如何获取在指定环境中评估调用对象时使用的所有参数的值 函数定义示例

问题描述

说我有一个函数 f as

f = function(x = 1,y,z,t) { x + y + z}

和一个列表 l 这样的

l = list(Y = 2,t = "test")

我可以像f一样评估l

eval(quote(f(y = Y,z = 3)),envir = l)
6

我的问题是我想获取最终被函数 f 使用的参数的所有值，即。一个函数 magic，它将接受一个调用对象和一个环境，并返回将用于评估表达式的所有参数的值。

例如：

call_obj = quote(f(y = Y,z = 3))
magic(call_obj,envir = l)
# I get a named list which value is list(1,2,3,"test")
# For that matter I do not even need the default arguments values (x)

编辑：为 base-r 答案添加赏金（虽然@Artem Sokolov 提供了一个 purrr-rlang，但提取几个相关函数仍然可以）

解决方法

这个怎么样：

magic <- function(call_obj,envir) {
  
  call_fun <- as.list(as.call(call_obj))[[1]]
  
  call_obj <- match.call(match.fun(call_fun),as.call(call_obj))

  
  ## arguments supplied in call
  call_args <- as.list(call_obj)[-1]
  
  
  ## arguments from function definition
  fun_args <- formals(match.fun(call_fun))
  
  ## match arguments from call with list
  new_vals_call <- lapply(call_args,function(x) eval(x,envir = envir))
  
  ## match arguments from function definition with list
  ## because everything (including NULL) can be a valid function argument we cannot directly use mget()
  
  in_list <- sapply(names(fun_args),function(x,env) exists(x,envir = env),as.environment(envir))
  new_vals_formals <- mget(names(fun_args),envir = as.environment(envir),ifnotfound = "")[in_list]
  
  
  ## values in the call take precedence over values from the list (can easily be reversed if needed)
  new_vals_complete <- modifyList(fun_args,new_vals_formals,keep.null = TRUE)
  new_vals_complete <- modifyList(new_vals_complete,new_vals_call,keep.null = TRUE)
  
  ## Construct a call object (if you want only the list of arguments return new_vals_complete)
  as.call(c(call_fun,new_vals_complete))
}


# -------------------------------------------------------------------------


f <- function(x = 1,y,z,t) { x + y + z}


## Tests

## basic test
magic(quote(f(y = Y,z = 3)),list(Y = 2,t = "test"))
#> f(x = 1,y = 2,z = 3,t = "test")    

## precedence (t defined twice)
magic(quote(f(y = Y,t=99)),t = 99)

## missing values (z is missing)
magic(quote(f(y = Y)),z =,t = "test")

## NULL values in call
magic(quote(f(y = Y,z = NULL)),z = NULL,t = "test")

## NULL values in list
magic(quote(f(y = Y,t = NULL))
#> f(x = 1,t = NULL)

## NULL values and precendece
magic(quote(f(y = Y,t= NULL)),t = NULL)
magic(quote(f(y = Y,t = 99)

## call with subcalls
magic(quote(f(y = sin(pi),y = 1.22460635382238e-16,t = "test")
magic(quote(f(y = Y,list(Y = sin(pi),t = "test")

## call with additional vars (g is not an argument of f)  -> error: unused arguments
magic(quote(f(g = Y,t = "test"))

## list with with additional vars (g is not an argument of f) -> vars are ignored 
magic(quote(f(y = Y,t = "test",g=99))
#> f(x = 1,t = "test")

## unnamed arguments
magic(quote(f(99,y = Y,t = "test"))
#> f(x = 99,t = "test")
magic(quote(f(99,77)),z = 77,t = "test")

,

tidyverse 解决方案

# Identify the variables in l that can be used to specify arguments of f
args1 <- l[ intersect( names(formals(f)),names(l) ) ]

# Augment the call with these variables
call_obj2 <- rlang::call_modify( call_obj,!!!args1 )
# f(y = Y,t = "test")

# Evaluate the arguments of the call in the context of l and combine with defaults
purrr::list_modify( formals(f),!!!purrr::map(rlang::call_args(call_obj2),eval,l) )

基础 R 解决方案

# As above
args1 <- l[ intersect( names(formals(f)),names(l) ) ]

# Augment the call with variables in args1
l1 <- modifyList( as.list(call_obj),args1 )[-1]

# Evaluate the arguments in the context of l and combine with defaults
modifyList(formals(f),lapply(l1,l))

两种解决方案的

输出

# $x
# [1] 1
#
# $y
# [1] 2
#
# $z
# [1] 3
#
# $t
# [1] "test"

,

Strictly Base R... 还支持 call_obj 中的未命名参数。

函数定义

magic <- function(call_obj,envir) {
  #browser()
  
  # Get all formal args
  Formals <- formals(as.character(call_obj))
  
  # fix names of call_obj to allow unnamed args
  unnamed <- which(names(call_obj)[-1] == "")

  # ignore extra arguments names if too many args (issue a warning?)
  unnamed <- unnamed[unnamed <= length(Formals)] 

  # check for names conflicts
  named <- which(names(call_obj)[-1] != "")

  if (any(unnamed > named))
    stop("Unnamed arguments cannot follow named arguments in call_obj")

  if (any(names(Formals)[unnamed] %in% names(call_obj)))
    stop("argument names conflicting in call_obj; ","avoid unnamed arguments if possible")
      
  names(call_obj)[unnamed + 1] <- names(Formals)[unnamed]
  
  # Replace defaults by call_obj values
  for (nn in intersect(names(call_obj),names(Formals))) {
    Formals[nn] <- call_obj[nn]
  }
  
  # Check for other values in envir
  for (mm in names(which(sapply(Formals,class) == "name"))) {
    if (mm %in% names(envir))
      Formals[mm] <- envir[mm]
    else if (Formals[mm] %in% names(envir))
      Formals[mm] <- envir[which(names(envir) == Formals[[mm]])]
  } 
  
  print(as.call(c(as.list(as.call(call_obj))[[1]],Formals)))
  return(invisible(Formals))
} 

示例

f = function(x = 1,t) { x + y + z}
l = list(Y = 2,t = "test")
call_obj = quote(f(y = Y,z = 3))

magic(call_obj,envir = l)

结果（打印）

f(x = 1,t = "test")

返回的对象（不可见，用于赋值）

$x
[1] 1
 
$y
[1] 2
 
$z
[1] 3

$t
[1] "test"

虽然我们通过不同的方式到达那里，但 AEF 的所有测试结果都与我的一致。