来自具有R中指定概率的分组数据帧的样本

编程问答 2022-06-11

问题描述

下面，我首先通过两个分类变量将data.frame（d）分组。首先，通过gender（2级； M / F）。其次，通过sector（教育，行业，非政府组织，私人，公共）。然后，我想从sector的每个级别中抽取以下概率：c(.2,.3,.1,.1)和gender，并遵循概率c(.4,.6)。

我正在使用下面的代码来实现我的目标而没有成功？有解决办法吗？

如果我的代码总体上符合我的正确描述，请发表评论？

d <- read.csv('https://raw.githubusercontent.com/rnorouzian/d/master/su.csv')

library(tidyverse)

set.seed(1)
(out <- d %>%
  group_by(gender,sector) %>%
  slice_sample(n = 2,weight_by = c(.4,.6,.2,.1))) # `Error:  incorrect number of probabilities`

解决方法

slice_sample并不能完全满足您的要求，因此我建议您使用splitstackshape来完成任务。根据需要安装并加载

# install.packages("splitstackshape")
library(splitstackshape)

有几种更短的方法来指定比例表，但我将从需要的总样本开始系统地进行操作，在这种情况下，我们将使n = 100为指定各种表的百分比因素水平。

total_sample <- 100
M_percent <- .4
F_percent <- .6
Education_percent <- .2
Industry_percent <- .3
NGO_percent <- .3
Private_percent <- .1
Public_percent <- .1

然后我们调用函数stratified，首先是我们要处理的两列的向量，然后是要根据上面的百分比计算出的组和想要的数目的向量... > 

abc <- 
   stratified(indt = d,c("gender","sector"),c("F Education" = F_percent * Education_percent * total_sample,"M Education" = M_percent * Education_percent * total_sample,"F Industry" = F_percent * Industry_percent * total_sample,"M Industry" = M_percent * Industry_percent * total_sample,"F NGO" = F_percent * NGO_percent * total_sample,"M NGO" = M_percent * NGO_percent * total_sample,"F Private" = F_percent * Private_percent * total_sample,"M Private" = M_percent * Private_percent * total_sample,"F Public" = F_percent * Public_percent * total_sample,"M Public" = M_percent * Public_percent * total_sample)
              )

我们取回了我们随机选择的数量

head(abc,20)
            fake.name    sector pretest state gender    pre                    email       phone
 1:            Correa Education    1254    TX      F Medium            Correa@...com xxx-xx-1886
 2:        Manzanares Education    1227    CA      F    Low        Manzanares@...com xxx-xx-1539
 3:          el-Daoud Education    1409    CA      F   High          el-Daoud@...com xxx-xx-9972
 4:            Engman Education    1436    CA      F   High            Engman@...com xxx-xx-9446
 5:           el-Kaba Education    1305    NY      F Medium           el-Kaba@...com xxx-xx-7060
 6:           Herrera Education    1405    NY      F   High           Herrera@...com xxx-xx-9146
 7:           el-Sham Education    1286    TX      F Medium           el-Sham@...com xxx-xx-4046
 8:          Harrison Education    1112    NY      F    Low          Harrison@...com xxx-xx-3118
 9:               Zhu Education    1055    CA      F    Low               Zhu@...com xxx-xx-6223
10:  Deguzman Gransee Education    1312    TX      F Medium  Deguzman Gransee@...com xxx-xx-5676
11:           Kearney Education    1303    NY      F Medium           Kearney@...com xxx-xx-5145
12: Hernandez Mendoza Education    1139    CA      F    Low Hernandez Mendoza@...com xxx-xx-9642
13:            Barros Education    1416    NY      M   High            Barros@...com xxx-xx-2455
14:            Torres Education    1370    CA      M   High            Torres@...com xxx-xx-2129
15:              King Education    1346    CA      M Medium              King@...com xxx-xx-5351
16:           Cabrera Education    1188    NY      M    Low           Cabrera@...com xxx-xx-6349
17:               Lee Education    1208    CA      M    Low               Lee@...com xxx-xx-7713
18:            Vernon Education    1216    TX      M    Low            Vernon@...com xxx-xx-7649
19:       Ripoll-Bunn Education    1419    TX      M   High       Ripoll-Bunn@...com xxx-xx-8126
20:             Ashby Education    1295    TX      M Medium             Ashby@...com xxx-xx-8416
dataframerrrandomrandomrandomsamplingtidyverse