识别组中缺失的观察结果

Question

我的代码有一些困难，希望你们中的一些人能提供帮助。

数据集如下所示：

df <- data.frame("group" = c("A", "A", "A","A_1", "A_1", "B","B","B_1"), 
                 "id" = c("id1", "id2", "id3", "id2", "id3", "id5","id1","id1"), 
                 "time" = c(1,1,1,3,3,2,2,5),
                 "Val" = c(10,10,10,10,10,12,12,12))

“group”表示个人“id”所在的组。“A_1”表示一个主题已离开该组。

例如，一个主题“id1”离开“组 A”，成为组“A_1”，其中只有“id2”和“id3”是成员。同样，“id5”离开组 B，成为“B_1”，只有 id1 作为成员。

我希望在最终数据集中拥有相反类型的组标识，应该如下所示：

final <- data.frame("group" = c("A", "A", "A","A_1", "B","B","B_1"), 
                     "id" = c("id1", "id2", "id3", "id1", "id5","id1","id5"), 
                     "time" = c(1,1,1,3,2,2,5),
                     "Val" = c(10,10,10,10,12,12,12),
                     "groupid" = c("A", "A", "A","A", "B","B","B"))

其中“A_1”和“B_1”仅表示已离开原组的科目，分别为“id1”和“id5”，而不是标识剩余科目。

有人对我如何系统地做到这一点有建议吗？

提前感谢您的帮助。

---

跟进：

我的数据比上面的例子稍微复杂一些，因为治疗有多个“出口”，而且组标识符可以具有不同的字符长度（例如 AAA 和 B）。数据看起来更像如下：

df2 <- data.frame("group" = c("AAA", "AAA", "AAA","AAA","AAA_1","AAA_1", "AAA_1","AAA_2","AAA_2","B","B","B_1"), 
                  "id" = c("id1", "id2", "id3","id4", "id2", "id3","id4", "id2","id3", "id5","id1","id1"), 
                  "time" = c(1,1,1,1,3,3,3,6,6,2,2,5),
                  "Val" = c(10,10,10,10,10,10,10,10,10,12,12,12))

id1 在时间 3 离开组 AAA，成为组 AAA_1，而在时间 6，id4 也离开组 AAA，成为组 AAA_2。如前所述，我希望带有“_”的组来标识离开该组的那些 id，而不是剩下的那个。因此最终的数据集应该是这样的：

final2 <- data.frame("group" = c("A", "A", "A","A","A_1","A_2",                     
                              "B","B","B_1"), 
                  "id" = c("id1", "id2", "id3","id4", "id1", "id4", "id5","id1","id5"), 
                  "time" = c(1,1,1,1,3,6,2,2,5),
                  "Val" = c(10,10,10,10,10,10,12,12,12))

谢谢你帮我解决这个问题

Answer 1

好的，你可以用这种方式尝试dplyr：也许它不优雅，但你得到了结果。背后的想法是首先获取group ... 中但不在相对..._1 中的那些，然后更改它们的group，获取其他的和rbind 一起：

library(dplyr)
# first you could find the one that are missing in the ..._1 groups
# and change their group to ..._1
    dups <-
           df %>%
           group_by(id, groupid = substr(group,1,1)) %>%
           filter(n() == 1)%>%
           mutate(group = paste0(group,'_1')) %>%
           left_join(df %>%
                     select(group, time, Val) %>%
                     distinct(), by ='group') %>% 
           select(group, id, time = time.y, Val = Val.y) %>%
           ungroup()

dups
# A tibble: 2 x 5
  groupid group id     time   Val
  <chr>   <chr> <fct> <dbl> <dbl>
1 A       A_1   id1       3    10
2 B       B_1   id5       5    12

# now you can select the ones that are in both groups:
dups2 <-
        df %>%
        filter(nchar(as.character(group)) == 1) %>%
        mutate(groupid = substr(group,1,1))

   dups2
  group  id time Val groupid
1     A id1    1  10       A
2     A id2    1  10       A
3     A id3    1  10       A
4     B id5    2  12       B
5     B id1    2  12       B

最后，rbind() 他们，arrange() 他们和order() 列：

rbind(dups, dups2) %>%
 arrange(group) %>%
 select(group, id, time, Val, groupid)

# A tibble: 7 x 5
  group id     time   Val groupid
  <chr> <fct> <dbl> <dbl> <chr>  
1 A     id1       1    10 A      
2 A     id2       1    10 A      
3 A     id3       1    10 A      
4 A_1   id1       3    10 A      
5 B     id5       2    12 B      
6 B     id1       2    12 B      
7 B_1   id5       5    12 B 

希望对你有帮助！

---

编辑：

您可以通过一些工作对其进行概括，这是我的尝试，希望对您有所帮助：

library(dplyr)
df3 <- df2

# you have to set a couple of fields you need:
df3$group <-ifelse(
  substr(df2$group,(nchar(as.character(df2$group))+1)-1,nchar(as.character(df2$group))) %in% c(0:9),
  paste0(substr(df2$group,1,1),"_",substr(df2$group,(nchar(as.character(df2$group))+1)-1,nchar(as.character(df2$group)))),
  paste0(substr(df2$group,1,1),"_0")
  )

df3$util <- as.numeric(substr(df3$group,3,3))+1

# two empty lists to populate with a nested loop:
changed <- list()
final_changed <- list()

现在先找谁变了，然后另一个：思路和上一部分一样：

for (j in c("A","B")) {
 df3_ <- df3[substr(df3$group,1,1)==j,] 
 for (i in unique(df3_$util)[1:length(unique(df3_$util))-1]) {
       temp1 <- df3_[df3_$util == i,]
       temp2 <- df3_[df3_$util == i+1,]
       changes <- temp1[!temp1$id %in% temp2$id,]
       changes$group <- paste0(j,'_',i )
       changes <- changes %>% left_join(temp2, by = 'group') %>% 
                  select(group , id = id.x, time = time.y, Val = Val.y)

     changed[[i]] <- changes
     }
  final_changed[[j]] <- changed
  }

change <- do.call(rbind,(do.call(Map, c(f = rbind, final_changed)))) %>% distinct()
change
  group  id time Val
1   A_1 id1    3  10
2   B_1 id5    5  12
3   A_2 id4    6  10

然后剩下的，拼凑起来：

remain <-
  df3 %>% mutate(group = gsub("_0", "", .$group)) %>%
  filter(nchar(as.character(group)) == 1) %>% select(-util)


rbind(change, remain) %>%
  mutate(groupid = substr(group,1,1)) %>% arrange(group) %>%
  select(group, id, time, Val, groupid)

  group  id time Val groupid
1     A id1    1  10       A
2     A id2    1  10       A
3     A id3    1  10       A
4     A id4    1  10       A
5   A_1 id1    3  10       A
6   A_2 id4    6  10       A
7     B id5    2  12       B
8     B id1    2  12       B
9   B_1 id5    5  12       B