rlang-patterns

用于数据屏蔽的现代 rlang 模式

为 tidyverse 数据屏蔽提供支持的元编程框架

核心概念

数据屏蔽允许 R 表达式像引用环境中的变量一样引用数据框列。rlang 提供了为 tidyverse 数据屏蔽提供支持的元编程框架。

主要 rlang 工具

• 拥抱 {{}} - 将函数参数转发给数据屏蔽函数
• 注入 !! - 注入单个表达式或值
• 拼接 !!! - 从列表中注入多个参数
• 动态点号 - 支持注入的可编程 ...
• 代词 .data/.env - 明确区分数据变量和环境变量

函数参数模式

使用 {{}} 转发

使用 {{}} 将函数参数转发给数据屏蔽函数：

# Single argument forwarding
my_summarise <- function(data, var) {
  data |> dplyr::summarise(mean = mean({{ var }}))
}

# Works with any data-masking expression
mtcars |> my_summarise(cyl)
mtcars |> my_summarise(cyl * am)
mtcars |> my_summarise(.data$cyl)  # pronoun syntax supported

转发 ...（无需特殊语法）

# Simple dots forwarding
my_group_by <- function(.data, ...) {
  .data |> dplyr::group_by(...)
}

# Works with tidy selections too
my_select <- function(.data, ...) {
  .data |> dplyr::select(...)
}

# For single-argument tidy selections, wrap in c()
my_pivot_longer <- function(.data, ...) {
  .data |> tidyr::pivot_longer(c(...))
}

使用 .data 的命名模式

使用 .data 代词以编程方式访问列：

# Single column by name
my_mean <- function(data, var) {
  data |> dplyr::summarise(mean = mean(.data[[var]]))
}

# Usage - completely insulated from data-masking
mtcars |> my_mean("cyl")  # No ambiguity, works like regular function

# Multiple columns with all_of()
my_select_vars <- function(data, vars) {
  data |> dplyr::select(all_of(vars))
}

mtcars |> my_select_vars(c("cyl", "am"))

注入运算符

各运算符的适用场景

运算符	使用场景	示例
{{ }}	转发函数参数	summarise(mean = mean({{ var }}))
!!	注入单个表达式/值	summarise(mean = mean(!!sym(var)))
!!!	注入多个参数	group_by(!!!syms(vars))
.data[[]]	按名称访问列	mean(.data[[var]])

使用 !! 进行高级注入

# Create symbols from strings
var <- "cyl"
mtcars |> dplyr::summarise(mean = mean(!!sym(var)))

# Inject values to avoid name collisions
df <- data.frame(x = 1:3)
x <- 100
df |> dplyr::mutate(scaled = x / !!x)  # Uses both data and env x

# Use data_sym() for tidyeval contexts (more robust)
mtcars |> dplyr::summarise(mean = mean(!!data_sym(var)))

使用 !!! 进行拼接

# Multiple symbols from character vector
vars <- c("cyl", "am")
mtcars |> dplyr::group_by(!!!syms(vars))

# Or use data_syms() for tidy contexts
mtcars |> dplyr::group_by(!!!data_syms(vars))

# Splice lists of arguments
args <- list(na.rm = TRUE, trim = 0.1)
mtcars |> dplyr::summarise(mean = mean(cyl, !!!args))

动态点号模式

使用 list2() 支持动态点号

my_function <- function(...) {
  # Collect with list2() instead of list() for dynamic features
  dots <- list2(...)
  # Process dots...
}

# Enables these features:
my_function(a = 1, b = 2)           # Normal usage
my_function(!!!list(a = 1, b = 2))  # Splice a list
my_function("{name}" := value)      # Name injection
my_function(a = 1, )               # Trailing commas OK

使用 Glue 语法注入名称

# Basic name injection
name <- "result"
list2("{name}" := 1)  # Creates list(result = 1)

# In function arguments with {{
my_mean <- function(data, var) {
  data |> dplyr::summarise("mean_{{ var }}" := mean({{ var }}))
}

mtcars |> my_mean(cyl)        # Creates column "mean_cyl"
mtcars |> my_mean(cyl * am)   # Creates column "mean_cyl * am"

# Allow custom names with englue()
my_mean <- function(data, var, name = englue("mean_{{ var }}")) {
  data |> dplyr::summarise("{name}" := mean({{ var }}))
}

# User can override default
mtcars |> my_mean(cyl, name = "cylinder_mean")

用于消歧的代词

.data 和 .env 的最佳实践

# Explicit disambiguation prevents masking issues
cyl <- 1000  # Environment variable

mtcars |> dplyr::summarise(
  data_cyl = mean(.data$cyl),    # Data frame column
  env_cyl = mean(.env$cyl),      # Environment variable
  ambiguous = mean(cyl)          # Could be either (usually data wins)
)

# Use in loops and programmatic contexts
vars <- c("cyl", "am")
for (var in vars) {
  result <- mtcars |> dplyr::summarise(mean = mean(.data[[var]]))
  print(result)
}

编程模式

桥接模式

在数据屏蔽行为和整洁选择行为之间转换：

# across() as selection-to-data-mask bridge
my_group_by <- function(data, vars) {
  data |> dplyr::group_by(across({{ vars }}))
}

# Works with tidy selection
mtcars |> my_group_by(starts_with("c"))

# across(all_of()) as names-to-data-mask bridge
my_group_by <- function(data, vars) {
  data |> dplyr::group_by(across(all_of(vars)))
}

mtcars |> my_group_by(c("cyl", "am"))

转换模式

# Transform single arguments by wrapping
my_mean <- function(data, var) {
  data |> dplyr::summarise(mean = mean({{ var }}, na.rm = TRUE))
}

# Transform dots with across()
my_means <- function(data, ...) {
  data |> dplyr::summarise(across(c(...), ~ mean(.x, na.rm = TRUE)))
}

# Manual transformation (advanced)
my_means_manual <- function(.data, ...) {
  vars <- enquos(..., .named = TRUE)
  vars <- purrr::map(vars, ~ expr(mean(!!.x, na.rm = TRUE)))
  .data |> dplyr::summarise(!!!vars)
}

应避免的易错模式

不要使用以下已弃用/危险模式

# Avoid - String parsing and eval (security risk)
var <- "cyl"
code <- paste("mean(", var, ")")
eval(parse(text = code))  # Dangerous!

# Good - Symbol creation and injection
!!sym(var)  # Safe symbol injection

# Avoid - get() in data mask (name collisions)
with(mtcars, mean(get(var)))  # Collision-prone

# Good - Explicit injection or .data
with(mtcars, mean(!!sym(var)))  # Safe
# or
mtcars |> summarise(mean(.data[[var]]))  # Even safer

常见错误

# Don't use {{ }} on non-arguments
my_func <- function(x) {
  x <- force(x)  # x is now a value, not an argument
  quo(mean({{ x }}))  # Wrong! Captures value, not expression
}

# Don't mix injection styles unnecessarily
# Pick one approach and stick with it:
# Either: embrace pattern
my_func <- function(data, var) data |> summarise(mean = mean({{ var }}))
# Or: defuse-and-inject pattern
my_func <- function(data, var) {
  var <- enquo(var)
  data |> summarise(mean = mean(!!var))
}

使用 rlang 开发程序包

导入策略

# In DESCRIPTION:
Imports: rlang

# In NAMESPACE, import specific functions:
importFrom(rlang, enquo, enquos, expr, !!!, :=)

# Or import key functions:
#' @importFrom rlang := enquo enquos

文档标签

#' @param var <[`data-masked`][dplyr::dplyr_data_masking]> Column to summarize
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Additional grouping variables
#' @param cols <[`tidy-select`][dplyr::dplyr_tidy_select]> Columns to select

测试 rlang 函数

# Test data-masking behavior
test_that("function supports data masking", {
  result <- my_function(mtcars, cyl)
  expect_equal(names(result), "mean_cyl")

  # Test with expressions
  result2 <- my_function(mtcars, cyl * 2)
  expect_true("mean_cyl * 2" %in% names(result2))
})

# Test injection behavior
test_that("function supports injection", {
  var <- "cyl"
  result <- my_function(mtcars, !!sym(var))
  expect_true(nrow(result) > 0)
})

这种现代 rlang 方法能够实现简洁、安全的元编程，同时保留用户对 tidyverse 函数所期待的直观数据屏蔽体验。