R中的循环 - 需要使用索引，反正避免“for”？ [英] Loops in R - Need to use index, anyway to avoid 'for'?

问题描述

我知道在R中使用 for 循环不是最好的做法，因为它没有增强的性能。几乎所有的情况下都有一个 * apply 这个函数解决了我们的问题。

然而，面临的情况，我没有看到一个解决方法。

我需要计算连续值的百分比变化：

< （x）= {
pv [i] < - （x [i ] - x [i-1]）/ x [i-1]
}

所以，你可以看到，我必须同时使用 x [i] 元素，而且 x [i-1] 元素。通过使用 * apply 函数，我只是看到如何使用 x [i] 。有没有反正我可以避免为循环？

解决方案

所提供的将是分数变化，但如果你乘以100，你会得到百分比变化：

pre $ pv < - vector长度（x））
pv [1] < - 0
pv [-1] < - 100 *（x [-1] - x [-length（x）] ）/ x [-length（x）]


向量化解决方案。 （你应该注意到，for循环会像应用解决方案一样慢......只是不够漂亮，总是寻找一个向量化的方法。）

解释更多： x [-length（x）] 是向量， x [1：（length {x-1） ] ， x [-1] 是向量， x [2：length（x）] ，并且R中的向量操作和你的for循环体一样，虽然不使用显式循环。 R首先在这些移位向量中构造差异，然后除以 x [ 1：（length {x-1）] 。

I know it's not the best practice in R to use the for loop because it doesn't have an enhanced performance. For almost all cases there is a function of the family *apply that solves our problems.

However I'm facing a situation where I don't see a workaround.

I need to calculate percent variation for consecutive values:

pv[1] <- 0
for(i in 2:length(x)) {
  pv[i] <- (x[i] - x[i-1])/x[i-1]
}

So, as you can see, I have to use both the x[i] element, but also the x[i-1] element. By using the *apply functions, I just see how to use the x[i]. Is there anyway I can avoid the forloops?

解决方案

What you offered would be the fractional variation, but if you multiplied by 100 you get the "percent variation":

pv<- vector("numeric",length(x))
pv[1] <- 0
pv[-1] <- 100* ( x[-1] - x[-length(x)] )/ x[-length(x)]

Vectorized solution. ( And you should note that for-loops are going to be just as slow as *apply solutions ... just not as pretty. Always look for a vectorized approach.)

To explain a bit more: The x[-length(x)] is the vector, x[1:(length{x-1)], and the x[-1] is the vector, x[2:length(x)], and the vector operations in R are doing the same operations as in your for-loop body, although not using an explicit loop. R first constructs the differences in those shifted vectors, x[-length(x)] - x[-1], and then divides by x[1:(length{x-1)].

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