分配给 LHS 上的空索引(空方括号 x[]<-) [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赋值语句.它没有使用通常的 myVar<- myValue 形式，而是使用 myVar[]<- myValue 形式，即左侧的对象用空方括号索引.就我个人而言，我从未见过这样的赋值，但它有一个非常有用的效果——它将赋值的数据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，因为这会让事情变得更容易.然而，"<-" 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吗?人们可以依靠它在所有情况下工作吗?
Is this a well established feature and one can rely on it to work in all cases?
另外，我猜测这可能是函数调用堆栈的副作用，即依次调用<-和[，但我想不通出如何.有人可以对此有所了解吗?
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
这是一个有意并记录在案的功能.正如 joran 所提到的，文档页面Extract"在Atomic Vectors"部分包含了这一点:
This is an intentional and documented feature. As joran mentioned, the documentation page "Extract" includes this in the "Atomic Vectors" section:
空索引选择所有值:这最常用于替换所有条目但保留属性.
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(例如，data.frames 或 lists)，属性仅保留为子集化的对象.它的部件没有得到这样的保护.
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 保留了它的 rownames 属性，但 x 列只是分配中使用的字符向量而不是因子.我们可以通过专门替换它的值来保持 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
所以用空子集替换对于向量、矩阵和数组来说是非常安全的，因为它们的元素不能有自己的属性.但是在处理类似列表的对象时需要小心.
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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