按百分位数分割向量 [英] split a vector by percentile

问题描述

我需要将 R 中已排序的未知长度向量拆分为前 10%，...，后 10%"因此，例如，如果我有 vector <- order(c(1:98928))，我想将其拆分为 10 个不同的向量，每个向量大约占总长度的 10%.

I need to split a sorted unknown length vector in R into "top 10%,..., bottom 10%" So, for example if I have vector <- order(c(1:98928)), I want to split it into 10 different vectors, each one representing approximately 10% of the total length.

我试过使用 split <- split(vector, 1:10) 但由于我不知道向量的长度，如果不是多个，我会得到这个错误

Ive tried using split <- split(vector, 1:10) but as I dont know the length of the vector, I get this error if its not multiple

数据长度不是拆分变量的倍数

data length is not a multiple of split variable

即使它的倍数和函数有效，split() 也不会保持我原始向量的顺序.这就是 split 给出的结果:

And even if its multiple and the function works, split() does not keep the order of my original vector. This is what split gives:

split(c(1:10) , 1:2)
$`1`
[1] 1 3 5 7 9

$`2`
[1]  2  4  6  8 10

这就是我想要的:

$`1`
[1] 1 2 3 4 5

$`2`
[1]  6  7  8  9 10

我是 R 的新手，我尝试了很多东西都没有成功，有人知道怎么做吗?

Im newbie in R and Ive been trying lots of things without success, does anyone knows how to do this?

推荐答案

问题说明

将一个已排序的向量 x 每 10% 分成 10 个块.

Problem statement

Break a sorted vector x every 10% into 10 chunks.

请注意，对此有两种解释:

Note there are two interpretation for this:

1. 按矢量索引切割:

split(x, floor(10 * seq.int(0, length(x) - 1) / length(x)))

按向量值(例如分位数)切割:

split(x, cut(x, quantile(x, prob = 0:10 / 10, names = FALSE), include = TRUE))

下面我会用数据做示范:

In the following, I will make demonstration using data:

set.seed(0); x <- sort(round(rnorm(23),1))

特别是，我们的示例数据是正态分布而不是均匀分布，因此按索引切割和按值切割有很大不同.

Particularly, our example data are Normally distributed rather than uniformly distributed, so cutting by index and cutting by value are substantially different.

按索引切割

#$`0`
#[1] -1.5 -1.2 -1.1
#
#$`1`
#[1] -0.9 -0.9
#
#$`2`
#[1] -0.8 -0.4
#
#$`3`
#[1] -0.3 -0.3 -0.3
#
#$`4`
#[1] -0.3 -0.2
#
#$`5`
#[1] 0.0 0.1
#
#$`6`
#[1] 0.3 0.4 0.4
#
#$`7`
#[1] 0.4 0.8
#
#$`8`
#[1] 1.3 1.3
#
#$`9`
#[1] 1.3 2.4

按分位数切割

#$`[-1.5,-1.06]`
#[1] -1.5 -1.2 -1.1
#
#$`(-1.06,-0.86]`
#[1] -0.9 -0.9
#
#$`(-0.86,-0.34]`
#[1] -0.8 -0.4
#
#$`(-0.34,-0.3]`
#[1] -0.3 -0.3 -0.3 -0.3
#
#$`(-0.3,-0.2]`
#[1] -0.2
#
#$`(-0.2,0.14]`
#[1] 0.0 0.1
#
#$`(0.14,0.4]`
#[1] 0.3 0.4 0.4 0.4
#
#$`(0.4,0.64]`
#numeric(0)
#
#$`(0.64,1.3]`
#[1] 0.8 1.3 1.3 1.3
#
#$`(1.3,2.4]`
#[1] 2.4

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