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mod::ule

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The mod package is a lightweight module system; It provides a simple way to structure program and data into modules for programming and interactive use, without the formalities of R packages.

Why?

This is a good question.

As units for code organization in R, packages are very robust. However, they are formal and compliated; they require additional knowledge to R and must be installed in the local library. Scripts, as widely understood, are simplistic and brittle, unsuitable for building tools and may conflict with each other.

Situated between packages and scripts, modules feature characteristics that are somewhat similar to those from other languages. They can be defined either inline with other codes or in a standalone file, and can be used in the user’s working environment, packages, or other modules.

Let’s see.

Installation

Install the development version from GitHub with:

devtools::install_github("iqis/mod")

Use

The mod package is designed to be used either attached or unattached to your working environment.

If you wish to attach the package:

require(mod)
#> Loading required package: mod
#> 
#> Attaching package: 'mod'
#> The following object is masked from 'package:base':
#> 
#>     drop

Vocabulary

Examples

Define an inline module:

my <- module({
        a <- 1
        b <- 2
        f <- function(x, y) x + y
})

The resulting module contains the variables defined within.

ls(my)
#> [1] "a" "b" "f"

Subset the module.

my$a
#> [1] 1
my$b
#> [1] 2
my$f(my$a, my$b)
#> [1] 3

Use the with() to spare qualification.

with(my, 
     f(a,b))
#> [1] 3

Attach a Module to the Search Path

Just like a package, a module can be attached to the search path.

use(my)

The my module is attached to the search path as “module:my”, before other attached packages.

search()
#>  [1] ".GlobalEnv"        "module:my"         "package:mod"      
#>  [4] "package:stats"     "package:graphics"  "package:grDevices"
#>  [7] "package:utils"     "package:datasets"  "package:methods"  
#> [10] "Autoloads"         "package:base"

And you can use the variables inside directly, just like those from a package.

f(a,b)
#> [1] 3

Detach the module from the search path when done, if desired.

drop("my")

Make Variables Available to another Module

Use refer() to “import” variables from another module.

ls(my)
#> [1] "a" "b" "f"

my_other<- module({
        refer(my)
        
        c <- 4
        d <- 5
})

ls(my_other)
#> [1] "a" "b" "c" "d" "f"

Use a package

The mod::require() makes packages available for use in a module.

mpg_analysis <- module({
    require(ggplot2)
    plot <- qplot(mtcars$mpg)    
})
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
mpg_analysis$plot
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Meanwhile, your working environment’s search path remain unaffected:

search()
#>  [1] ".GlobalEnv"        "package:mod"       "package:stats"    
#>  [4] "package:graphics"  "package:grDevices" "package:utils"    
#>  [7] "package:datasets"  "package:methods"   "Autoloads"        
#> [10] "package:base"
"package:ggplot2" %in% search()
#> [1] FALSE

Private Variables

A variable is private if its name starts with ...

room_101 <- module({
        ..diary <- "Dear Diary: I used SPSS today..."
        get_diary <- function(){
                ..diary
        }
})

A private variable cannot be seen or touched. There is no way to access the ..diary from the outside, except by a function defined within the module. This can be useful if you want to shield some information from the user or other programs.

ls(room_101)
#> [1] "get_diary"
room_101$..diary
#> NULL
room_101$get_diary()
#> [1] "Dear Diary: I used SPSS today..."

Another way is using provide() function to declair public variables, while all others become private.

room_102 <- module({
        provide(open_info, get_classified)
        
        open_info <- "I am a data scientist."
        classified_info <- "I can't get the database driver to work."
        get_classified <- function(){
                classified_info
        }
})

ls(room_102)
#> [1] "get_classified" "open_info"
room_102$open_info
#> [1] "I am a data scientist."
room_102$classified_info
#> NULL
room_102$get_classified()
#> [1] "I can't get the database driver to work."

Simlate OOP

The below example simulates one essential behavior of an object in Object-oriented Programming by manipulating the state of ..count.

counter <- module({
        ..count <- 0
        add_one <- function(){
                #Its necessary to use `<<-` operator, as ..count lives in the parent frame.
                ..count <<- ..count + 1 
        }
        reset <- function(){
                ..count <<- 0
        }
        get_count <- function(){
                ..count
        }
})

A variable must be private to be mutable like ..count.

The following demonstration should be self-explanatory:

counter$get_count() 
#> [1] 0

counter$add_one() 
counter$add_one() 

counter$get_count() 
#> [1] 2

counter$reset()

counter$get_count()
#> [1] 0

It is imperative that mod be only adopted in the simplest cases. If full-featured OOP is desired, use R6.

Notes

Environment

A module is an environment. This means that every rule that applies to environments, such as copy-by-reference, applies to modules as well.

mode(my)
#> [1] "environment"
is.environment(my)
#> [1] TRUE

Terms

Some may wonder the choice of terms. Why refer() and provide()? Further, why not import() and export()? This is because we feel import() and export() are used too commonly, in both R, and other popular languages with varying meanings. The reticulate package also uses import(). To avoid confusion, we decided to introduce some synonyms. With analogous semantics, refer() is borrowed from Clojure, while provide() from Racket; Both languages are R’s close relatives.

Locked

If a module is locked, it is impossible to either change the value of a variable or add a new variable to a module. A private variable’s value can only be changed by a function defined within the module, as shown previously.

my$a <- 1
#> Error in my$a <- 1: cannot change value of locked binding for 'a'
my$new_var <- 1
#> Error in my$new_var <- 1: cannot add bindings to a locked environment

Hidden Variables

As a general R rule, names that start with . define hidden variables.

my_yet_another <- module({
        .var <- "I'm hidden!"
})

Hidden variables are not returned by ls(), unless specified.

ls(my_yet_another)
#> character(0)
ls(my_yet_another, all.names = TRUE)
#> [1] ".var"

Nonetheless, in a module, they are treated the same as public variables.

my_yet_another$.var
#> [1] "I'm hidden!"

Load/Attach from File

module_path <- system.file("misc/example_module.R", package = "mod")

To load and assign to variable:

example_module <- acquire(module_path)
ls(example_module)
#> [1] "a" "d" "e"
example_module$a
#> [1] 1
example_module$d()
#> [1] 6
example_module$e(100)
#> [1] 106

To load and attach to search path:

use(module_path)
ls("module:example_module")
#> [1] "a" "d" "e"
a
#> [1] 1
d()
#> [1] 6
e(100)
#> [1] 106

Modules and Packages

It could be confusing how modules and packages live together. To clarify:

Unattached

As aforementioned, the package is designed to be usable both attached and unattached.

If you use the package unattached, you must always qualify the variable name with ::, such as mod::ule(), a shorthand for mod::module(). However, while inside a module, the mod package is always available, so you do not need to use ::. Note that in the following example, provide() inside the module expression is unqualified.

See:

detach("package:mod")

my_mind <- mod::ule({
  provide(good_thought)
  good_thought <- "I love working on this package!"
  bad_thought <- "I worry that no one will appreciate it."
})

mod::use(my_mind)
good_thought
#> [1] "I love working on this package!"

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.