为什么使用“

I followed the discussion over HERE and am curious why is using<<- frowned upon in R. What kind of confusion will it cause?

我跟着这里的讨论，很好奇为什么使用<< - 在R中不赞成。它会引起什么样的困惑？

I also would like some tips on how I can avoid <<-. I use the following quite often. For example:

我也想了解如何避免<< - 的一些提示。我经常使用以下内容。例如：

### Create dummy data frame of 10 x 10 integer matrix.
### Each cell contains a number that is between 1 to 6.
df <- do.call("rbind", lapply(1:10, function(i) sample(1:6, 10, replace = TRUE)))

What I want to achieve is to shift every number down by 1, i.e all the 2s will become 1s, all the 3s will be come 2 etc. Therefore, all n would be come n-1. I achieve this by the following:

我想要实现的是将每个数字减少1，即所有2s将变为1，所有3将变为2等等。因此，所有n都将变为n-1。我通过以下方式实现这一目标：

df.rescaled <- df
sapply(2:6, function(i) df.rescaled[df.rescaled == i] <<- i-1))

In this instance, how can I avoid <<-? Ideally I would want to be able to pipe the sapply results into another variable along the lines of:

在这种情况下，我怎样才能避免<< - ？理想情况下，我希望能够将sapply结果传递到另一个变量：

df.rescaled <- sapply(...)

2 个解决方案

#1

First point

第一点

<<- is NOT the operator to assign to global variable. It tries to assign the variable in the nearest parent environment. So, say, this will make confusion:

<< - 不是分配给全局变量的运算符。它尝试在最近的父环境中分配变量。所以，比如说，这会让人感到困惑：

f <- function() {
    a <- 2
    g <- function() {
        a <<- 3
    }
}

then,

然后，

> a <- 1
> f()
> a # the global `a` is not affected
[1] 1

Second point

第二点

You can do that by using Reduce:

你可以使用Reduce来做到这一点：

Reduce(function(a, b) {a[a==b] <- a[a==b]-1; a}, 2:6, df)

or apply

或申请

apply(df, c(1, 2), function(i) if(i >= 2) {i-1} else {i})

But

但

simply, this is sufficient:

简单来说，这就足够了：

ifelse(df >= 2, df-1, df)

#2

You can think of <<- as global assignment (approximately, because as kohske points out it assigns to the top environment unless the variable name exists in a more proximal environment). Examples of why this is bad are here:

您可以将<< - 视为全局赋值（大约，因为正如kohske指出的那样，除非变量名存在于更近端的环境中，否则它将分配给顶层环境）。这里不好的原因如下：

Examples of the perils of globals in R and Stata

R和Stata中全局变量的危险例子

#1