分配给LHS上的空索引（空方括号x []&lt ;-) [英] Assignment to empty index (empty square brackets x[]<-) on LHS

问题描述

在查看最近在SO上发布的答案时，我注意到一个陌生的作业陈述。它使用 myVar []<-myValue 的形式，而不是 myVar <-m​​yValue 的常规形式，即左侧的对象用空方括号索引。就我个人而言，我从未见过这样的分配，但是它产生了非常有用的效果-将分配的数据 myValue重塑为 myVar的形状。

While looking at an answer posted recently on SO, I noticed an unfamiliar assignment statement. Instead of the usual form of myVar<- myValue, it used the form myVar[]<- myValue, i.e. the object on lefthand side is indexed with empty square brackets. Personally, I had never seen such an assignment, but it had a highly useful effect-- it reshaped the assigned data 'myValue' to the shape of 'myVar'.

我想在我的代码中使用它，因为这使事情变得容易得多。但是<- 的文档似乎对此保持沉默。

I would like to use this in my code as this makes things lot easier. However the documentation for "<-" seems to be silent on it.

这是一项完善的功能，在任何情况下都可以依靠它来工作吗？

Is this a well established feature and one can rely on it to work in all cases?

我的猜测是这可能是函数调用堆栈的副作用，即在其中调用<-和 [顺序，但我不知道如何。有人可以对此有所启示吗？

Also, my guess is that it might be a side effect of a function call stack, i.e. calling <- and [ in sequence, but I could not figure out how. Can someone throw some light on that?

这里是一个例子-

# A dataframe
df1 <- data.frame(a = 1:4, b = 11:14)

# simple assignment assigns to class of RHS
df1 <- c(21:24, 31:34)
df1 
#[1] 21 22 23 24 31 32 33 34
class(df1)
#[1] "integer"

#assignment with [] casts to class of LHS 
df1<- data.frame(a = 1:4, b = 11:14)
df1[]<- c(21:24,31:34)
df1

#    a  b
# 1 21 31
# 2 22 32
# 3 23 33
# 4 24 34


# recycling to preserve shape
df1[]<- c(101:102)
df1

#     a   b
# 1 101 101
# 2 102 102
# 3 101 101
# 4 102 102

class(df1)
#data.frame

# reshaping 

df1<- data.frame(a = 1:4, b = 11:14)
df1[] <- matrix(1:8, 2,4)
df1 #matrix reshaped 
class(df1)
#[1] "data.frame"

# flattening 
x<- 1:8
x[] <- matrix(1:8,4,2)
x
#[1] 1 2 3 4 5 6 7 8

推荐答案

这是有意记录的功能。如joran所述，文档页面提取在原子向量部分中包括以下内容：

This is an intentional and documented feature. As joran mentioned, the documentation page "Extract" includes this in the "Atomic Vectors" section:

空索引会选择所有值：

An empty index selects all values: this is most often used to replace all the entries but keep the attributes.

但是，对于递归对象（ data.frames 或 lists ），则仅为子集对象保留属性。

However, in the case of recursive objects (data.frames or lists, for example), the attributes are only kept for the subsetted object. Its parts don't get such protection.

这里是一个例子：

animals <- factor(c('cat', 'dog', 'fish'))
df_factor <- data.frame(x = animals)
rownames(df_factor) <- c('meow', 'bark', 'blub')
str(df_factor)
# 'data.frame': 3 obs. of  1 variable:
#   $ x: Factor w/ 3 levels "cat","dog","fish": 1 2 3

df_factor[] <- 'cat'
str(df_factor)
# 'data.frame': 3 obs. of  1 variable:
#   $ x: chr  "cat" "cat" "cat"
rownames(df_factor)
# [1] "meow" "bark" "blub"

df_factor 保留其行名属性，但 x 列仅是赋值中使用的字符向量，而不是因子。我们可以通过专门替换其值来保持 x 的类和级别：

df_factor kept its rownames attribute, but the x column is just the character vector used in the assignment instead of a factor. We can keep the class and levels of x by specifically replacing its values:

df_factor <- data.frame(x = animals)
df_factor$x[] <- 'cat'
str(df_factor)
# 'data.frame': 3 obs. of  1 variable:
#   $ x: Factor w/ 3 levels "cat","dog","fish": 1 1 1

因此，对于向量，矩阵和数组，用空子集替换非常安全，因为它们的元素不能具有自己的属性。但是在处理类似列表的对象时需要格外小心。

So replacement with empty subsetting is very safe for vectors, matrices, and arrays, because their elements can't have their own attributes. But it requires some care when dealing with list-like objects.

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