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https://github.com/armcn/maybe
The Maybe Monad in R
https://github.com/armcn/maybe
functional-programming r rstats
Last synced: 3 months ago
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The Maybe Monad in R
- Host: GitHub
- URL: https://github.com/armcn/maybe
- Owner: armcn
- License: other
- Created: 2022-01-16T23:27:32.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-10T00:01:38.000Z (about 1 year ago)
- Last Synced: 2024-07-21T05:24:59.876Z (4 months ago)
- Topics: functional-programming, r, rstats
- Language: R
- Homepage: https://armcn.github.io/maybe
- Size: 1.11 MB
- Stars: 49
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - armcn/maybe - The Maybe Monad in R (R)
README
---
output: github_document
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# maybe 
[](https://CRAN.R-project.org/package=maybe)
[](https://github.com/armcn/maybe/actions)
[](https://app.codecov.io/gh/armcn/maybe?branch=main)
[](https://cran.r-project.org/package=maybe)
[](https://github.com/armcn/maybe/actions/workflows/R-CMD-check.yaml)
# Overview
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.
## Installation
You can install the released version of maybe from [CRAN](https://CRAN.R-project.org) with:
```{r, eval=FALSE}
install.packages("maybe")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("armcn/maybe")
```
# Usage
The following example shows how the maybe package can be used to create
a safe data processing pipeline.
```{r}
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)
mean_mpg_of_cyl(100L)
```
Here is an example of working with data stored in JSON format.
```{r}
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]}')
sum_first_numbers('{}')
sum_first_numbers('1, 2, 3')
```
## The maybe type
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.
```{r}
divide <- function(a, b) {
a / b
}
safe_divide <- function(a, b) {
if (b == 0) nothing() else just(a / b)
}
divide(10, 2)
safe_divide(10, 2)
divide(10, 0)
safe_divide(10, 0)
```
`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.
```{r}
safe_divide(10, 2)
safe_divide(10, 2) %>% with_default(0)
safe_divide(10, 0)
safe_divide(10, 0) %>% with_default(0)
```
## Chaining maybe values
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`.
```{r}
just(9) %>% maybe_map(sqrt)
nothing() %>% maybe_map(sqrt)
```
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.
```{r}
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)
nothing() %>%
and_then(safe_max) %>%
and_then(safe_sqrt)
```
## Creating maybe functions
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`.
```{r}
safe_max <- maybe(max)
safe_sqrt <- maybe(sqrt, ensure = not_infinite)
safe_max(1:9) %>% and_then(safe_sqrt)
safe_max("hello") %>% and_then(safe_sqrt)
```
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.
```{r}
perhaps_max <- perhaps(max, ensure = is.numeric, default = 0)
perhaps_max(1:9)
perhaps_max("hello")
```
## Predicates
Multiple predicates can be combined with the `and`/`or` functions.
```{r}
safe_sqrt <- maybe(sqrt, ensure = and(not_nan, not_empty))
safe_sqrt(9)
safe_sqrt(-1)
```
Predefined combinations are also provided such as `not_undefined`, which ensures
that the output is not any of `NULL`, `NA`, `NaN`, `-Inf`, or `Inf`.
```{r}
safe_mean <- maybe(mean, ensure = not_undefined)
safe_mean(c(1, 2, 3))
safe_mean(c(NA, 2, 3))
```
## Function names
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`
## Inspiration / Prior work
- [monads R package](https://github.com/hadley/monads)
- [rmonad R package](https://github.com/arendsee/rmonad)
- [Maybe Monad in R blog post](https://www.r-bloggers.com/2019/05/maybe-monad-in-r/)
- [Elm Maybe package](https://package.elm-lang.org/packages/elm/core/1.0.5/Maybe)