_int(p)

And too big for an numeric number:
``` {r error = TRUE}
ids::proquint_to_int(p, as = "numeric")

To allow this, we use openssl's bignum support:

ids::proquint_to_int(p, as = "bignum")

This returns a list with one bignum (this is required to allow vectorisation).

Roll your own identifiers

The ids functions can build identifiers in the style of adjective_animal or sentence. It takes as input a list of strings. This works particularly well with the rcorpora package which includes lists of strings.

Here is a list of Pokemon names:

pokemon <- tolower(rcorpora::corpora("games/pokemon")$pokemon$name)

...and here is a list of adjectives

adjectives <- tolower(rcorpora::corpora("words/adjs")$adjs)

So we have a total pool size of about r human_no(length(adjectives) * length(pokemon)), which is not huge, but it is at least topical.

To generate one identifier:

ids::ids(1, adjectives, pokemon)

All the style-changing code is available:

ids::ids(10, adjectives, pokemon, style = "dot")

Better would be to wrap this so that the constants are not passed around the whole time:

adjective_pokemon <- function(n = 1, style = "snake") {
  pokemon <- tolower(rcorpora::corpora("games/pokemon")$pokemon$name)
  adjectives <- tolower(rcorpora::corpora("words/adjs")$adjs)
  ids::ids(n, adjectives, pokemon, style = style)

adjective_pokemon(10, "kebab")

As a second example we can use the word lists in rcorpora to generate identifiers in the form <mood>_<scientist>, like "melancholic_darwin". These are similar to the names of docker containers.

First the lists of names themselves:

moods <- tolower(rcorpora::corpora("humans/moods")$moods)
scientists <- tolower(rcorpora::corpora("humans/scientists")$scientists)

Moods include:

sample(moods, 10)

The scientists names contain spaces which is not going to work for us because ids won't correctly translate all internal spaces to the requested style.

sample(scientists, 10)

To hack around this we'll just take the last name from the list and remove all hyphens:

scientists <- vapply(strsplit(sub("(-|jr\\.$)", "", scientists), " "),
                     tail, character(1), 1)

Which gives strings that are just letters (though there are a few non-ASCII characters here that may cause problems because string handling is just a big pile of awful)

sample(scientists, 10)

With the word lists, create an identifier:

ids::ids(1, moods, scientists)

Or pass NULL for n and create a function:

sci_id <- ids::ids(NULL, moods, scientists, style = "kebab")

which takes just the number of identifiers to generate as an argument


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ids documentation built on May 2, 2019, 2:08 a.m.