tibble 中的逐步采样

Question

我正在尝试通过对多个步骤进行采样来模拟一些数据。

第一步（创建 x）工作正常。

在第二步中，我想根据 x 的值从不同的向量中采样来创建变量 y。

我的代码运行没有错误，但在我试图实现的目标上失败了，因为它只对一个值进行采样，例如 x == "A"，然后为 x = 的所有后续行重复使用该值= "A"。我希望它为 x == "A"

的每一行采样一次

代码：

library(tidyverse)
set.seed(1)

data <- tibble(
  x = sample(c("A", "B", "C"), size = 10000, prob = c(0.1, 0.2, 0.7), replace = TRUE),
  y = case_when(
    x == "A" ~ sample(c("A1", "A2", "A3"), size = 1, prob = c(0.3, 0.4, 0.3)),
    x == "B" ~ sample(c("B1", "B2", "B3"), size = 1, prob = c(0.3, 0.4, 0.3)),
    x == "C" ~ sample(c("C1", "C2", "C3"), size = 1, prob = c(0.3, 0.4, 0.3)),
  ))

unique(data$x)
[1] "C" "A" "B"

unique(data$y)
[1] "C1" "A2" "B3"

如果代码按预期工作 unique(data$y) 应该 return 类似于 [1] "A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"

我知道问题出在 sample() 中的 size = 1 参数，但我可以用什么替换它？删除它 return 错误：

Error: `x == "A" ~ sample(c("A1", "A2", "A3"), prob = c(0.3, 0.4, 0.3))` must be length 100 or one, not 3

而且我已经尝试了 size = nrow(.data) 和 size=nrow(.)，但这也是 return 的错误。

有没有简单的解决方法？

Answer 1

也许有更简单的方法，但这与您的原始代码很接近并且可以工作...

data <- tibble(
  x = sample(c("A", "B", "C"), size = 10000, prob = c(0.1, 0.2, 0.7), replace = TRUE)) %>%
  rowwise() %>%
  summarise(x= x, 
            y = case_when(
    x == "A" ~ sample(c("A1", "A2", "A3"), size = 1, prob = c(0.3, 0.4, 0.3)),
    x == "B" ~ sample(c("B1", "B2", "B3"), size = 1, prob = c(0.3, 0.4, 0.3)),
    x == "C" ~ sample(c("C1", "C2", "C3"), size = 1, prob = c(0.3, 0.4, 0.3)),
  ))

Answer 2

它与向量化函数和回收有关。如果你把它矢量化，它会回收相同的值。如果你用循环来做，它就可以工作。例如，

v1 <- c('A', 'A', 'B', 'B', 'C', 'C', 'C', 'A', 'A')

#Vectorized ifelse
ifelse(v1 == 'A', sample(c("A1", "A2", "A3"), size = 1, prob = c(0.3, 0.4, 0.3)), NA)
#[1] "A3" "A3" NA   NA   NA   NA   NA   "A3" "A3"

#Not vectorized if/else with a loop,
sapply(v1, function(i) if (i == 'A') { sample(c("A1", "A2", "A3"), size = 1, prob = c(0.3, 0.4, 0.3)) }else {NA})
#   A    A    B    B    C    C    C    A    A 
#"A2" "A3"   NA   NA   NA   NA   NA "A2" "A1"

Answer 3

分解成几个步骤就容易理解了

library(dplyr)
data <- tibble(
   x = sample(c("A", "B", "C"), size = 10000, 
                prob = c(0.1, 0.2, 0.7), replace = TRUE))

data <- data %>%
  mutate(y = case_when(
     x == "A" ~ sample(c("A1", "A2", "A3"), size = n(), 
               prob = c(0.3, 0.4, 0.3), replace = TRUE),
     x == "B" ~ sample(c("B1", "B2", "B3"), size = n(), 
                 prob = c(0.3, 0.4, 0.3), replace = TRUE),
     x == "C" ~ sample(c("C1", "C2", "C3"), size = n(), 
                prob = c(0.3, 0.4, 0.3), replace = TRUE),
)) 

unique(data$y)
#[1] "C2" "B3" "A1" "C3" "B1" "C1" "B2" "A3" "A2"

或者，如果您想继续您的方式，您需要指定 size 参数，与 x 和 replace = TRUE

中提到的参数相同

data <- tibble(
  x = sample(c("A", "B", "C"), size = 10000, 
            prob = c(0.1, 0.2, 0.7), replace = TRUE),
  y = case_when(
    x == "A" ~ sample(c("A1", "A2", "A3"), size = 10000, 
                  prob = c(0.3, 0.4, 0.3), replace = TRUE),
    x == "B" ~ sample(c("B1", "B2", "B3"), size = 10000, 
                  prob = c(0.3, 0.4, 0.3), replace = TRUE),
    x == "C" ~ sample(c("C1", "C2", "C3"), size = 10000, 
                  prob = c(0.3, 0.4, 0.3), replace = TRUE),
  ))

tibble 中的逐步采样

Stepwise sampling in a tibble

random

r

dplyr

tibble