根据按特定日期长度的情节分组的行将新列添加到data.frame [英] Add new column to data.frame based on rows grouped by episodes of specific days' length
本文介绍了根据按特定日期长度的情节分组的行将新列添加到data.frame的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
df = read.table(text = 'ID Day Count Count_sum
33021 9535 3 29
33029 9535 3 29
34001 9535 3 29
32010 9534 2 29
33023 9534 2 29
45012 9533 4 29
47001 9533 4 29
48010 9533 4 29
50001 9533 4 29
49004 9532 1 29
9002 9531 2 29
67008 9531 2 29
40011 9530 1 29
42003 9529 2 29
42011 9529 2 29
55023 9528 1 29
40012 9527 3 29
43007 9527 3 29
47011 9527 3 29
52004 9526 4 29
52005 9526 4 29
52006 9526 4 29
52007 9526 4 29
19001 9525 1 29
57008 9524 5 29
57010 9524 5 29
58006 9524 5 29
58008 9524 5 29
59001 9524 5 29
58008 9537 3 27
66001 9537 3 27
68001 9537 3 27
54057 9536 1 27
33021 9535 3 27
33029 9535 3 27
34001 9535 3 27
32010 9534 2 27
33023 9534 2 27
32010 9534 2 27
33023 9534 2 27
45012 9533 4 27
47001 9533 4 27
48010 9533 4 27
50001 9533 4 27
45012 9533 4 27
47001 9533 4 27
48010 9533 4 27
50001 9533 4 27
49004 9532 1 27
49004 9532 1 27
9002 9531 2 27
67008 9531 2 27
9002 9531 2 27
67008 9531 2 27
40011 9530 1 27
40011 9530 1 27
42003 9529 2 27
42011 9529 2 27
42003 9529 2 27
42011 9529 2 27
55023 9528 1 27
55023 9528 1 27
40012 9527 3 27
43007 9527 3 27
47011 9527 3 27
40012 9527 3 27
43007 9527 3 27
47011 9527 3 27
52004 9526 4 27
52005 9526 4 27
52006 9526 4 27
52007 9526 4 27
52004 9526 4 27
52005 9526 4 27
52006 9526 4 27
52007 9526 4 27
19001 9525 1 27
57008 9524 5 27
57010 9524 5 27
58006 9524 5 27
58008 9524 5 27
59001 9524 5 27
65004 9523 1 27
49004 9532 1 26
9002 9531 2 26
67008 9531 2 26
40011 9530 1 26
42003 9529 2 26
42011 9529 2 26
55023 9528 1 26
40012 9527 3 26
43007 9527 3 26
47011 9527 3 26
52004 9526 4 26
52005 9526 4 26
52006 9526 4 26
52007 9526 4 26
19001 9525 1 26
57008 9524 5 26
57010 9524 5 26
58006 9524 5 26
58008 9524 5 26
59001 9524 5 26
65004 9523 1 26
75003 9522 1 26
76007 9521 4 26
77002 9521 4 26
77003 9521 4 26
78003 9521 4 26
48007 9538 2 25
48011 9538 2 25
58008 9537 3 25
66001 9537 3 25
68001 9537 3 25
54057 9536 1 25
54057 9536 1 25
33021 9535 3 25
33029 9535 3 25
34001 9535 3 25
33021 9535 3 25
33029 9535 3 25
34001 9535 3 25
32010 9534 2 25
33023 9534 2 25
32010 9534 2 25
33023 9534 2 25
45012 9533 4 25
47001 9533 4 25
48010 9533 4 25
50001 9533 4 25
45012 9533 4 25
47001 9533 4 25
48010 9533 4 25
50001 9533 4 25
45012 9533 4 25
47001 9533 4 25
48010 9533 4 25
50001 9533 4 25
49004 9532 1 25
49004 9532 1 25
49004 9532 1 25
9002 9531 2 25
67008 9531 2 25
9002 9531 2 25
67008 9531 2 25
9002 9531 2 25
67008 9531 2 25
9002 9531 2 25
67008 9531 2 25
40011 9530 1 25
40011 9530 1 25
40011 9530 1 25
40011 9530 1 25
42003 9529 2 25
42011 9529 2 25
42003 9529 2 25
42011 9529 2 25
42003 9529 2 25
42011 9529 2 25
42003 9529 2 25
42011 9529 2 25
55023 9528 1 25
55023 9528 1 25
55023 9528 1 25
55023 9528 1 25
40012 9527 3 25
43007 9527 3 25
47011 9527 3 25
40012 9527 3 25
43007 9527 3 25
47011 9527 3 25
40012 9527 3 25
43007 9527 3 25
47011 9527 3 25
40012 9527 3 25
43007 9527 3 25
47011 9527 3 25
52004 9526 4 25
52005 9526 4 25
52006 9526 4 25
52007 9526 4 25
52004 9526 4 25
52005 9526 4 25
52006 9526 4 25
52007 9526 4 25
52004 9526 4 25
52005 9526 4 25
52006 9526 4 25
52007 9526 4 25
19001 9525 1 25
19001 9525 1 25
19001 9525 1 25
57008 9524 5 25
57010 9524 5 25
58006 9524 5 25
58008 9524 5 25
59001 9524 5 25
57008 9524 5 25
57010 9524 5 25
58006 9524 5 25
58008 9524 5 25
59001 9524 5 25
65004 9523 1 25
65004 9523 1 25
75003 9522 1 25
75003 9522 1 25
76007 9521 4 25
77002 9521 4 25
77003 9521 4 25
78003 9521 4 25
74001 9520 1 25
39093 9539 2 24
41006 9539 2 24
48007 9538 2 24
48011 9538 2 24
58008 9537 3 24
66001 9537 3 24
68001 9537 3 24
54057 9536 1 24
33021 9535 3 24
33029 9535 3 24
34001 9535 3 24
32010 9534 2 24
33023 9534 2 24
45012 9533 4 24
47001 9533 4 24
48010 9533 4 24
50001 9533 4 24
49004 9532 1 24
9002 9531 2 24
67008 9531 2 24
40011 9530 1 24
40011 9530 1 24
42003 9529 2 24
42011 9529 2 24
42003 9529 2 24
42011 9529 2 24
42003 9529 2 24
42011 9529 2 24
55023 9528 1 24
55023 9528 1 24
55023 9528 1 24
40012 9527 3 24
43007 9527 3 24
47011 9527 3 24
40012 9527 3 24
43007 9527 3 24
47011 9527 3 24
52004 9526 4 24
52005 9526 4 24
52006 9526 4 24
52007 9526 4 24
52004 9526 4 24
52005 9526 4 24
52006 9526 4 24
52007 9526 4 24
19001 9525 1 24
19001 9525 1 24
57008 9524 5 24
57010 9524 5 24
58006 9524 5 24
58008 9524 5 24
59001 9524 5 24
57008 9524 5 24
57010 9524 5 24
58006 9524 5 24
58008 9524 5 24
59001 9524 5 24
65004 9523 1 24
65004 9523 1 24
75003 9522 1 24
75003 9522 1 24
76007 9521 4 24
77002 9521 4 24
77003 9521 4 24
78003 9521 4 24
76007 9521 4 24
77002 9521 4 24
77003 9521 4 24
78003 9521 4 24
74001 9520 1 24
74001 9520 1 24
33021 9518 1 24
55023 9528 1 22
40012 9527 3 22
43007 9527 3 22
47011 9527 3 22
52004 9526 4 22
52005 9526 4 22
52006 9526 4 22
52007 9526 4 22
19001 9525 1 22
57008 9524 5 22
57010 9524 5 22
58006 9524 5 22
58008 9524 5 22
59001 9524 5 22
65004 9523 1 22
75003 9522 1 22
76007 9521 4 22
77002 9521 4 22
77003 9521 4 22
78003 9521 4 22
74001 9520 1 22
33021 9518 1 22
40012 9527 3 21
43007 9527 3 21
47011 9527 3 21
52004 9526 4 21
52005 9526 4 21
52006 9526 4 21
52007 9526 4 21
19001 9525 1 21
57008 9524 5 21
57010 9524 5 21
58006 9524 5 21
58008 9524 5 21
59001 9524 5 21
65004 9523 1 21
75003 9522 1 21
76007 9521 4 21
77002 9521 4 21
77003 9521 4 21
78003 9521 4 21
74001 9520 1 21
33021 9518 1 21
52004 9526 4 18
52005 9526 4 18
52006 9526 4 18
52007 9526 4 18
19001 9525 1 18
57008 9524 5 18
57010 9524 5 18
58006 9524 5 18
58008 9524 5 18
59001 9524 5 18
65004 9523 1 18
75003 9522 1 18
76007 9521 4 18
77002 9521 4 18
77003 9521 4 18
78003 9521 4 18
74001 9520 1 18
33021 9518 1 18
19001 9525 1 14
57008 9524 5 14
57010 9524 5 14
58006 9524 5 14
58008 9524 5 14
59001 9524 5 14
65004 9523 1 14
75003 9522 1 14
76007 9521 4 14
77002 9521 4 14
77003 9521 4 14
78003 9521 4 14
74001 9520 1 14
33021 9518 1 14
57008 9524 5 13
57010 9524 5 13
58006 9524 5 13
58008 9524 5 13
59001 9524 5 13
65004 9523 1 13
75003 9522 1 13
76007 9521 4 13
77002 9521 4 13
77003 9521 4 13
78003 9521 4 13
74001 9520 1 13
33021 9518 1 13
65004 9523 1 8
75003 9522 1 8
76007 9521 4 8
77002 9521 4 8
77003 9521 4 8
78003 9521 4 8
74001 9520 1 8
33021 9518 1 8
75003 9522 1 7
76007 9521 4 7
77002 9521 4 7
77003 9521 4 7
78003 9521 4 7
74001 9520 1 7
33021 9518 1 7
76007 9521 4 6
77002 9521 4 6
77003 9521 4 6
78003 9521 4 6
74001 9520 1 6
33021 9518 1 6
74001 9520 1 2
33021 9518 1 2
33021 9518 1 1', header = TRUE)
Day 列显示天；
Count 列显示该特定日期ID的总和；
Count_sum 列显示 ID 以12天为单位，即Day + Day -1 + Day -2 + Day -3 + Day -4 + Day -5 + Day -6 + Day -7 + Day -8 + Day -9 + -10天+第-11天。
Day column shows days;
Count column shows the sum of ID in that particular Day;
Count_sum column shows the sum of ID by blocks of 12 days, i.e. Day + Day -1 + Day -2 + Day -3 + Day -4 + Day -5 + Day -6 + Day -7 + Day -8 + Day -9 + Day -10 + Day -11.
例如
1）Count_sum = 29，因为它代表3（第9535天）+ 2（第9534天）+ 4（第9533天）+ 1（第9532天）+ 2（第9531天）+ 1（第9530天）的总和+ 2（第9529天）+ 1（第9528天）+ 3（第9527天）+ 4（第9526天）+ 1（第9525天）+ 5（第9524天）;
e.g. 1) Count_sum = 29 because it represents the sum of 3 (Day 9535) + 2 (Day 9534) + 4 (Day 9533) + 1 (Day 9532) + 2 (Day 9531) + 1 (Day 9530) + 2 (Day 9529) + 1 (Day 9528) + 3 (Day 9527) + 4 (Day 9526) + 1 (Day 9525) + 5 (Day 9524);
2）Count_sum = 27，因为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）；
2) Count_sum = 27 because of 3 (Day 9537) + 1 (Day 9536) + 3 (Day 9535) + 2 (Day 9534) + 4 (Day 9533) + 1 (Day 9532) + 2 (Day 9531) + 1 (Day 9530) + 2 Day 9529) + 1 (Day 9528) + 3 (Day 9527) + 4 (Day 9526);
等，依此类推。
我需要做的是向 df 中添加第5列（Episode_ID），每列12天单集的值介于1到21之间（因为 df 有21天是不重复的天）。
What I need to do is to add a 5th column (Episode_ID) to df which groups each 12-day episodes with a unique value from 1 to 21 (because in df there are 21 unique days).
Count_sum近正确地将它们分组，但是可能有2个或更多的12天情节具有相同的Count_sum值，并且在几天之内也可能重叠。
Count_sum nearly group them correctly but there can be 2 or more 12-day episodes with same Count_sum values and that can also overlap within days.
我的真实data.frame包含> 300,000行而且我还想获得一个适用于12天情节的代码（例如 df ），也适用于其他数据。按2、3、4、5、6、7、8，n天分组的帧。
My real data.frame contains >300,000 rows and I would like also to obtain a code that works for 12-day episodes (as df) but also for other data.frames grouped by 2,3,4,5,6,7,8,n days.
这是我对 df （12天的情节）的预期输出：
Here my expected output for df (12-day episodes):
ID Day Count Count_sum Episode_ID
33021 9535 3 29 1
33029 9535 3 29 1
34001 9535 3 29 1
32010 9534 2 29 1
33023 9534 2 29 1
45012 9533 4 29 1
47001 9533 4 29 1
48010 9533 4 29 1
50001 9533 4 29 1
49004 9532 1 29 1
9002 9531 2 29 1
67008 9531 2 29 1
40011 9530 1 29 1
42003 9529 2 29 1
42011 9529 2 29 1
55023 9528 1 29 1
40012 9527 3 29 1
43007 9527 3 29 1
47011 9527 3 29 1
52004 9526 4 29 1
52005 9526 4 29 1
52006 9526 4 29 1
52007 9526 4 29 1
19001 9525 1 29 1
57008 9524 5 29 1
57010 9524 5 29 1
58006 9524 5 29 1
58008 9524 5 29 1
59001 9524 5 29 1
58008 9537 3 27 2
66001 9537 3 27 2
68001 9537 3 27 2
54057 9536 1 27 2
33021 9535 3 27 2
33029 9535 3 27 2
34001 9535 3 27 2
32010 9534 2 27 2
33023 9534 2 27 2
45012 9533 4 27 2
47001 9533 4 27 2
48010 9533 4 27 2
50001 9533 4 27 2
49004 9532 1 27 2
9002 9531 2 27 2
67008 9531 2 27 2
40011 9530 1 27 2
42003 9529 2 27 2
42011 9529 2 27 2
55023 9528 1 27 2
40012 9527 3 27 2
43007 9527 3 27 2
47011 9527 3 27 2
52004 9526 4 27 2
52005 9526 4 27 2
52006 9526 4 27 2
52007 9526 4 27 2
32010 9534 2 27 3
33023 9534 2 27 3
45012 9533 4 27 3
47001 9533 4 27 3
48010 9533 4 27 3
50001 9533 4 27 3
49004 9532 1 27 3
9002 9531 2 27 3
67008 9531 2 27 3
40011 9530 1 27 3
42003 9529 2 27 3
42011 9529 2 27 3
55023 9528 1 27 3
40012 9527 3 27 3
43007 9527 3 27 3
47011 9527 3 27 3
52004 9526 4 27 3
52005 9526 4 27 3
52006 9526 4 27 3
52007 9526 4 27 3
19001 9525 1 27 3
57008 9524 5 27 3
57010 9524 5 27 3
58006 9524 5 27 3
58008 9524 5 27 3
59001 9524 5 27 3
65004 9523 1 27 3
49004 9532 1 26 4
9002 9531 2 26 4
67008 9531 2 26 4
40011 9530 1 26 4
42003 9529 2 26 4
42011 9529 2 26 4
55023 9528 1 26 4
40012 9527 3 26 4
43007 9527 3 26 4
47011 9527 3 26 4
52004 9526 4 26 4
52005 9526 4 26 4
52006 9526 4 26 4
52007 9526 4 26 4
19001 9525 1 26 4
57008 9524 5 26 4
57010 9524 5 26 4
58006 9524 5 26 4
58008 9524 5 26 4
59001 9524 5 26 4
65004 9523 1 26 4
75003 9522 1 26 4
76007 9521 4 26 4
77002 9521 4 26 4
77003 9521 4 26 4
78003 9521 4 26 4
48007 9538 2 25 5
48011 9538 2 25 5
58008 9537 3 25 5
66001 9537 3 25 5
68001 9537 3 25 5
54057 9536 1 25 5
33021 9535 3 25 5
33029 9535 3 25 5
34001 9535 3 25 5
32010 9534 2 25 5
33023 9534 2 25 5
45012 9533 4 25 5
47001 9533 4 25 5
48010 9533 4 25 5
50001 9533 4 25 5
49004 9532 1 25 5
9002 9531 2 25 5
67008 9531 2 25 5
40011 9530 1 25 5
42003 9529 2 25 5
42011 9529 2 25 5
55023 9528 1 25 5
40012 9527 3 25 5
43007 9527 3 25 5
47011 9527 3 25 5
54057 9536 1 25 6
33021 9535 3 25 6
33029 9535 3 25 6
34001 9535 3 25 6
32010 9534 2 25 6
33023 9534 2 25 6
45012 9533 4 25 6
47001 9533 4 25 6
48010 9533 4 25 6
50001 9533 4 25 6
49004 9532 1 25 6
9002 9531 2 25 6
67008 9531 2 25 6
40011 9530 1 25 6
42003 9529 2 25 6
42011 9529 2 25 6
55023 9528 1 25 6
40012 9527 3 25 6
43007 9527 3 25 6
47011 9527 3 25 6
52004 9526 4 25 6
52005 9526 4 25 6
52006 9526 4 25 6
52007 9526 4 25 6
19001 9525 1 25 6
45012 9533 4 25 7
47001 9533 4 25 7
48010 9533 4 25 7
50001 9533 4 25 7
49004 9532 1 25 7
9002 9531 2 25 7
67008 9531 2 25 7
40011 9530 1 25 7
42003 9529 2 25 7
42011 9529 2 25 7
55023 9528 1 25 7
40012 9527 3 25 7
43007 9527 3 25 7
47011 9527 3 25 7
52004 9526 4 25 7
52005 9526 4 25 7
52006 9526 4 25 7
52007 9526 4 25 7
19001 9525 1 25 7
57008 9524 5 25 7
57010 9524 5 25 7
58006 9524 5 25 7
58008 9524 5 25 7
59001 9524 5 25 7
65004 9523 1 25 7
75003 9522 1 25 7
9002 9531 2 25 8
67008 9531 2 25 8
40011 9530 1 25 8
42003 9529 2 25 8
42011 9529 2 25 8
55023 9528 1 25 8
40012 9527 3 25 8
43007 9527 3 25 8
47011 9527 3 25 8
52004 9526 4 25 8
52005 9526 4 25 8
52006 9526 4 25 8
52007 9526 4 25 8
19001 9525 1 25 8
57008 9524 5 25 8
57010 9524 5 25 8
58006 9524 5 25 8
58008 9524 5 25 8
59001 9524 5 25 8
65004 9523 1 25 8
75003 9522 1 25 8
76007 9521 4 25 8
77002 9521 4 25 8
77003 9521 4 25 8
78003 9521 4 25 8
74001 9520 1 25 8
39093 9539 2 24 9
41006 9539 2 24 9
48007 9538 2 24 9
48011 9538 2 24 9
58008 9537 3 24 9
66001 9537 3 24 9
68001 9537 3 24 9
54057 9536 1 24 9
33021 9535 3 24 9
33029 9535 3 24 9
34001 9535 3 24 9
32010 9534 2 24 9
33023 9534 2 24 9
45012 9533 4 24 9
47001 9533 4 24 9
48010 9533 4 24 9
50001 9533 4 24 9
49004 9532 1 24 9
9002 9531 2 24 9
67008 9531 2 24 9
40011 9530 1 24 9
42003 9529 2 24 9
42011 9529 2 24 9
55023 9528 1 24 9
40011 9530 1 24 10
42003 9529 2 24 10
42011 9529 2 24 10
55023 9528 1 24 10
40012 9527 3 24 10
43007 9527 3 24 10
47011 9527 3 24 10
52004 9526 4 24 10
52005 9526 4 24 10
52006 9526 4 24 10
52007 9526 4 24 10
19001 9525 1 24 10
57008 9524 5 24 10
57010 9524 5 24 10
58006 9524 5 24 10
58008 9524 5 24 10
59001 9524 5 24 10
65004 9523 1 24 10
75003 9522 1 24 10
76007 9521 4 24 10
77002 9521 4 24 10
77003 9521 4 24 10
78003 9521 4 24 10
74001 9520 1 24 10
42003 9529 2 24 11
42011 9529 2 24 11
55023 9528 1 24 11
40012 9527 3 24 11
43007 9527 3 24 11
47011 9527 3 24 11
52004 9526 4 24 11
52005 9526 4 24 11
52006 9526 4 24 11
52007 9526 4 24 11
19001 9525 1 24 11
57008 9524 5 24 11
57010 9524 5 24 11
58006 9524 5 24 11
58008 9524 5 24 11
59001 9524 5 24 11
65004 9523 1 24 11
75003 9522 1 24 11
76007 9521 4 24 11
77002 9521 4 24 11
77003 9521 4 24 11
78003 9521 4 24 11
74001 9520 1 24 11
33021 9518 1 24 11
55023 9528 1 22 12
40012 9527 3 22 12
43007 9527 3 22 12
47011 9527 3 22 12
52004 9526 4 22 12
52005 9526 4 22 12
52006 9526 4 22 12
52007 9526 4 22 12
19001 9525 1 22 12
57008 9524 5 22 12
57010 9524 5 22 12
58006 9524 5 22 12
58008 9524 5 22 12
59001 9524 5 22 12
65004 9523 1 22 12
75003 9522 1 22 12
76007 9521 4 22 12
77002 9521 4 22 12
77003 9521 4 22 12
78003 9521 4 22 12
74001 9520 1 22 12
33021 9518 1 22 12
40012 9527 3 21 13
43007 9527 3 21 13
47011 9527 3 21 13
52004 9526 4 21 13
52005 9526 4 21 13
52006 9526 4 21 13
52007 9526 4 21 13
19001 9525 1 21 13
57008 9524 5 21 13
57010 9524 5 21 13
58006 9524 5 21 13
58008 9524 5 21 13
59001 9524 5 21 13
65004 9523 1 21 13
75003 9522 1 21 13
76007 9521 4 21 13
77002 9521 4 21 13
77003 9521 4 21 13
78003 9521 4 21 13
74001 9520 1 21 13
33021 9518 1 21 13
52004 9526 4 18 14
52005 9526 4 18 14
52006 9526 4 18 14
52007 9526 4 18 14
19001 9525 1 18 14
57008 9524 5 18 14
57010 9524 5 18 14
58006 9524 5 18 14
58008 9524 5 18 14
59001 9524 5 18 14
65004 9523 1 18 14
75003 9522 1 18 14
76007 9521 4 18 14
77002 9521 4 18 14
77003 9521 4 18 14
78003 9521 4 18 14
74001 9520 1 18 14
33021 9518 1 18 14
19001 9525 1 14 15
57008 9524 5 14 15
57010 9524 5 14 15
58006 9524 5 14 15
58008 9524 5 14 15
59001 9524 5 14 15
65004 9523 1 14 15
75003 9522 1 14 15
76007 9521 4 14 15
77002 9521 4 14 15
77003 9521 4 14 15
78003 9521 4 14 15
74001 9520 1 14 15
33021 9518 1 14 15
57008 9524 5 13 16
57010 9524 5 13 16
58006 9524 5 13 16
58008 9524 5 13 16
59001 9524 5 13 16
65004 9523 1 13 16
75003 9522 1 13 16
76007 9521 4 13 16
77002 9521 4 13 16
77003 9521 4 13 16
78003 9521 4 13 16
74001 9520 1 13 16
33021 9518 1 13 16
65004 9523 1 8 17
75003 9522 1 8 17
76007 9521 4 8 17
77002 9521 4 8 17
77003 9521 4 8 17
78003 9521 4 8 17
74001 9520 1 8 17
33021 9518 1 8 17
75003 9522 1 7 18
76007 9521 4 7 18
77002 9521 4 7 18
77003 9521 4 7 18
78003 9521 4 7 18
74001 9520 1 7 18
33021 9518 1 7 18
76007 9521 4 6 19
77002 9521 4 6 19
77003 9521 4 6 19
78003 9521 4 6 19
74001 9520 1 6 19
33021 9518 1 6 19
74001 9520 1 2 20
33021 9518 1 2 20
33021 9518 1 1 21
如果看到输出，则在 Count_sum = 27内有2个不同的情节，对于 Count_sum = 25， Count_sum = 24等2集。
If you see the output, within Count_sum = 27 there are 2 distinct episodes, 4 episodes for Count_sum = 25, 2 episodes for Count_sum = 24, etc..
Episode_ID列从1到21其中1是具有最大Count_group的情节，而当有2个或更多情节时是同一Count_group，需要按Day减少= TRUE进行排序。
The Episode_ID column starts from 1 to 21 where 1 is the episode with largest Count_group and when there are 2 or more episode with same Count_group they need to be ordered by Day decreasing = TRUE.
这是我从（更新）基于两列向data.frame添加索引列，但不起作用：
Here what I tried from (Update) Add index column to data.frame based on two columns but does not work:
1）
df$Episode_ID <- cumsum(c(1,abs(diff(df$Day)) > 1) + c(0,diff(df$Count_sum) != 0) > 0)
2）
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]
有任何建议吗？
推荐答案
我必须承认我还没有完全理解所有细节，尤其是没有明确定义 episode ，所提供的数据在我看来与 Count_sum 是计算出来的。
I must admit I haven't fully understood all the details, in particular, there is no explicit definition of episode and the provided data appear to me as not fully matching the description of how Count_sum is computed.
不过，我能够重现预期的结果。
Nevertheless, I was able to reproduce the expected results.
建议的解决方案基于以下观察结果： Day 由许多单调递减的序列组成（大概是OP用 episodes 表示的意思）。因此，任务是识别新序列开始处的中断，增加序列计数器并使用该序列ID为该序列的所有后续行编号。
The proposed solution is based on the observation that Day is consisting of a number of monotonically decreasing sequences (which presumably is what the OP means by episodes). So, the task is to identify the breaks where a new sequence starts, to advance the sequence counter, and to number all subsequent rows of that sequence with that sequence id.
这是通过
library(data.table) # CRAN version 1.10.4 used
setDT(expected)[, Sequence_ID := cumsum(Day - shift(Day, fill = -1L) > 0)]
请注意，此处由OP提供的第二个数据集用于证明计算与预期结果一致。例如，第一次休息发生在第29行和第30行之间：
Note that here the second data set provided by the OP is used to demonstrate that the computations are in line with the expected results. For instance, the first break happens between rows 29 and 30:
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
表达式已识别出从9524天到9537天的跳跃。
The expression has recognized the jump from day 9524 to 9537.
不幸的是，结尾处存在差异：
Unfortunately, there is a discrepancy at the end:
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
OP具有将最后一行分配给新剧集，尽管日子依旧单调递减。如果这仅是提供的数据中的错误，请完成。
The OP has assigned the last row to a new episode although the days are still in monotonically decreasing order. If this is just an error in the provided data, we are done.
如果这是故意的，则必须在使用<$的情节编号中考虑 Count_sum 的变化c $ c> data.table 方便的 rleid（）函数：
If this is intentional, then changes in Count_sum have to be considered in the numbering of episodes by using data.table's handy rleid() function:
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
这也可以写得更简洁作为单线
This can also be written more concisely as a one-liner
expected[, new_Episode_ID := rleid(cumsum(Day - shift(Day, fill = -1L) > 0), Count_sum)]
< h3>数据
Data
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,
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, 27L,
27L, 27L, 27L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
26L, 26L, 26L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 1L),
Episode_ID = 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,
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, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L,
20L, 20L, 21L)), .Names = c("ID", "Day", "Count", "Count_sum",
"Episode_ID"), row.names = c(NA, -395L), class = "data.frame")
这篇关于根据按特定日期长度的情节分组的行将新列添加到data.frame的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
