metaprogramming

R Metaprogramming with rlang

Metaprogramming is the ability to defuse, create, and inject R expressions. The core pattern is defuse-and-inject: capture code as data, optionally transform it, then inject it into another context for evaluation.

Quick Reference

Task	Function/Operator
Defuse your own expression	expr(x + 1)
Defuse user's single argument	enquo(arg)
Defuse user's ... arguments	enquos(...)
Inject single expression	!! or {{
Splice list of expressions	!!!
Get expression from quosure	quo_get_expr(q)
Get environment from quosure	quo_get_env(q)
Build symbol from string	sym("name")
Build symbol with .data pronoun	data_sym("name")
Build symbols from vector	syms(names) / data_syms(names)
Auto-label expression	as_label(quo)
Format argument as string	englue("{{ x }}")
Interpolate name in dynamic dots	"{name}" := value
Interpolate argument in name	"{{ arg }}" := value

Defusing Expressions

Defusing stops evaluation and returns the expression as a tree-like object (a "blueprint" for computation).

# Normal evaluation returns result
1 + 1
#> [1] 2

# Defusing returns the expression
expr(1 + 1)
#> 1 + 1

expr() vs enquo()

Function	Defuses	Returns	Use When
expr()	Your own code	Expression	Building expressions locally
enquo()	User's argument	Quosure	Forwarding function arguments
enquos()	User's ...	List of quosures	Forwarding multiple arguments

# Defuse your own expression
my_expr <- expr(mean(x, na.rm = TRUE))

# Defuse user's function argument (returns quosure)
my_function <- function(var) {

  enquo(var)
}
my_function(cyl + am)
#> <quosure>
#> expr: ^cyl + am
#> env:  global

enquos() with .named

Auto-label unnamed arguments:

g <- function(...) {

  vars <- enquos(..., .named = TRUE)
  names(vars)
}
g(cyl, 1 + 1)
#> [1] "cyl"   "1 + 1"

g(foo = cyl, bar = 1 + 1)
#> [1] "foo" "bar"

Types of Defused Expressions

• Calls: f(x, y), 1 + 1 - function invocations
• Symbols: x, df - named object references
• Constants: 1, "text", NULL - literal values

Quosures

A quosure wraps an expression with its original environment. This is critical for correct evaluation when expressions travel across function and package boundaries.

Why Environments Matter

# In package A
my_function <- function(data, var) {
  # 'var' was defined in the user's environment
  # The quosure tracks that environment
  var <- enquo(var)

  # When passed to package B's function, the quosure
  # ensures symbols resolve in the correct environment
  pkg_b_function(data, !!var)
}

Without environment tracking, symbols might resolve to wrong objects when code crosses package boundaries.

When You Need Quosures

Situation	Use Quosure?
Defusing function arguments	Yes - use enquo()
Building local expressions	No - use expr()
Cross-package composition	Yes - environments matter
Simple local evaluation	No - expr() + eval() suffices

Quosure Operations

q <- enquo(x + 1)

quo_get_expr(q)   # Extract expression: x + 1
quo_get_env(q)    # Extract environment

# Create quosure manually
new_quosure(expr(x + 1), env = global_env())

# Convert expression to quosure
as_quosure(expr(x + 1), env = global_env())

Injection Operators

Injection inserts defused expressions back into code before evaluation.

{{ (Embrace)

Defuses and injects in one step. Equivalent to !!enquo(arg):

# These are equivalent:
my_summarise <- function(data, var) {

  data |> dplyr::summarise({{ var }})
}

my_summarise <- function(data, var) {
  data |> dplyr::summarise(!!enquo(var))
}

Use {{ when you simply need to forward an argument. Use enquo() + !! when you need to inspect or transform the expression first.

!! (Bang-Bang)

Injects a single expression:

var <- expr(cyl)
mtcars |> dplyr::summarise(mean(!!var))
#> Equivalent to: summarise(mean(cyl))

# Inject a value to avoid name collisions
x <- 100
df |> dplyr::mutate(x = x / !!x)
#> Uses column x divided by env value 100

!!! (Splice)

Injects each element of a list as separate arguments:

vars <- exprs(cyl, am, vs)
mtcars |> dplyr::select(!!!vars)
#> Equivalent to: select(cyl, am, vs)

# With enquos()
my_group_by <- function(.data, ...) {
  .data |> dplyr::group_by(!!!enquos(...))
}

Where Operators Work

• Data-masked arguments: Implicitly enabled (dplyr, ggplot2, etc.)
• inject(): Explicitly enables operators in any context
• Dynamic dots: !!! and {name} work in functions using list2()

# Enable injection in base functions
inject(
  with(mtcars, mean(!!sym("cyl")))
)

Building Expressions from Data

sym() and syms()

Convert strings to symbols:

var <- "cyl"
sym(var)
#> cyl

vars <- c("cyl", "am")
syms(vars)
#> [[1]]
#> cyl
#> [[2]]
#> am

data_sym() and data_syms()

Create .data$col expressions (safer in tidy eval, avoids collisions):

data_sym("cyl")
#> .data$cyl

data_syms(c("cyl", "am"))
#> [[1]]
#> .data$cyl
#> [[2]]
#> .data$am

Use sym() for base R functions; use data_sym() for tidy eval functions.

Building Calls

# With call()
call("mean", sym("x"), na.rm = TRUE)
#> mean(x, na.rm = TRUE)

# With expr() and injection
var <- sym("x")
expr(mean(!!var, na.rm = TRUE))
#> mean(x, na.rm = TRUE)

Name Interpolation (Glue Operators)

In dynamic dots, use glue syntax for names.

{ for Variable Values

name <- "foo"
tibble::tibble("{name}" := 1:3)
#> # A tibble: 3 x 1
#>     foo
#>   <int>
#> 1     1
#> 2     2
#> 3     3

tibble::tibble("prefix_{name}" := 1:3)
#> Column named: prefix_foo

{{ for Argument Labels

my_mutate <- function(data, var) {
  data |> dplyr::mutate("mean_{{ var }}" := mean({{ var }}))
}
mtcars |> my_mutate(cyl)
#> Creates column: mean_cyl

englue() for String Formatting

my_function <- function(var) {
  englue("Column: {{ var }}")
}
my_function(some_column)
#> [1] "Column: some_column"

Advanced: Manual Expression Transformation

When you need to modify expressions before injection:

my_mean <- function(data, var) {
  # 1. Defuse

  var <- enquo(var)

  # 2. Transform: wrap in mean()
  wrapped <- expr(mean(!!var, na.rm = TRUE))

  # 3. Inject
  data |> dplyr::summarise(mean = !!wrapped)
}

For multiple arguments:

my_mean <- function(.data, ...) {
  vars <- enquos(..., .named = TRUE)

  # Transform each expression
  vars <- purrr::map(vars, ~ expr(mean(!!.x, na.rm = TRUE)))

  .data |> dplyr::summarise(!!!vars)
}

Base R Equivalents

rlang	Base R	Notes
expr()	bquote()	bquote uses .() for injection
enquo()	substitute()	substitute returns naked expr, not quosure
enquos(...)	eval(substitute(alist(...)))	Workaround for dots
!!	.() in bquote	Only inside bquote
eval_tidy()	eval()	eval_tidy supports .data/.env pronouns

Pitfalls

{{ on Non-Arguments

{{ should only wrap function arguments. On regular objects, it captures the value, not the expression:

# Correct: var is a function argument
my_fn <- function(var) {{ var }}

# Problematic: x is not an argument
x <- 1
{{ x }}  # Returns 1, not the expression

Operators Out of Context

Outside tidy eval/inject contexts, operators have different meanings:

Operator	Intended	Outside Context
{{	Embrace	Double braces (returns value)
!!	Inject	Double negation (logical)
!!!	Splice	Triple negation (logical)

These fail silently. See the tidy-evaluation skill for details on proper usage contexts.

See Also

• tidy-evaluation: Programming patterns for data-masked functions
• designing-tidy-r-functions: Function API design principles
• rlang-conditions: Error handling with rlang

Reference Files

• topic-quosure.md - Complete quosure reference
• topic-metaprogramming.md - Advanced transformation patterns
• topic-multiple-columns.md - Multiple columns patterns

Vignettes

Access detailed rlang documentation via R:

# Defusing expressions
vignette("topic-defuse", package = "rlang")

# Injection operators
vignette("topic-inject", package = "rlang")

# Or browse all vignettes
browseVignettes("rlang")