Package ‘rando’

July 23, 2025

Type Package
Title Context Aware Random Numbers
Version 0.2.0
Description Provides random number generating functions that
are much more context aware than the built-in functions. The
functions are also much safer, as they check for incompatible
values, and more reproducible.
Language en-GB
License MIT + file LICENSE

URL https://github.com/MyKo101/rando

BugReports https://github.com/MyKo101/rando/issues
Imports dplyr, glue, rlang, stats, tibble
Suggests spelling, covr, testthat
Encoding UTF-8
LazyData true
RoxygenNote 7.1.1
NeedsCompilation no
Author Michael Barrowman [cre, aut]
Maintainer Michael Barrowman <myko101ab@gmail.com>
Repository CRAN
Date/Publication 2021-02-16 15:40:02 UTC

Contents
rando-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
as_function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
blueprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
bp_where . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
default_n . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1
2 rando-package

extract_dots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
is_wholenumber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
match.call2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
null_switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
r_bern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
r_beta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
r_binom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
r_cauchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
r_cdf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
r_chisq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
r_exp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
r_fdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
r_gamma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
r_geom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
r_hyper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
r_letters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
r_lgl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
r_lnorm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
r_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
r_nbinom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
r_norm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
r_pois . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
r_sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
r_tdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
r_unif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
r_weibull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
seed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
set_n . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Index 37

rando-package Context Aware Random Number Generation

Description

rando is designed to make random number generation easier by providing the ability to set a default
number of numbers to generate or to assess the context in which the functions are being ran.
as_function 3

as_function Convert to function

Description
This function is a wrapper around rlang::as_function() which adds a two extra features:

• formulas can use .t in place of .x to be easier to understand in time-based functions

• functions can take additional named arguments.

Usage
as_function(x, env = parent.frame())

Arguments
x a function or formula, see rlang::as_function() for more information
env Environment in which to fetch the function in case x is a string

Value
Either:

• the function as it is passed to as_function(), whether as a string or a name

• the function derived from a formula, where the first argument is passed as ., .x or .t, the
second argument is passed as .y and any other named arguments are passed as they are named

Examples
f1 <- as_function(mean)
f1(1:10)

f2 <- as_function("sum")
f2(1,2,3)

f3 <- as_function(~.x + 1)
f3(9)

f4 <- as_function(~ .t + 1)
f4(10)

f5 <- as_function(~.x + .y)

f5(1,2)

f6 <- as_function(~ .t + alpha)

f6(10, alpha = 2)
4 blueprint

blueprint Blueprint a Dataset

Description
Allows for the generation of population based on a prescribed set of rando functions.

Usage
blueprint(...)

is_blueprint(bp)

Arguments
... arguments used to generate the blueprint, see Examples.
bp Object to check

Value
A function that will produce a tibble, which matches the blueprint that was provided. The generated
function will take the following arguments:

• ... - any arguments that are used within the blueprinting

• n - the number of rows that the resulting tibble should be
• .seed - the random seed to set before generating the data

is_blueprint() simply checks whether a function is a blueprinting function or not and returns a
logical.

Examples
make_tbl <- blueprint(
x = r_norm(),
y = r_norm()
)

make_tbl(n = 2)

make_tbl(n = 5)

# Blueprints can use additional parameters:

make_tbl2 <- blueprint(
x = r_norm(mean = x_mu),
y = r_unif(min = y_min, max = y_max)
)

# Which are simply passed to the generated function

bp_where 5

make_tbl2(x_mu = 10, y_min = -10, y_max = -5)

is_blueprint(make_tbl)

bp_where Blueprint based on a condition

Description
Runs a blueprint function where a condition is true, otherwise returns NA values

Usage
bp_where(condition, bp, ...)

Arguments
condition Condition to check before evaluating. Results will be given where this is TRUE,
and NA when this is FALSE
bp Blueprint function to run based on the condition
... arguments passed on to Blueprint, such as .seed

Value
a tibble

Examples
make_tbl <- blueprint(
x = r_norm(),
y = r_unif()
)

set_n(10)
i <- r_lgl()

bp_where(i, make_tbl)

df <- tibble::tibble(
id = 1:10,
cnd = r_lgl()
)
dplyr::mutate(df, bp_where(cnd, make_tbl))
6 default_n

default_n Find the Default Value for n in Context

Description
Checks for various information surrounding the call to this function to figure out what value for n
should be used

Usage
default_n(...)

blueprint_n()

tibble_n()

dplyr_n()

args_n(...)

Arguments
... parameters to check the lengths of

Details
The default_n() function will run through the other functions found here until it finds a viable
value for n.

It first checks for contxt to see if calls external to default_n() indicate which value should be
used:

• blueprint_n() - Checks if the function is being called within a blueprinting function, and
returns the value supplied to that function, see blueprint().
• tibble_n() - Checks if the function is being called within the declaration of a tibble. It then
checks the lengths of the other arguments being passed to the call. If you want to specify how
many rows should be generate you can use the .rows argument in your tibble() call, see
tibble()
• dplyr_n() - Checks if the function is being used within a dplyr verb, if so, it returns the
value of n()

It then checks the lengths of the arguments supplied via ..., if there is a discrepancy between these
arguments and the context aware value found above, it will throw an error.
If all the above values return 1 or NULL, we then check for a global n assigned by set_n(), if none
is set then default_n() will return 1.
extract_dots 7

Value
The context aware value for n

Examples
# Global Values:
set_n(NULL)
default_n()
set_n(10)
default_n()

# In a blueprint:
bp <- blueprint(x=r_norm(),n=default_n())
bp(n=7)
bp <- blueprint(x=r_norm(),n=blueprint_n())
bp(n=8)

# In a tibble:
tibble::tibble(id = 1:3, n = default_n())
tibble::tibble(id = 1:5, n = tibble_n())

# In a dplyr verb:
df <- tibble::tibble(id = 1:4)
dplyr::mutate(df, n = default_n())
dplyr::mutate(df, n = dplyr_n())

# From arguments:
default_n(1:5)
default_n(1:5,c("a","b","c","d","e"))
args_n(1:3,c("a","b","d"))
args_n(1:3, 1:4)

## Not run:
default_n(1:3, 1:4)
tibble::tibble(id=1:5,n=default_n(1:4))

## End(Not run)

extract_dots Extract the ellipsis inside a function

Description
Allow the named entries in ... to be used easily within a function by attaching them to the func-
tion’s environment

Usage
extract_dots()
8 is_wholenumber

Value

No return value, called for it’s side effect

Examples
f <- function(...) {
a + b
}

## Not run:
# Throws an error because a and b are trapped inside `...`
f(a = 1, b = 2)

## End(Not run)

f <- function(...) {
extract_dots()
a + b
}
f(a = 1, b = 2)

is_wholenumber Check if a Number is Whole

Description

The built-in function is.integer() will check if a number is of the integer class. However, we
would usually want a function that can check if a number is a whole number. It is also vectorised
over the input.

Usage

is_wholenumber(x, tol = .Machine$double.eps^0.5)

Arguments

x Number to check
tol tolerance to check the values

Value

A logical vector the same length as x

logit 9

Examples

is.integer(2)
is_wholenumber(2)

is.integer(seq(2, 3, 0.25))
is_wholenumber(seq(2, 3, 0.25))

logit The logit and inverse logit functions

Description

Calculates the logit or the inverse logit of a value

Usage

logit(prob, base = exp(1))

invlogit(alpha, base = exp(1))

Arguments

prob vector of probabilities

base base of the logarithmic function to use
alpha vector of values to find the inverse logit of

Value

A numeric vector

Examples

logit(0.5)

logit(seq(0.01, 0.99, 0.01))

invlogit(-10:10)
10 match.call2

match.call2 Alternate Parametrisation of match.call()

Description
Alters the built-in function match.call() by providing an additional argument which means that
by default a user can specify how far up the call stack they want to match a call of. See match.call()
for more details.

Usage
match.call2(
n = 0L,
definition = sys.function(sys.parent(n + 1L)),
call = sys.call(sys.parent(n + 1L)),
expand.dots = TRUE,
envir = parent.frame(n + 3L)
)

Arguments
n How far up the call-stack they would like to extract. The default, n=0 produces
the same result as match.call() so this can be inserted wherever match.call()
is used.
definition a function, by default the function from which match.call2() is called.
call an unevaluated call to the function specified by definition, as generated by
call
expand.dots logical. Should arguments matching ... in the call be included or left as a ...
argument?
envir an environment, from which the ... in call are retrieved, if any.

Value
An object of class call

Examples
f <- function(n) {
g(n)
}

g <- function(n) {
h(n)
}

h <- function(n) {
match.call2(n)
null_switch 11

f(0)
f(1)
f(2)

null_switch Evaluate Expressions until not NULL

Description

Evaluates expressions until one that is not NULL is encountered and returns that. Expressions after
the first non-NULL result are not evaluated. If all expressions are NULL, it will return NULL

Usage

null_switch(...)

Arguments

... expressions to try to evaluate

Value

The result of evaluating one of the expressions. Will only be NULL if they all evaluated to NULL

Examples

f <- function() {
cat("Evaluating f\n")
NULL
}
g <- function() {
cat("Evaluating g\n")
2
}

null_switch(NULL, f(), g())

null_switch(NULL, g(), f())
null_switch(f(), f(), f())
12 r_bern

r_bern Generate Bernoulli Distributed Values

Description

Generates a set of Bernoulli distributed values.

Usage

r_bern(prob = 0.5, ..., n = default_n(prob), .seed = NULL)

Arguments

prob vector of probability of successes, between 0 & 1

... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value

A numeric vector of length n

Examples
set_n(5)

r_bern(0.9)

r_bern(seq(0, 1, 0.1))

r_bern(1 / 4, n = 10)
r_beta 13

r_beta Generate Beta Distributed Values

Description

Generates a set of Beta distributed values.

Usage

r_beta(alpha, beta, ..., n = default_n(alpha, beta), .seed = NULL)

Arguments

alpha, beta vectors of shape parameters, strictly positive

... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value

A numeric vector of length n

Examples
set_n(5)

r_beta(1, 1)

r_beta(1:10, 2)

r_beta(1, 2, n = 10)
14 r_binom

r_binom Generate Binomial Distributed Values

Description
Generates a set of Binomial distributed values.

Usage
r_binom(size, prob = 0.5, ..., n = default_n(size, prob), .seed = NULL)

Arguments
size vector of number of trials, positive integer
prob vector of probabilities of success on each trial, between 0 & 1
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_binom(10)

r_binom(1:10)

r_binom(10, 0.2)

r_binom(1, 0.2, n = 10)

r_cauchy 15

r_cauchy Generate Cauchy Distributed Values

Description
Generates a set of Cauchy distributed values.

Usage
r_cauchy(
location = 0,
scale = 1,
...,
n = default_n(location, scale),
.seed = NULL
)

Arguments
location vector of locations
scale vector of scales, strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_cauchy(10)

r_cauchy(1:10)

r_cauchy(10, 2)
16 r_cdf

r_cauchy(10, 2, n = 10)

r_cdf Generate Random Numbers Based on an arbitrary CDF

Description
Generates Random Numbers based on a distribution defined by any arbitrary cumulative distribution
function

Usage
r_cdf(
Fun,
min = -Inf,
max = Inf,
...,
data = NULL,
n = default_n(..., data),
.seed = NULL
)

Arguments
Fun function to use as the cdf. See details
min, max range values for the domain of the Fun
... arguments that can be passed to Fun
data data set containing arguments to be passed to Fun
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Details
The Fun argument accepts purrr style inputs. Must be vectorised, defined on the whole Real line
and return a single numeric value between 0 and 1 for any input. The random variable will be
passed to Fun as the first argument. This means that R’s argument matching can be used with
named arguments in ... if a different positional argument is wanted.
r_chisq 17

Value
A numeric vector of length n

Examples
set_n(5)

my_fun <- function(x, beta = 1) {

1 - exp(-beta * x)
}

r_cdf(my_fun)

r_cdf(~ 1 - exp(-.x), min = 0)

r_cdf(~ 1 - exp(-.x * beta), beta = 1:10, min = 0)

r_chisq Generate Chi-Squared Distributed Values

Description
Generates a set of Chi-Squared distributed values.

Usage
r_chisq(df, ..., n = default_n(df), .seed = NULL)

Arguments
df degrees of freedom, strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n
18 r_exp

Examples
set_n(5)

r_chisq(10)

r_chisq(1:10)

r_chisq(10, n = 10)

r_exp Generate Exponentially Distributed Values

Description
Generates a set of Exponentially distributed values.

Usage
r_exp(rate = 1, ..., n = default_n(rate), .seed = NULL)

Arguments
rate vector of rates, strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_exp(10)

r_exp(1:10)

r_exp(10, n = 10)
r_fdist 19

r_fdist Generate F Distributed Values

Description

Generates a set of F distributed values.

Usage

r_fdist(df1, df2, ..., n = default_n(df1, df2), .seed = NULL)

Arguments

df1, df2 vectors of degrees of freedom, strictly positive

... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value

A numeric vector of length n

Examples
set_n(5)

r_fdist(1, 1)

r_fdist(1:10, 2)

r_fdist(10, 2)

r_fdist(10, 2, n = 10)
20 r_gamma

r_gamma Generate Gamma Distributed Values

Description
Generates a set of Gamma distributed values. Can be defined by one and only one of scale, rate
or mean. This must be named in the call.

Usage
r_gamma(
shape,
...,
scale = 1,
rate = NULL,
mean = NULL,
n = default_n(shape, scale, rate, mean),
.seed = NULL
)

Arguments
shape vector of shape parameters, strictly positive
... Unused
scale vector of scale parameters, cannot be specified with rate and mean, strictly
positive
rate vector of rate parameters, cannot be specified with scale and mean, strictly
positive
mean vector of mean parameters, cannot be specified with scale and rate, strictly
positive
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n
r_geom 21

Examples
set_n(5)

r_gamma(10)

r_gamma(1:10, scale = 2)
r_gamma(1:10, rate = 1 / 2)
r_gamma(1:10, mean = 5)

r_gamma(10, n = 10)

r_geom Generate Geometric Distributed Values

Description

Generates a set of Geometric distributed values.

Usage

r_geom(prob = 0.5, ..., n = default_n(prob), .seed = NULL)

Arguments

prob vector of probability of success, must strictly greater than 0 and (non-strictly)
less than 1, i.e. 0 < prob <= 1
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value

A numeric vector of length n

22 r_hyper

Examples
set_n(5)

r_geom(0.1)

r_geom(seq(0.1, 1, 0.1))

r_geom(0.1, n = 10)

r_hyper Generate Hypergeometric Distributed Values

Description
Generates a set of Hypergeometric distributed values.

Usage
r_hyper(
total,
positives,
num,
...,
n = default_n(total, positives, num),
.seed = NULL
)

Arguments
total size of the population (e.g. number of balls)
positives number of elements with the desirable feature (e.g number of black balls)
num number of draws to make
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()
r_letters 23

Value
A numeric vector of length n

Examples
set_n(5)

r_hyper(10, 5, 5)

r_hyper(10:20, 10, 5)

r_hyper(10, 5, 5, n = 10)

r_letters Generate Random Letters

Description
Generates a set of Random Letters.

Usage
r_letters(nchar = 1, ..., n = default_n(nchar), .seed = NULL)

r_LETTERS(nchar = 1, ..., n = default_n(nchar), .seed = NULL)

r_Letters(nchar = 1, ..., n = default_n(nchar), .seed = NULL)

Arguments
nchar vector of number of characters to return, positive integer
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A character vector of length n
24 r_lgl

Functions
• r_letters: Uses only lower-case letters
• r_LETTERS: Uses only upper-case letters
• r_Letters: Uses lower- & upper-case letters

Examples
set_n(5)

r_letters(3)

r_letters(1:10)

r_letters(3, n = 10)

r_LETTERS(3)

r_LETTERS(1:10)

r_LETTERS(3, n = 10)

r_Letters(3)

r_Letters(1:10)

r_Letters(3, n = 10)

r_lgl Generate Logical Values

Description
Generates a set of Logical values.

Usage
r_lgl(prob = 0.5, ..., n = default_n(prob), .seed = NULL)

Arguments
prob vector of probability of TRUE results, between 0 & 1
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
r_lnorm 25

• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A logical vector of length n

Examples
set_n(5)

r_lgl(0.9)

r_lgl(seq(0, 1, 0.1))

r_lgl(1 / 4, n = 10)

r_lnorm Generate Log Normal Distributed Values

Description
Generates a set of Log Normal distributed values.

Usage
r_lnorm(
mean_log = 0,
sd_log = 1,
...,
n = default_n(mean_log, sd_log),
.seed = NULL
)

Arguments
mean_log vector of means (on the log scale)
sd_log vector of standard deviations (on the log scale), strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
26 r_matrix

• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_lnorm(10)

r_lnorm(10, 2)

r_lnorm(1:10)

r_lnorm(-2, n = 10)

r_matrix Generate a random Matrix

Description
Generate a random matrix, given a rando function and it’s dimensions. By default, this will generate
a square matrix.

Usage
r_matrix(
engine,
row_names = NULL,
col_names = NULL,
...,
nrow = default_n(row_names),
ncol = default_n(col_names),
.seed = NULL
)

Arguments
engine The rando function that will be used to generate the random numbers
col_names, row_names
names to be assigned to the rows or columns. This is also used in deciding the
dimensions of the result.
r_nbinom 27

... Unused
nrow, ncol dimensions of the matrix. The default_n() function will provide a default
value within context.
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A matrix with nrow rows and ncol columns an a type as decided by the function passed to engine.

Examples
set_n(5)

r_matrix(r_norm)

r_matrix(r_unif,min=1,max=2)

r_matrix(r_norm,mean=10,sd=2,ncol=2)

r_nbinom Generate Negative Binomial Distributed Values

Description
Generates a set of Negative Binomial distributed values. Only two of r, prob and mu can be pro-
vided.

Usage
r_nbinom(
r = NULL,
prob = 0.5,
...,
mu = NULL,
n = default_n(r, prob, mu),
.seed = NULL
)
28 r_norm

Arguments
r number of failure trials until stopping, strictly positive
prob vector of probabilities of success on each trial, between 0 & 1
... Unused
mu vector of means
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Note
It is important to note that this is the number of failures, and not the number of successes, as in
rnbinom(), so rnbinom(prob = x,...) is equivalent to r_nbinom(prob=1-x,...)

Examples
set_n(5)

r_nbinom(10, 0.5)

r_nbinom(1:10, mu = 2)
#'
r_nbinom(10, 0.2, n = 10)

r_norm Generate Normally Distributed Values

Description
Generates a set of Normally distributed values.

Usage
r_norm(mean = 0, sd = 1, ..., n = default_n(mean, sd), .seed = NULL)
r_pois 29

Arguments

mean vector of means

sd vector of standard deviations, strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value

A numeric vector of length n

Examples
set_n(5)

r_norm(10)

r_norm(10, 2)

r_norm(1:10)

r_norm(-2, n = 10)

r_pois Generate Poisson Distributed Values

Description

Generates a set of Poisson distributed values.

Usage

r_pois(rate, ..., n = default_n(rate), .seed = NULL)

30 r_sample

Arguments
rate vector of rates, strictly positive
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_pois(10)

r_pois(1:10)

r_pois(10, n = 10)

r_sample Generate Random Sample

Description
Generates a Sample from a set, with replacement

Usage
r_sample(sample, weights = NULL, ..., n = default_n(), .seed = NULL)

Arguments
sample a set of values to choose from
weights a vector of weights, must be the same length as sample, between 0 & 1
... Unused
r_tdist 31

n number of observations to generate. The default_n() function will provide a

default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A vector of length n of the same type as sample

Examples
set_n(15)

r_sample(c("blue", "red", "yellow"))

r_sample(c("blue", "red", "yellow"),

weights = c(1, 5, 1)
)

r_sample(c("blue", "red", "yellow"), n = 10)

r_tdist Generate T Distributed Values

Description
Generates a set of Student’s T distributed values.

Usage
r_tdist(df, ..., n = default_n(df), .seed = NULL)

Arguments
df vector of degrees of freedom
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
32 r_unif

• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Value
A numeric vector of length n

Examples
set_n(5)

r_tdist(10)

r_tdist(1:10)

r_tdist(10, n = 10)

r_unif Generate Uniformly Distributed Values

Description
Generates a set of Uniformly distributed values.

Usage
r_unif(min = 0, max = 1, ..., n = default_n(min, max), .seed = NULL)

Arguments
min, max vectors of lower and upper limits of the distribution
... Unused
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()
r_weibull 33

Value
A numeric vector of length n

Examples
set_n(5)

r_unif()

r_unif(1:5, 6:10)

r_unif(1:5, 10)

r_unif(n = 10)

r_weibull Generate Weibull Distributed Values

Description
Generates a set of Weibull distributed values.

Usage
r_weibull(
shape,
scale = 1,
...,
b_scale = NULL,
B_scale = NULL,
n = default_n(shape, scale, b_scale, B_scale),
.seed = NULL
)

Arguments
shape vector of shape parameters, strictly positive
scale vector of scale parameters, strictly positive
... Unused
b_scale, B_scale
alternative definition of scale parameter, cannot be provided with scale, strictly
positive.
n number of observations to generate. The default_n() function will provide a
default value within context
.seed One of the following:
34 seed

• NULL (default) will not change the current seed. This is the usual case for
generating random numbers.
• A numeric value. This will be used to set the seed before generating the
random numbers. This seed will be stored with the results.
• TRUE. A random seed value will be generated and set as the seed before
the results are generated. Again, this will be stored with the results.
To extract the random seed from a previously generated set of values, use pull_seed()

Details
This function provides alternative definitions for the scale parameter depending on the user’s
parametrisation of the Weibull distribution, with k = shape.
Using λ = scale:
F (x) = 1 − exp(−(x/λ)k )

Using b = b_scale:
F (x) = 1 − exp(−bxk )

Using β = B_scale:
F (x) = 1 − exp(−(βx)k )

Value
A numeric vector of length n

Examples
set_n(5)

r_weibull(10)

r_weibull(1:10, 2)

r_weibull(1:10, scale = 2)
r_weibull(1:10, b_scale = 2)
r_weibull(1:10, B_scale = 2)

r_weibull(10, 2, n = 10)

seed Random Seed Defining Functions

Description
Functions related to generating random seeds and utilising them for reproducibility.
seed 35

Usage
gen_seed()

set_seed(seed)

fix_seed(reset = FALSE)

with_seed(seed, expression)

pull_seed(x)

Arguments
seed The random seed to be used
reset Should the fixed seed be forced to reset
expression expression to be evaluated
x object to extract the seed from

Details
Random values are generated based on the current seed used by the R system. This means by
deliberately setting a seed in R, we can make work reproducible.

Value
gen_seed() returns a single numeric value
with_seed() returns the value of the evaluated expression after with the relevant seed as an attribute
(if required)
pull_seed() returns a single numeric value
fix_seed() and set_seed() do not return anything

Functions
• gen_seed: Generates a random seed, which can be used in set_seed()
• set_seed: Sets the current seed
• fix_seed: Resets the seed to re-run code
• with_seed: Evaluates the expression after setting the seed. If seed is TRUE, then it first
generates a seed using gen_seed(). Results are output with the seed attached (if set).#’
• pull_seed: Extracts the seed used to generate the results of with_seed()

Examples
my_seed <- gen_seed()

set_seed(my_seed)
36 set_n

r_norm(n=10)
set_seed(my_seed)
r_norm(n=10)

fix_seed()
r_norm(n=3)

fix_seed()
r_norm(n=3)

fix_seed(reset=TRUE)
r_norm(n=3)

res <- with_seed(my_seed, r_norm(n = 10))

res

pull_seed(res)

set_n Set and Get the Default Value for n

Description
Set and get the global value for n for rando functions

Usage
set_n(n)

get_n()

Arguments
n value to set as the default n

Value
The current global default value for n.
set_n() returns this value invisibly

Examples
set_n(100)

get_n()
Index

args_n (default_n), 6 r_gamma, 20

as_function, 3 r_geom, 21
r_hyper, 22
blueprint, 4 r_LETTERS (r_letters), 23
blueprint(), 6 r_Letters (r_letters), 23
blueprint_n (default_n), 6 r_letters, 23
bp_where, 5 r_lgl, 24
r_lnorm, 25
default_n, 6 r_matrix, 26
default_n(), 12–25, 27–33 r_nbinom, 27
dplyr, 6 r_norm, 28
dplyr_n (default_n), 6 r_pois, 29
r_sample, 30
extract_dots, 7
r_tdist, 31
fix_seed (seed), 34 r_unif, 32
r_weibull, 33
gen_seed (seed), 34 rando-package, 2
get_n (set_n), 36 rlang::as_function(), 3

invlogit (logit), 9 seed, 34

is_blueprint (blueprint), 4 set_n, 36
is_wholenumber, 8 set_n(), 6
set_seed (seed), 34
logit, 9
tibble, 4, 5
match.call(), 10 tibble(), 6
match.call2, 10 tibble_n (default_n), 6
n(), 6 with_seed (seed), 34
null_switch, 11

pull_seed (seed), 34

r_bern, 12
r_beta, 13
r_binom, 14
r_cauchy, 15
r_cdf, 16
r_chisq, 17
r_exp, 18
r_fdist, 19

37