The maybe type represents the possibility of some value or nothing. It
is often used instead of throwing an error or returning an undefined
value like NA
or NULL
. The advantage of using a maybe type is that
the functions which work with it are both composable and require the
developer to explicitly acknowledge the potential absence of a value,
helping to avoid unexpected behavior.
You can install the released version of maybe from CRAN with:
install.packages("maybe")
And the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("armcn/maybe")
The following example shows how the maybe package can be used to create a safe data processing pipeline.
library(maybe)
safe_filter <- maybe(dplyr::filter, ensure = not_empty)
safe_mean <- maybe(mean, ensure = not_undefined)
safe_pull <- maybe(dplyr::pull)
mean_mpg_of_cyl <- function(.cyl) {
mtcars %>%
safe_filter(cyl == .cyl) %>%
and_then(safe_pull, mpg) %>%
and_then(safe_mean) %>%
with_default(0)
}
mean_mpg_of_cyl(8L)
#> [1] 15.1
mean_mpg_of_cyl(100L)
#> [1] 0
Here is an example of working with data stored in JSON format.
library(purrr)
parse_numbers <-
function(x) filter_map(x, maybe(as.numeric))
safe_first <-
maybe(function(x) x[[1]], ensure = not_empty)
sum_first_numbers <- function(json) {
jsonlite::fromJSON(json) %>%
filter_map(compose(safe_first, parse_numbers)) %>%
perhaps(reduce, default = 0)(`+`)
}
sum_first_numbers('{"a": [], "b": [1, 2.2, "three"], "c": [3]}')
#> [1] 4
sum_first_numbers('{}')
#> [1] 0
sum_first_numbers('1, 2, 3')
#> [1] 0
Maybe values can be used to model computations that may fail or have undefined outputs. For example, dividing by zero is mathematically undefined but in many programming languages, including R, infinity is returned. If it is not properly accounted for this may cause unexpected behavior later in the program. The maybe type can be used to improve the safety of the divide function.
divide <- function(a, b) {
a / b
}
safe_divide <- function(a, b) {
if (b == 0) nothing() else just(a / b)
}
divide(10, 2)
#> [1] 5
safe_divide(10, 2)
#> Just
#> [1] 5
divide(10, 0)
#> [1] Inf
safe_divide(10, 0)
#> Nothing
safe_divide(10, 2)
returns Just 5
and safe_divide(10, 0)
returns
Nothing
. These are the two possible values of the maybe type. It can
be Just
the value, or it can be Nothing
, the absence of a value. For
the value to be used as an input to another function you need to specify
what will happen if the function returns Nothing
.
This can be done using the with_default
function. This function will
return the value contained in the Just
, or if it is Nothing
it will
return the default. Think of a maybe value as a container. In this
container can be Just
the value or Nothing
. To use the contained
value in a regular R function you need to unwrap it first.
safe_divide(10, 2)
#> Just
#> [1] 5
safe_divide(10, 2) %>% with_default(0)
#> [1] 5
safe_divide(10, 0)
#> Nothing
safe_divide(10, 0) %>% with_default(0)
#> [1] 0
This may seem tedious to rewrite functions to return maybe values and then specify a default value each time. This is where the maybe chaining functions become useful.
maybe_map
allows a regular R function to be evaluated on a maybe
value. maybe_map
, often called fmap
in other languages, reaches into
the maybe value, applies a function to the value, then re-wraps the
result in a maybe. If the input is a Just
value, the return value of
maybe_map
will also be a Just
. If it is Nothing
the return value
will be Nothing
.
just(9) %>% maybe_map(sqrt)
#> Just
#> [1] 3
nothing() %>% maybe_map(sqrt)
#> Nothing
What if we wanted to chain multiple “safe” functions (functions that
return maybe values) together? The function and_then
, often called
bind
in other languages, works similarly to maybe_map
except the
function provided must return a maybe value.
safe_max <- function(a) {
if (length(a) == 0) nothing() else just(max(a))
}
safe_sqrt <- function(a) {
if (a < 0) nothing() else just(sqrt(a))
}
just(1:9) %>%
and_then(safe_max) %>%
and_then(safe_sqrt)
#> Just
#> [1] 3
nothing() %>%
and_then(safe_max) %>%
and_then(safe_sqrt)
#> Nothing
The maybe package provides another way to create functions that return
maybe values. Instead of rewriting the function to return maybe values
we can wrap it in the maybe
function. This will modify the function to
return Nothing
on an error or warning.
A predicate function (a function that returns TRUE
or FALSE
) can be
provided as an argument to assert something about the return value. If
the predicate returns TRUE
then a Just
value will be returned,
otherwise it will be Nothing
.
safe_max <- maybe(max)
safe_sqrt <- maybe(sqrt, ensure = not_infinite)
safe_max(1:9) %>% and_then(safe_sqrt)
#> Just
#> [1] 3
safe_max("hello") %>% and_then(safe_sqrt)
#> Nothing
This pattern of modifying a function with the maybe
function and then
setting a default value is so common that there is a shortcut,
perhaps
. The default value is set with the default
parameter. This
function will always return a regular R value, never maybe values.
perhaps_max <- perhaps(max, ensure = is.numeric, default = 0)
perhaps_max(1:9)
#> [1] 9
perhaps_max("hello")
#> [1] 0
Multiple predicates can be combined with the and
/or
functions.
safe_sqrt <- maybe(sqrt, ensure = and(not_nan, not_empty))
safe_sqrt(9)
#> Just
#> [1] 3
safe_sqrt(-1)
#> Nothing
Predefined combinations are also provided such as not_undefined
, which
ensures that the output is not any of NULL
, NA
, NaN
, -Inf
, or
Inf
.
safe_mean <- maybe(mean, ensure = not_undefined)
safe_mean(c(1, 2, 3))
#> Just
#> [1] 2
safe_mean(c(NA, 2, 3))
#> Nothing
The names of functions maybe_map
, and_then
, maybe_flatten
, and
with_default
are different from the traditional names used for these
functions in other functional programming languages. If you would like
to use the more traditional names aliases are provided.
fmap
==maybe_map
bind
==and_then
join
==maybe_flatten
from_maybe
==with_default