R 按组检测序列并为子集计算新变量

我需要在data.frame中按组检测序列并计算新变量

假设我有以下

数据。frame

：

df1 <- data.frame(ID = c(1,1,1,1,1,1,1,2,2,2,3,3,3,3),
              seqs = c(1,2,3,4,5,6,7,1,2,3,1,2,3,4),
              count = c(2,1,3,1,1,2,3,1,2,1,3,1,4,1),
              product = c("A", "B", "C", "C", "A,B", "A,B,C", "D", "A", "B", "A", "A", "A,B,C", "D", "D"),
              stock = c("A", "A,B", "A,B,C", "A,B,C", "A,B,C", "A,B,C", "A,B,C,D", "A", "A,B", "A,B", "A", "A,B,C", "A,B,C,D", "A,B,C,D"))

df1

> df1
   ID seqs count product   stock
1   1    1     2       A       A
2   1    2     1       B     A,B
3   1    3     3       C   A,B,C
4   1    4     1       C   A,B,C
5   1    5     1     A,B   A,B,C
6   1    6     2   A,B,C   A,B,C
7   1    7     3       D A,B,C,D
8   2    1     1       A       A
9   2    2     2       B     A,B
10  2    3     1       A     A,B
11  3    1     3       A       A
12  3    2     1   A,B,C   A,B,C
13  3    3     4       D A,B,C,D
14  3    4     1       D A,B,C,D

在本例中，这适用于：

对于这些ID和行，我需要计算一个名为

new

的度量值，该度量值取序列最后一行

的
产品
的值，如果
位于序列的第二行，而不是第一个序列的
库存

预期结果如下所示：

> output
  ID seq1 seq2 seq3 new
1  1    2    3    4   C
2  2    1    2    3    
3  3    2    3    4   D

注意：

在ID检测序列中，未向库存添加新产品
在原始数据中，有许多ID没有任何序列
一些
ID
具有多个限定序列。所有这些都应该记录下来
计数始终为1或更大
原始数据包含数百万个
ID
，最多包含1500个序列
如何编写一段高效的代码来获得此输出？
这里有一个
数据。表
选项：

库（data.table）
char_cols这里是另一种使用；然而，我认为
lag
和
lead
使得这个解决方案有点耗时。我在代码中加入了注释，使其更清晰

但我花了足够的时间在上面，不管怎样，我还是把它贴出来了

计数是否可以小于1？@C.Braun No。计数始终为1或更大。
setDT
将
data.frame
转换为
data.table
。您可以使用
setDF
进行转换。
 - rows 2, 3, 4 for `ID==1`
 - rows 8, 9, 10 for `ID==2`
 - rows 12, 13, 14 for `ID==3`

> output
  ID seq1 seq2 seq3 new
1  1    2    3    4   C
2  2    1    2    3    
3  3    2    3    4   D

library(tidyverse)

df1 %>% group_by(ID) %>%  

 # this finds the row with count > 1 which ...
 #... the counts of the row before and the one of after it equals to 1
 mutate(test = (count > 1 & c(F, lag(count==1)[-1]) & c(lead(count==1)[-n()],F))) %>% 

 # this makes a column which has value of True for each chunk...      
 #that meets desired condition to later filter based on it
 mutate(test2 = test | c(F,lag(test)[-1]) | c(lead(test)[-n()], F))  %>% 

 filter(test2) %>% ungroup() %>% 

 # group each three occurrences in case of having multiple ones within each ID
 group_by(G=trunc(3:(n()+2)/3)) %>% group_by(ID,G) %>% 

 # creating new column with string extracting techniques ...
 #... (assuming those columns are characters) 
 mutate(new=
 str_remove_all(
    as.character(regmatches(stock[2], gregexpr(product[3], stock[2]))),
               stock[1])) %>% 

  # selecting desired columns and adding times for long to wide conversion
  select(ID,G,seqs,new) %>% mutate(times = 1:n()) %>% ungroup() %>% 

  # long to wide conversion using tidyr (part of tidyverse)
  gather(key, value, -ID, -G, -new, -times) %>%
  unite(col, key, times) %>% spread(col, value) %>% 

  # making the desired order of columns
  select(-G,-new,new) %>% as.data.frame()

#   ID seqs_1 seqs_2 seqs_3 new
# 1  1      2      3      4   C
# 2  2      1      2      3    
# 3  3      2      3      4   D