在将R中的var分组分开的某些观察结果之前选择组 [英] select group before certain observations separated by grouping var in R

问题描述

在本主题的继续中
在某些观察之前选择组在R
中，我对var- add （x或y）进行了分组

in the continuation of this topic select group before certain observations in R I have grouping var -add (x or y)

    data=structure(list(add = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("x", 
"y"), class = "factor"), x1 = c(14L, 15L, 36L, 53L, 95L, 56L, 
53L, 10L, 39L, 27L, 67L, 25L, 19L, 49L, 53L, 64L, 61L, 12L, 75L, 
34L, 88L, 43L, 85L, 93L, 44L, 31L, 37L, 90L, 66L, 39L, 59L, 96L, 
41L, 23L, 20L, 26L, 69L, 28L, 35L, 96L, 87L, 82L, 70L, 68L, 26L, 
12L, 58L, 18L, 76L, 93L, 3L, 31L), group = structure(c(2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("female", "male"), class = "factor")), .Names = c("add", 
"x1", "group"), class = "data.frame", row.names = c(NA, -52L))

此分析如何按组划分？

AntoniosK的解决方案非常好

The solutuion of AntoniosK is very good

library(tidyverse)
library(data.table)

data %>%
  group_by(group, group2 = rleid(group)) %>%                       
  mutate(MEAN = mean(x1[group=="male" & group2==1]),               
         Q25 = quantile(x1[group=="male" & group2==1], 0.25)) %>%
  ungroup() %>%
  mutate(x1 = ifelse(group=="male" & group2==3 & x1 > unique(Q25[!is.na(Q25)]), unique(MEAN[!is.na(MEAN)]), x1)) %>%
  ungroup() %>%
  select(-group2) %>%
  data.frame()

但是如果我要为x和y组单独执行它。我这样做

but if i want for x and y group perform it separately. I do so

data %>% group_by(add) %>% 
  group_by(group, group2 = rleid(group)) %>%                       
  mutate(MEAN = mean(x1[group=="male" & group2==1]),               
         Q25 = quantile(x1[group=="male" & group2==1], 0.25)) %>%
  ungroup() %>%
  mutate(x1 = ifelse(group=="male" & group2==3 & x1 > unique(Q25[!is.na(Q25)]), unique(MEAN[!is.na(MEAN)]), x1)) %>%
  ungroup() %>%
  select(-group2) %>%
  data.frame()

由于结果统计不正确

   add       x1  group     MEAN   Q25
1    x 14.00000   male 46.86364 26.25
2    x 15.00000   male 46.86364 26.25
3    x 36.00000   male 46.86364 26.25
4    x 53.00000   male 46.86364 26.25
5    x 95.00000   male 46.86364 26.25
6    x 56.00000   male 46.86364 26.25
7    x 53.00000   male 46.86364 26.25
8    x 10.00000   male 46.86364 26.25
9    x 39.00000   male 46.86364 26.25
10   x 27.00000   male 46.86364 26.25
11   x 67.00000   male 46.86364 26.25
12   x 25.00000 female      NaN    NA
13   x 19.00000 female      NaN    NA
14   x 49.00000 female      NaN    NA
15   x 53.00000 female      NaN    NA
16   x 64.00000 female      NaN    NA
17   x 61.00000 female      NaN    NA
18   x 12.00000 female      NaN    NA
19   x 46.86364   male      NaN    NA
20   x 46.86364   male      NaN    NA
21   x 46.86364   male      NaN    NA
22   x 46.86364   male      NaN    NA
23   x 46.86364   male      NaN    NA
24   x 46.86364   male      NaN    NA
25   x 46.86364   male      NaN    NA
26   x 46.86364   male      NaN    NA
27   y 37.00000   male 46.86364 26.25
28   y 90.00000   male 46.86364 26.25
29   y 66.00000   male 46.86364 26.25
30   y 39.00000   male 46.86364 26.25
31   y 59.00000   male 46.86364 26.25
32   y 96.00000   male 46.86364 26.25
33   y 41.00000   male 46.86364 26.25
34   y 23.00000   male 46.86364 26.25
35   y 20.00000   male 46.86364 26.25
36   y 26.00000   male 46.86364 26.25
37   y 69.00000   male 46.86364 26.25
38   y 28.00000 female      NaN    NA
39   y 35.00000 female      NaN    NA
40   y 96.00000 female      NaN    NA
41   y 87.00000 female      NaN    NA
42   y 82.00000 female      NaN    NA
43   y 70.00000 female      NaN    NA
44   y 68.00000 female      NaN    NA
45   y 26.00000   male      NaN    NA
46   y 12.00000   male      NaN    NA
47   y 46.86364   male      NaN    NA
48   y 18.00000   male      NaN    NA
49   y 46.86364   male      NaN    NA
50   y 46.86364   male      NaN    NA
51   y  3.00000   male      NaN    NA
52   y 46.86364   male      NaN    NA

均值 x 女性= 42
平均fo r y 男性，女性之前= 51

mean for x for male before female=42 mean for y for male before female=51

推荐答案

这应该有效：

data %>%  
  group_by(add) %>%                                           # for each add do the below...
  mutate(group2 = rleid(group)) %>% 
  group_by(add, group, group2) %>%
  mutate(MEAN = mean(x1[group=="male" & group2==1]),               
         Q25 = quantile(x1[group=="male" & group2==1], 0.25)) %>%
  group_by(add) %>%                                            # for each add update x1 values....
  mutate(x1 = ifelse(group=="male" & group2==3 & x1 > unique(Q25[!is.na(Q25)]), unique(MEAN[!is.na(MEAN)]), x1)) %>%
  ungroup() %>%
  select(-group2) %>%
  data.frame()

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