条件发生后如何过滤掉每组的行答案

【问题标题】：How to Filter out Rows per Group after Condition Occurrs条件发生后如何过滤掉每组的行
【发布时间】：2018-10-11 18:02:49
【问题描述】：

我是 R 编程新手，并尝试在满足过滤条件后删除一组行中的某些行。

场景：对于每个 GROUP，如果 2 个 TYPE "B" 在一行中，则删除该 GROUP 的所有以下行。 “Include in DataSet”列显示输出应该是什么。

这是我的示例输入：

GROUP   TYPE    Include in DataSet?
--------------------------------------------
1       A       yes
1       A       yes
1       B       yes
1       B       yes
1       B       no
2       A       yes
2       B       yes
2       B       yes
2       A       no
2       B       no
2       B       no

DF = structure(list(GROUP = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L), TYPE = c("A", "A", "B", "B", "B", "A", "B", "B", "A", 
"B", "B"), inc = c("yes", "yes", "yes", "yes", "no", "yes", "yes", 
"yes", "no", "no", "no")), .Names = c("GROUP", "TYPE", "inc"), row.names = c(NA, 
-11L), class = "data.frame")

预期输出：

GROUP   TYPE    Include in DataSet?
--------------------------------------------
1       A       yes
1       A       yes
1       B       yes
1       B       yes
2       A       yes
2       B       yes
2       B       yes

我尝试编写了一些代码，但由于分组问题没有成功。

i=1
j=2
x <- allrows
for (i in x){
  for(j in x){
    if(i==j){
      a$REMOVE=1
    }
    else{
      a$REMOVE=2
    }
  }
}

【问题讨论】：

标签： r dplyr

【解决方案1】：

您可以通过创建一个标识“双 B”行的新变量来做到这一点，然后过滤掉组中第一个“双 B”行之后的行：

library(dplyr)
df %>%
    group_by(GROUP) %>%
    # Create new variable that tests if each row and the one below it TYPE==B
    mutate(double_B = (TYPE == 'B' & lag(TYPE) == 'B')) %>%
    # Find the first row with `double_B` in each group, filter out rows after it
    filter(row_number() <= min(which(double_B == TRUE))) %>%
    # Optionally, remove `double_B` column when done with it
    select(-double_B)

# A tibble: 7 x 3
# Groups:   GROUP [2]
  GROUP TYPE  IncludeinDataSet
  <int> <chr> <chr>           
1     1 A     yes             
2     1 A     yes             
3     1 B     yes             
4     1 B     yes             
5     2 A     yes             
6     2 B     yes             
7     2 B     yes

正如@Frank 在评论中指出的那样，您不需要创建double_B 变量：您只需在which 内的which 语句中测试“双B”条件@：

df %>%
    group_by(GROUP) %>%
    # Find the first row with `double_B` in each group, filter out rows after it
    filter(row_number() <= min(which(TYPE == 'B' & lag(TYPE) == 'B')))

此外，如果在组中未找到“双 B”条件，它将返回警告，但仍会正确过滤

【讨论】：

Re 是“可选”，另一种方法是使用条件而不指定名称：df %>% group_by(GROUP) %>% filter(row_number() <= min(which(TYPE == 'B' & lag(TYPE) == 'B')))。顺便说一句，如果从来没有双 B，你会收到警告（例如，尝试min(which(FALSE))），但我不确定是否有办法解决这个问题。

【解决方案2】：

这可以通过检查'TYPE'的当前值和'TYPE'的下一个值来找到数字索引，使用seq_len获取从1到该数字的序列以对行进行子集（在@ 987654322@)

library(dplyr)
df1 %>% 
  group_by(GROUP) %>% 
  slice(seq_len(which((TYPE == "B") & lead(TYPE) == "B")[1] + 1))
# A tibble: 7 x 3
# Groups:   GROUP [2]
#  GROUP TYPE  IncludeInDataSet
#  <int> <chr> <chr>           
#1     1 A     yes             
#2     1 A     yes             
#3     1 B     yes             
#4     1 B     yes             
#5     2 A     yes             
#6     2 B     yes             
#7     2 B     yes

数据

df1 <- structure(list(GROUP = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
 2L, 2L), TYPE = c("A", "A", "B", "B", "B", "A", "B", "B", "A", 
 "B", "B"), IncludeInDataSet = c("yes", "yes", "yes", "yes", "no", 
  "yes", "yes", "yes", "no", "no", "no")), class = "data.frame", 
 row.names = c(NA, -11L))

【讨论】：

【解决方案3】：

另一种方法可能是：

library(dplyr)
library(data.table)

df %>%
  group_by(GROUP, rleid(TYPE)) %>%
  mutate(temp = seq_along(TYPE)) %>%
  ungroup() %>%
  group_by(GROUP) %>%
  filter(row_number() <= min(which(TYPE == "B" & temp == 2))) %>%
  select(GROUP, TYPE, IncludeInDataSet)

【讨论】：

【解决方案4】：

这是一个基本的 R 解决方案：

subset(DF, as.logical(ave(DF$TYPE,DF$GROUP, FUN= function(x) 
  seq_along(x) <= which((sequence(rle(x=="B")$length) * (x=="B")) %in% 2)[1])))
#   GROUP TYPE inc
# 1     1    A yes
# 2     1    A yes
# 3     1    B yes
# 4     1    B yes
# 6     2    A yes
# 7     2    B yes
# 8     2    B yes

【讨论】：