``` {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 } ids::random_id()
All ids
functions take n
as the first argument to be the number
of identifiers generated:
ids::random_id(5)
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) f
This function sets all arguments except for n
f() f(4)
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:
ids::uuid()
As above, generate more than one UUID:
ids::uuid(4)
Generate time-based UUIDs:
ids::uuid(4, use_time = TRUE)
and optionally drop the hyphens:
ids::uuid(5, drop_hyphens = TRUE)
Generate (somewhat) human readable identifiers by combining one or more adjectives with an animal name.
ids::adjective_animal()
The list of adjectives and animals comes from gfycat.com, via https://github.com/a-type/adjective-adjective-animal
Generate more than one identifier:
ids::adjective_animal(4)
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:
kebab-case:
ids::adjective_animal(1, 2, style = "kebab")
dot.case:
ids::adjective_animal(1, 2, style = "dot")
camel-case:
ids::adjective_animal(1, 2, style = "camel")
PascalCase:
ids::adjective_animal(1, 2, style = "pascal")
CONSTANT_CASE (aka SHOUTY_CASE)
ids::adjective_animal(1, 2, style = "constant")
or with spaces, lower case:
ids::adjective_animal(1, 2, style = "lower")
UPPER CASE
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.
aa3() aa3(4)
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.
ids::sentence()
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 https://arxiv.org/html/0901.4016
ids
can generate proquints:
ids::proquint(10)
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
ids::proquint_to_int("kapop") ids::int_to_proquint(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) w
and ids::proquint_word_to_int
does the reverse
ids::proquint_word_to_int(w)
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$double.eps)
But if you had a 6 word proquint this would not work!
p <- ids::proquint(1, 6)
Too big for an integer: ``` {r error = TRUE} ids::proquint_to_int(p)
And too big for an numeric number: ``` {r error = TRUE} ids::proquint_to_int(p, as = "numeric")
To allow this, we use openssl
's bignum
support:
ids::proquint_to_int(p, as = "bignum")
This returns a list with one bignum (this is required to allow vectorisation).
The ids
functions can build identifiers in the style of
adjective_animal
or sentence
. It takes as input a list of
strings. This works particularly well with the rcorpora
package
which includes lists of strings.
Here is a list of Pokemon names:
pokemon <- tolower(rcorpora::corpora("games/pokemon")$pokemon$name) length(pokemon)
...and here is a list of adjectives
adjectives <- tolower(rcorpora::corpora("words/adjs")$adjs) length(adjectives)
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
sci_id(10)
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