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基于按特定日期的事件分组的行，将新列添加到data.frame'；长度

r

基于按特定日期的事件分组的行，将新列添加到data.frame'；长度,r,grouping,add,rows,col,R,Grouping,Add,Rows,Col,Day列显示天数 Count列显示特定日期的ID总和 Count\u sum列按12天分块显示ID的总和，即天+天-1+天-2+天-3+天-4+天-5+天-6+天-7+天-8+天-9+天-10+天-11 e、 g。 1） Count_sum=29，因为它表示3（第9535天）+2（第9534天）+4（第9533天）+1（第9532天）+2（第9531天）+1（第9530天）+2（第9529天）+1（第9528天）+3（第9527天）+4（第9526天）+1（第9525天）+5（第9524天）之和

Day
列显示天数
Count
列显示特定日期的ID总和
Count\u sum
列按12天分块显示
ID
的总和，即天+天-1+天-2+天-3+天-4+天-5+天-6+天-7+天-8+天-9+天-10+天-11

e、 g。 1） Count_sum=29，因为它表示3（第9535天）+2（第9534天）+4（第9533天）+1（第9532天）+2（第9531天）+1（第9530天）+2（第9529天）+1（第9528天）+3（第9527天）+4（第9526天）+1（第9525天）+5（第9524天）之和

2） 由于3（第9537天）+1（第9536天）+3（第9535天）+2（第9534天）+4（第9533天）+1（第9532天）+2（第9531天）+1（第9530天）+2（第9529天）+1（第9528天）+3（第9527天）+4（第9526天），计数总和=27

等等等等

我需要做的是在
df
中添加第5列（插曲ID），将每12天的插曲以1到21的唯一值进行分组（因为在
df
中有21天是唯一的）

Count_sum几乎可以正确地对它们进行分组，但可能有2次或更多的12天发作，具有相同的Count_sum值，并且在几天内也可能重叠

我的real data.frame包含>300000行，我还想获得一个代码，该代码适用于12天的剧集（如
df
），但也适用于按2,3,4,5,6,7,8,n天分组的其他data.frame

这里是我对
df
（12天剧集）的预期输出：

如果您看到输出，在
Count\u sum
=27中有两个不同的片段，4个片段用于
Count\u sum
=25，2个片段用于
Count\u sum
=24，等等

“事件ID”列从1到21开始，其中1是具有最大计数组的事件，当有2个或更多具有相同计数组的事件时，它们需要按天递减=真排序

以下是我尝试过但不起作用的内容：

(一)

df$eposion\u ID 1）+c（0，差异（df$Count\u sum）！=0）>0）
(二)

库（data.table）

插曲ID我必须承认我还没有完全理解所有细节，特别是，插曲没有明确的定义，提供的数据在我看来与如何计算
计数和
的描述不完全匹配

尽管如此，我还是能够重现预期的结果

提出的解决方案是基于观察到的
天
由若干单调递减序列组成（这大概就是OP所指的情节）。因此，任务是识别新序列开始的断点，推进序列计数器，并使用该序列id对该序列的所有后续行进行编号

这是通过以下方式实现的：


library(data.table)
Episode_ID <-setDT(df)[, if(Count_sum[1L] < .N) ((seq_len(.N)-1) %/% Count_sum[1L])+1  
                      else as.numeric(Count_sum), rleid(Count_sum)][, rleid(V1)]
df = df[, Episode_ID := Episode_ID]


请注意，这里使用OP提供的第二组数据来证明计算结果与预期结果一致。例如，第一次中断发生在第29行和第30行之间：


library(data.table)   # CRAN version 1.10.4 used
setDT(expected)[, Sequence_ID := cumsum(Day - shift(Day, fill = -1L) > 0)]


表达式已识别从第9524天到第9537天的跳跃

不幸的是，最后出现了一个差异：


expected[28:31]
#      ID  Day Count Count_sum Episode_ID Sequence_ID
#1: 58008 9524     5        29          1           1
#2: 59001 9524     5        29          1           1
#3: 58008 9537     3        27          2           2
#4: 66001 9537     3        27          2           2


OP已将最后一行指定给新的剧集，尽管天数仍按单调递减顺序排列。如果这只是提供的数据中的一个错误，我们就完成了

如果这是有意的，那么在使用
数据对剧集进行编号时，必须考虑
计数和的变化。表
的方便
rleid（）
功能：


tail(expected, 11)
#       ID  Day Count Count_sum Episode_ID Sequence_ID
# 1: 74001 9520     1         7         18          18
# 2: 33021 9518     1         7         18          18
# 3: 76007 9521     4         6         19          19
# 4: 77002 9521     4         6         19          19
# 5: 77003 9521     4         6         19          19
# 6: 78003 9521     4         6         19          19
# 7: 74001 9520     1         6         19          19
# 8: 33021 9518     1         6         19          19
# 9: 74001 9520     1         2         20          20
#10: 33021 9518     1         2         20          20
#11: 33021 9518     1         1         21          20


这也可以更简洁地写成一行


expected[, new_Episode_ID := rleid(Sequence_ID, Count_sum)]
tail(expected, 5L)
#      ID  Day Count Count_sum Episode_ID Sequence_ID new_Episode_ID
#1: 74001 9520     1         6         19          19             19
#2: 33021 9518     1         6         19          19             19
#3: 74001 9520     1         2         20          20             20
#4: 33021 9518     1         2         20          20             20
#5: 33021 9518     1         1         21          20             21


资料

请解释差异所在。使用提供的数据，预期的
插曲ID
与计算的
新插曲ID
精确匹配。谢谢。这很遗憾，特别是你已经花了很多时间写这篇文章和相关的问题。我一直觉得你的Q是一个更大的问题的一部分，你还没有完全披露。尽管如此，我还是花了两个小时来解决你的具体问题，并写了一个答案。你应该感到荣幸。请参阅。您提供的数据与我的结果之间没有差异
expected[Spidence\u ID！=new\u Spidence\u ID]空数据。表（0行）共6列：ID、Day、Count、Count\u sum、Spidence\u ID、new\u Spidence\u ID
您好，您的代码与输入数据框（即expected）完美配合，这与我的输入df不同，因此代码不起作用。请告诉我如何按“预期”订购“df”（但不是手动订购！）。thanksI还注意到您手动将插曲ID列添加到预期中……我的意思是……我也可以使用df自己完成……但是我正在使用的data.frame已超过300000行…………与您第一次尝试的解决方案类似的内容应该可以工作。我会做
df$eposion_ID 0 | diff（df$Count_sum）它不起作用。Count_sum=27有两集：一集从第9537天开始（最多9526集），第二集从第9534天开始（最多9523集）。确实有54行的Count_sum=27！！！这两个数据帧不同，因为在具有相同计数和值且天数重叠的剧集中，输入df的顺序不正确！！！！！两个数据帧中没有错误。查看输出，您将看到计数为27的两个不同的片段。这是第二集和第三集！请仔细阅读我的问题。

expected[28:31]
#      ID  Day Count Count_sum Episode_ID Sequence_ID
#1: 58008 9524     5        29          1           1
#2: 59001 9524     5        29          1           1
#3: 58008 9537     3        27          2           2
#4: 66001 9537     3        27          2           2



tail(expected, 11)
#       ID  Day Count Count_sum Episode_ID Sequence_ID
# 1: 74001 9520     1         7         18          18
# 2: 33021 9518     1         7         18          18
# 3: 76007 9521     4         6         19          19
# 4: 77002 9521     4         6         19          19
# 5: 77003 9521     4         6         19          19
# 6: 78003 9521     4         6         19          19
# 7: 74001 9520     1         6         19          19
# 8: 33021 9518     1         6         19          19
# 9: 74001 9520     1         2         20          20
#10: 33021 9518     1         2         20          20
#11: 33021 9518     1         1         21          20



expected[, new_Episode_ID := rleid(Sequence_ID, Count_sum)]
tail(expected, 5L)
#      ID  Day Count Count_sum Episode_ID Sequence_ID new_Episode_ID
#1: 74001 9520     1         6         19          19             19
#2: 33021 9518     1         6         19          19             19
#3: 74001 9520     1         2         20          20             20
#4: 33021 9518     1         2         20          20             20
#5: 33021 9518     1         1         21          20             21



expected[, new_Episode_ID := rleid(cumsum(Day - shift(Day, fill = -1L) > 0), Count_sum)]



expected <- structure(list(ID = c(33021L, 33029L, 34001L, 32010L, 33023L, 
45012L, 47001L, 48010L, 50001L, 49004L, 9002L, 67008L, 40011L, 
42003L, 42011L, 55023L, 40012L, 43007L, 47011L, 52004L, 52005L, 
52006L, 52007L, 19001L, 57008L, 57010L, 58006L, 58008L, 59001L, 
58008L, 66001L, 68001L, 54057L, 33021L, 33029L, 34001L, 32010L, 
33023L, 45012L, 47001L, 48010L, 50001L, 49004L, 9002L, 67008L, 
40011L, 42003L, 42011L, 55023L, 40012L, 43007L, 47011L, 52004L, 
52005L, 52006L, 52007L, 32010L, 33023L, 45012L, 47001L, 48010L, 
50001L, 49004L, 9002L, 67008L, 40011L, 42003L, 42011L, 55023L, 
40012L, 43007L, 47011L, 52004L, 52005L, 52006L, 52007L, 19001L, 
57008L, 57010L, 58006L, 58008L, 59001L, 65004L, 49004L, 9002L, 
67008L, 40011L, 42003L, 42011L, 55023L, 40012L, 43007L, 47011L, 
52004L, 52005L, 52006L, 52007L, 19001L, 57008L, 57010L, 58006L, 
58008L, 59001L, 65004L, 75003L, 76007L, 77002L, 77003L, 78003L, 
48007L, 48011L, 58008L, 66001L, 68001L, 54057L, 33021L, 33029L, 
34001L, 32010L, 33023L, 45012L, 47001L, 48010L, 50001L, 49004L, 
9002L, 67008L, 40011L, 42003L, 42011L, 55023L, 40012L, 43007L, 
47011L, 54057L, 33021L, 33029L, 34001L, 32010L, 33023L, 45012L, 
47001L, 48010L, 50001L, 49004L, 9002L, 67008L, 40011L, 42003L, 
42011L, 55023L, 40012L, 43007L, 47011L, 52004L, 52005L, 52006L, 
52007L, 19001L, 45012L, 47001L, 48010L, 50001L, 49004L, 9002L, 
67008L, 40011L, 42003L, 42011L, 55023L, 40012L, 43007L, 47011L, 
52004L, 52005L, 52006L, 52007L, 19001L, 57008L, 57010L, 58006L, 
58008L, 59001L, 65004L, 75003L, 9002L, 67008L, 40011L, 42003L, 
42011L, 55023L, 40012L, 43007L, 47011L, 52004L, 52005L, 52006L, 
52007L, 19001L, 57008L, 57010L, 58006L, 58008L, 59001L, 65004L, 
75003L, 76007L, 77002L, 77003L, 78003L, 74001L, 39093L, 41006L, 
48007L, 48011L, 58008L, 66001L, 68001L, 54057L, 33021L, 33029L, 
34001L, 32010L, 33023L, 45012L, 47001L, 48010L, 50001L, 49004L, 
9002L, 67008L, 40011L, 42003L, 42011L, 55023L, 40011L, 42003L, 
42011L, 55023L, 40012L, 43007L, 47011L, 52004L, 52005L, 52006L, 
52007L, 19001L, 57008L, 57010L, 58006L, 58008L, 59001L, 65004L, 
75003L, 76007L, 77002L, 77003L, 78003L, 74001L, 42003L, 42011L, 
55023L, 40012L, 43007L, 47011L, 52004L, 52005L, 52006L, 52007L, 
19001L, 57008L, 57010L, 58006L, 58008L, 59001L, 65004L, 75003L, 
76007L, 77002L, 77003L, 78003L, 74001L, 33021L, 55023L, 40012L, 
43007L, 47011L, 52004L, 52005L, 52006L, 52007L, 19001L, 57008L, 
57010L, 58006L, 58008L, 59001L, 65004L, 75003L, 76007L, 77002L, 
77003L, 78003L, 74001L, 33021L, 40012L, 43007L, 47011L, 52004L, 
52005L, 52006L, 52007L, 19001L, 57008L, 57010L, 58006L, 58008L, 
59001L, 65004L, 75003L, 76007L, 77002L, 77003L, 78003L, 74001L, 
33021L, 52004L, 52005L, 52006L, 52007L, 19001L, 57008L, 57010L, 
58006L, 58008L, 59001L, 65004L, 75003L, 76007L, 77002L, 77003L, 
78003L, 74001L, 33021L, 19001L, 57008L, 57010L, 58006L, 58008L, 
59001L, 65004L, 75003L, 76007L, 77002L, 77003L, 78003L, 74001L, 
33021L, 57008L, 57010L, 58006L, 58008L, 59001L, 65004L, 75003L, 
76007L, 77002L, 77003L, 78003L, 74001L, 33021L, 65004L, 75003L, 
76007L, 77002L, 77003L, 78003L, 74001L, 33021L, 75003L, 76007L, 
77002L, 77003L, 78003L, 74001L, 33021L, 76007L, 77002L, 77003L, 
78003L, 74001L, 33021L, 74001L, 33021L, 33021L), Day = c(9535L, 
9535L, 9535L, 9534L, 9534L, 9533L, 9533L, 9533L, 9533L, 9532L, 
9531L, 9531L, 9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 
9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 9524L, 
9524L, 9537L, 9537L, 9537L, 9536L, 9535L, 9535L, 9535L, 9534L, 
9534L, 9533L, 9533L, 9533L, 9533L, 9532L, 9531L, 9531L, 9530L, 
9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 9526L, 9526L, 9526L, 
9526L, 9534L, 9534L, 9533L, 9533L, 9533L, 9533L, 9532L, 9531L, 
9531L, 9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 9526L, 
9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 9524L, 9524L, 
9523L, 9532L, 9531L, 9531L, 9530L, 9529L, 9529L, 9528L, 9527L, 
9527L, 9527L, 9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 
9524L, 9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 9521L, 9521L, 
9538L, 9538L, 9537L, 9537L, 9537L, 9536L, 9535L, 9535L, 9535L, 
9534L, 9534L, 9533L, 9533L, 9533L, 9533L, 9532L, 9531L, 9531L, 
9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 9536L, 9535L, 
9535L, 9535L, 9534L, 9534L, 9533L, 9533L, 9533L, 9533L, 9532L, 
9531L, 9531L, 9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 
9526L, 9526L, 9526L, 9526L, 9525L, 9533L, 9533L, 9533L, 9533L, 
9532L, 9531L, 9531L, 9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 
9527L, 9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 
9524L, 9524L, 9523L, 9522L, 9531L, 9531L, 9530L, 9529L, 9529L, 
9528L, 9527L, 9527L, 9527L, 9526L, 9526L, 9526L, 9526L, 9525L, 
9524L, 9524L, 9524L, 9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 
9521L, 9521L, 9520L, 9539L, 9539L, 9538L, 9538L, 9537L, 9537L, 
9537L, 9536L, 9535L, 9535L, 9535L, 9534L, 9534L, 9533L, 9533L, 
9533L, 9533L, 9532L, 9531L, 9531L, 9530L, 9529L, 9529L, 9528L, 
9530L, 9529L, 9529L, 9528L, 9527L, 9527L, 9527L, 9526L, 9526L, 
9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 9524L, 9524L, 9523L, 
9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 9529L, 9529L, 9528L, 
9527L, 9527L, 9527L, 9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 
9524L, 9524L, 9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 9521L, 
9521L, 9520L, 9518L, 9528L, 9527L, 9527L, 9527L, 9526L, 9526L, 
9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 9524L, 9524L, 9523L, 
9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 9518L, 9527L, 9527L, 
9527L, 9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 
9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 
9518L, 9526L, 9526L, 9526L, 9526L, 9525L, 9524L, 9524L, 9524L, 
9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 
9518L, 9525L, 9524L, 9524L, 9524L, 9524L, 9524L, 9523L, 9522L, 
9521L, 9521L, 9521L, 9521L, 9520L, 9518L, 9524L, 9524L, 9524L, 
9524L, 9524L, 9523L, 9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 
9518L, 9523L, 9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 9518L, 
9522L, 9521L, 9521L, 9521L, 9521L, 9520L, 9518L, 9521L, 9521L, 
9521L, 9521L, 9520L, 9518L, 9520L, 9518L, 9518L), Count = c(3L, 
3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 1L, 
3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 2L, 2L, 
1L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 
5L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 4L, 4L, 4L, 2L, 2L, 3L, 3L, 
3L, 1L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 2L, 2L, 1L, 2L, 
2L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 1L, 
2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 
4L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 4L, 4L, 
4L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 2L, 2L, 4L, 
4L, 4L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 4L, 4L, 
4L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 
5L, 5L, 1L, 1L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 4L, 4L, 
4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 4L, 4L, 4L, 1L, 1L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 
4L, 4L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 
1L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 
4L, 4L, 4L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 1L, 1L, 4L, 4L, 4L, 4L, 
1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 1L, 
1L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L), Count_sum = c(29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
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