sicher

sicher (German for safe or certain) is an R package that brings runtime type safety to R programming, inspired by TypeScript for JavaScript. Declare types for your variables and have them enforced automatically on every assignment, catching type errors early and making your code more robust and self-documenting. 🛡️

sicher implements runtime type safety in R by attaching types to variables via active bindings rather than traditional attributes or classes. When a variable is declared with a type (e.g., x %:% Numeric %<-% 5), it is replaced by an active binding that wraps the value inside a closure with a private environment storing both the value and its associated type.

Every read and write to the variable is intercepted: reads return the stored value, while writes trigger validation through the type’s checker function before updating. This design provides strong guarantees that values cannot violate their declared types, enabling expressive and composable type definitions (including unions, structured lists, and data frame schemas).

📦 Installation

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("feddelegrand7/sicher")

🚀 Quick Start

library(sicher)

# Annotate a variable with a type, then assign a value
name %:% String  %<-% "Alice"
age  %:% Numeric %<-% 30

# The type is enforced on every subsequent assignment
age <- "thirty"   # Error: Type error
#> Error: Type error in 'age': Expected numeric, got string
#> Received: thirty

✨ Core Features

🧩 Built-in Types

sicher ships with a complete set of primitive and container types:

Type	Checks
Integer	is.integer()
Double	is.double()
Numeric	is.numeric()
String	is.character()
Bool	is.logical()
List	is.list()
DataFrame	is.data.frame()
Function	is.function()
Any	always passes
Null	is.null()

x    %:% Integer   %<-% 42L
y    %:% Double    %<-% 3.14
flag %:% Bool      %<-% TRUE
df   %:% DataFrame %<-% data.frame(a = 1:3)

⚙️ Operators

Operator	Purpose
%:%	Annotate a variable with a type
%<-%	Assign a value (type-checked)

🔧 Type Modifiers

1️⃣ Scalar() — single-element values

single %:% Scalar(Numeric) %<-% 42
single <- c(1, 2, 3)   # Error: length > 1
#> Error: Type error in 'single': Expected scalar<numeric>, got double of length 3
#> Received: [1, 2, 3]

🔒 Readonly() — immutable variables

PI %:% Readonly(Double) %<-% 3.14159
PI <- 3.0   # Error: cannot reassign readonly variable
#> Error: Cannot reassign readonly variable 'PI'. Remove Readonly() from the type declaration if mutation is needed.

❓ Optional() — nullable values

middle_name %:% Optional(String) %<-% NULL   # OK
middle_name <- "Marie"                        # Also OK
middle_name <- 123                            # Error: not string or null
#> Error: Type error in 'middle_name': Expected string | null, got double
#> Received: 123

🔀 Union Types

Accept more than one type with |:

id %:% (String | Numeric) %<-% "user123"
id <- 456   # Also OK
id <- TRUE  # Error: not string or numeric
#> Error: Type error in 'id': Expected string | numeric, got bool
#> Received: TRUE

🏷️ Enum Types

Restrict a value to an explicit set of allowed values with Enum():

status %:% Enum("draft", "published", "archived") %<-% "draft"
status <- "published"   # OK
status <- "deleted"     # Error: not one of the allowed enum values
#> Error: Type error in 'status': Expected enum["draft", "published", "archived"], got string
#> Received: deleted

priority %:% Enum(1, 2, 3) %<-% 2
priority <- 3            # OK
priority <- 5            # Error
#> Error: Type error in 'priority': Expected enum[1, 2, 3], got double
#> Received: 5

📏 Size-constrained Vectors

Append [n] to any type to require an exact vector length:

coords %:% Numeric[3] %<-% c(1, 2, 3)
coords <- c(4, 5, 6)   # OK — same length
coords <- c(1, 2)      # Error: wrong length
#> Error: Type error in 'coords': Expected numeric[3], got double of length 2
#> Received: [1, 2]

📋 Structured List Types

Define object-like schemas with create_list_type():

Person <- create_list_type(list(
  name  = String,
  age   = Numeric,
  email = Optional(String)   # nullable field
))

person %:% Person %<-% list(name = "Alice", age = 30, email = "alice@example.com")

person <- list(name = "Bob")   # Error: missing required field 'age'
#> Error: Type error: Expected {name: string, age: numeric, email?: string | null}, got list
#> Details: Missing required field(s): age (expected fields: name, age)
#> Received: list with fields: [name]

🗄️ Data Frame Schemas

Validate column names and types with create_dataframe_type():

UserTable <- create_dataframe_type(list(
  id       = Integer,
  username = String,
  active   = Bool
))

users %:% UserTable %<-% data.frame(
  id       = 1:2,
  username = c("alice", "bob"),
  active   = c(TRUE, FALSE)
)

# Wrong column type fails immediately
users <- data.frame(
  id       = c("1", "2"),   # Error: id must be integer
  username = c("alice", "bob"),
  active   = c(TRUE, FALSE)
)
#> Error: Type error in 'id': Expected integer, got string of length 2
#> Received: [1, 2]

📦 Homogeneous Lists with ListOf()

Validate every element of a list against the same type:

TodoItem <- create_list_type(list(
  id        = Numeric,
  title     = String,
  completed = Bool
))

TodoList <- ListOf(TodoItem)

todos %:% TodoList %<-% list(
  list(id = 1, title = "Buy milk",  completed = FALSE),
  list(id = 2, title = "Read book", completed = TRUE)
)

todos <- list(
  list(id = 1, title = "Buy milk", completed = FALSE),
  list(wrong = "shape")   # Error: element does not match TodoItem
)
#> Error: Type error in 'todos': Expected list<{id: numeric, title: string, completed: bool}>, got list of length 2
#> Received: list of length 2

🛠️ Custom Types

Use create_type() to define your own validator with any predicate function:

Positive <- create_type("positive", function(x) is.numeric(x) && all(x > 0))

value %:% Positive %<-% 5
value <- -1   # Error
#> Error: Type error in 'value': Expected positive, got double
#> Received: -1

Use Enum() when the allowed set is fixed and finite. Reach for create_type() when the rule is more general than membership in a predefined list.

🔤 Typed Functions

Use typed_function() to wrap any function with runtime type checks on its parameters and, optionally, its return value — a typed function signature for R:

# Basic typed function — checks params and return type
add <- typed_function(
  function(x, y) x + y,
  params  = list(x = Numeric, y = Numeric),
  .return = Numeric
)

add(1, 2)     # Returns 3
#> [1] 3
add("a", 2)   # Error: Type error in 'x'
#> Error: Type error in 'x': Expected numeric, got string
#> Received: a

Num_not_inf <- create_type(
  name = "Num_not_inf", 
  checker = function(x) {
    is.numeric(x) && !is.infinite(x)
  }
)


divide <- function(a, b) {
  return(a / b)
}

divide_safe <- typed_function(
  fn = divide, 
  params = list(
    a = Num_not_inf, 
    b = Num_not_inf
  ), 
  .return = Num_not_inf
)


divide_safe(10, 2) # works normally
#> [1] 5
divide_safe(10, 0) # fails as 10/0 returns Inf
#> Error: Type error in '<return value>': Expected Num_not_inf, got double
#> Received: Inf

# Optional parameter
greet <- typed_function(
  function(name, title = NULL) {
    if (is.null(title)) paste("Hello,", name)
    else paste("Hello,", title, name)
  },
  params = list(name = String, title = Optional(String))
)

greet("Alice")                   # "Hello, Alice"
#> [1] "Hello, Alice"
greet("Alice", title = "Dr.")    # "Hello, Dr. Alice"
#> [1] "Hello, Dr. Alice"
greet("Alice", title = 42)       # Error: Type error in 'title'
#> Error: Type error in 'title': Expected string | null, got double
#> Received: 42

# Union type in params
describe <- typed_function(
  function(id) paste("ID:", id),
  params  = list(id = String | Numeric),
  .return = String
)

describe("abc")   # "ID: abc"
#> [1] "ID: abc"
describe(123)     # "ID: 123"
#> [1] "ID: 123"
describe(TRUE)    # Error: Type error in 'id'
#> Error: Type error in 'id': Expected string | numeric, got bool
#> Received: TRUE

You can also define an object to expect as a return value:

Person <- create_list_type(
  type_spec = list(
    name = String, 
    age = Numeric
  )
)

get_person_info_as_list <- function(name, age) {
  return(list(
    name = name, 
    age = age
  ))
}

get_person_info_as_message <- function(name, age) {
  return(
    paste("Hi my name is ", name, " I'm ", age, " years old")
  )
}

get_person_info_as_list_safe <- typed_function(
  fn = get_person_info_as_list, 
  params = list(name = String, age = Numeric), 
  .return = Person
)

get_person_info_as_list_safe(name = "Omar", age = 30) # works fine
#> $name
#> [1] "Omar"
#> 
#> $age
#> [1] 30

get_person_info_as_message_safe <- typed_function(
  fn = get_person_info_as_message, 
  params = list(name = String, age = Numeric), 
  .return = Person
)

# Should fail as the function does not return a Person list anymore
get_person_info_as_message_safe(name = "Omar", age = 30) 
#> Error: Type error in '<return value>': Expected {name: string, age: numeric}, got string
#> Received: Hi my name is  Omar  I'm  30  years old

🌍 Function usage

# Catch bad payroll data early instead of getting silent NAs
calculate_mean_payroll <- function(salaries) {
  salaries %:% Numeric %<-% salaries
  mean(salaries)
}

calculate_mean_payroll(c(1800, 2300, 4000))   # Works fine
#> [1] 2700

calculate_mean_payroll(c(1800, "2300", 4000)) # Error: type mismatch
#> Error: Type error in 'salaries': Expected numeric, got string of length 3
#> Received: [1800, 2300, 4000]

🔍 Type inference

You can automatically infer the type for an R object using infer_type():

# Primitives
infer_type(42L)                # Integer
#> <type: integer >
infer_type(3.14)               # Double
#> <type: double >
infer_type("abc")              # String
#> <type: string >
infer_type(TRUE)               # Bool
#> <type: bool >
infer_type(NULL)               # Null
#> <type: null >
infer_type(function(x) x + 1)  # Function
#> <type: Function >

# Default mode infers types, not observed lengths
infer_type(c(1L, 2L, 3L))      # Integer
#> <type: integer >
infer_type(c(1, 2, 3))         # Double
#> <type: double >
infer_type(c("a", "b"))        # String
#> <type: string >
infer_type(c(TRUE, FALSE))     # Bool
#> <type: bool >

# Named and unnamed lists
infer_type(list(a = 1L, b = "x")) # create_list_type(list(a = Integer, b = String))
#> <type: {a: integer, b: string} >
infer_type(list(1L, 2L, 3L))   # ListOf(Integer)
#> <type: list<integer> >
infer_type(list(1L, "a"))      # List
#> <type: list >
infer_type(list(a = NULL, b = 1)) # create_list_type(list(a = Optional(Any), b = Double))
#> <type: {a?: any | null, b: double} >

# Data frames
infer_type(data.frame(x = 1:3, y = c("a", "b", "c"), stringsAsFactors = FALSE))
#> <type: data.frame{x: integer, y: string} >
# create_dataframe_type(list(x = Integer, y = String))

# Use strict = TRUE to also infer Scalar() and [n] size constraints
infer_type(42L, strict = TRUE)           # Scalar(Integer)
#> <type: scalar<integer> >
infer_type(c("a", "b"), strict = TRUE) # String[2]
#> <type: string[2] >
infer_type(data.frame(x = 1:3), strict = TRUE)
#> <type: data.frame{x: integer[3]} >

Global Options

The behavior of sicher can be controlled globally using R’s options() mechanism. These options allow you to switch between strict type enforcement and a fully disabled mode depending on your use case.

sicher.mode

Controls how typed assignments (%:% and %<-%) behave.

Values

• "on" (default)
  Enables full runtime type enforcement.
  • Typed variables are implemented as active bindings
  • All assignments are validated against their declared type
  • Type violations result in immediate errors
• "off"
  Disables the type system entirely.
  • Typed annotations are ignored
  • Assignments behave like standard R assignments (<-)
  • No validation is performed
  • No active bindings are created

How to set options

You can configure the mode globally, for example, disabling strict type checking with:

options(sicher.mode = "off")

Or enable strict typing with:

options(sicher.mode = "on") # this is the default

When to use each mode

• "on" (default) (strict mode) Use in situations where correctness and safety are important:
  • Package development
  • Data validation pipelines
  • Production systems where type guarantees are desired
  • APIs or functions expecting structured inputs In this mode, sicher actively enforces types at runtime and prevents invalid assignments.
• "off" (disabled mode) Use when you want to bypass the type system entirely:
  • Performance-sensitive code where validation overhead is not desired
  • Debugging or testing environments where strict typing is temporarily unnecessary
  • Running code in environments where active bindings may interfere with other tools or workflows
  • Interoperability with code that assumes standard R assignment semantics In this mode, typed annotations are effectively ignored, and variables behave like regular R objects.

Example

my_var_x %:% Numeric %<-% 10   # validated and bound with type enforcement

my_var_x <- "string" # trigger an error
#> Error: Type error in 'my_var_x': Expected numeric, got string
#> Received: string

options(sicher.mode = "off")
my_var_y %:% Numeric %<-% 10 
my_var_y <- "string" # nothing happens

It can also be temporarily scoped:

withr::with_options(
  list(sicher.mode = "off"),
  {
    my_var_y %:% Numeric %<-% 10
    my_var_y <- "string"
  }
)

📚 Learn More

Full documentation and worked examples are available at the package website.

Code of Conduct

Please note that the sicher project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.