如何将一个和操作员嵌入数据中。表函数？
假设我们从立即下方的代码生成的此数据框开始：
> data1
ID Period Values_1 Values_2 State
1 1 1 5 5 X0
2 1 2 0 2 X1
3 1 3 0 0 X2
4 1 4 0 12 X1
5 2 1 1 2 X0
6 2 2 -1 0 X2
7 2 3 0 1 X0
8 2 4 0 0 X0
9 3 1 0 0 X2
10 3 2 0 0 X1
11 3 3 0 0 X9
12 3 4 0 2 X3
13 4 1 1 4 X2
14 4 2 2 5 X1
15 4 3 3 6 X9
16 4 4 0 0 X3
data1 <-
data.frame(
ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4),
Period = c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4),
Values_1 = c(5, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0),
Values_2 = c(5, 2, 0, 12, 2, 0, 1, 0, 0, 0, 0, 2, 4, 5, 6, 0),
State = c("X0","X1","X2","X1","X0","X2","X0","X0", "X2","X1","X9","X3", "X2","X1","X9","X3")
)
我一直在使用此数据
setDT(data1)[, State1 := ifelse(rev(cumsum(rev(Values_1 + Values_2))), State, "END"), ID]
。结果：
> data1
ID Period Values_1 Values_2 State State1
1: 1 1 5 5 X0 X0
2: 1 2 0 2 X1 X1
3: 1 3 0 0 X2 X2
4: 1 4 0 12 X1 X1
5: 2 1 1 2 X0 X0
6: 2 2 -1 0 X2 END
7: 2 3 0 1 X0 X0
8: 2 4 0 0 X0 END
9: 3 1 0 0 X2 X2
10: 3 2 0 0 X1 X1
11: 3 3 0 0 X9 X9
12: 3 4 0 2 X3 X3
13: 4 1 1 4 X2 X2
14: 4 2 2 5 X1 X1
15: 4 3 3 6 X9 X9
16: 4 4 0 0 X3 END
当我想为ID = 2提供这些结果时：为此
> data1
ID Period Values_1 Values_2 State State1
1: 1 1 5 5 X0 X0
2: 1 2 0 2 X1 X1
3: 1 3 0 0 X2 X2
4: 1 4 0 12 X1 X1
5: 2 1 1 2 X0 X0
6: 2 2 -1 0 X2 X2
7: 2 3 0 1 X0 X0
8: 2 4 0 0 X0 END
9: 3 1 0 0 X2 X2
10: 3 2 0 0 X1 X1
11: 3 3 0 0 X9 X9
12: 3 4 0 2 X3 X3
13: 4 1 1 4 X2 X2
14: 4 2 2 5 X1 X1
15: 4 3 3 6 X9 X9
16: 4 4 0 0 X3 END
，我需要更改数据。值_1和值_2的ID的未来周期值（单独计算）= 0，然后在其所有未来周期中标记该ID的状态_1。如何在数据中完成。
setDT(data1)[, State1 := ifelse(rev(cumsum(rev(Values_1))) & rev(cumsum(rev(Values_2))), State, "END"), ID]
这与相关帖子链接如何使用dplyr或data。可通过数据子集执行浏览的计算？
Suppose we start with this dataframe generated by the code immediately beneath:
> data1
ID Period Values_1 Values_2 State
1 1 1 5 5 X0
2 1 2 0 2 X1
3 1 3 0 0 X2
4 1 4 0 12 X1
5 2 1 1 2 X0
6 2 2 -1 0 X2
7 2 3 0 1 X0
8 2 4 0 0 X0
9 3 1 0 0 X2
10 3 2 0 0 X1
11 3 3 0 0 X9
12 3 4 0 2 X3
13 4 1 1 4 X2
14 4 2 2 5 X1
15 4 3 3 6 X9
16 4 4 0 0 X3
data1 <-
data.frame(
ID = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4),
Period = c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4),
Values_1 = c(5, 0, 0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0),
Values_2 = c(5, 2, 0, 12, 2, 0, 1, 0, 0, 0, 0, 2, 4, 5, 6, 0),
State = c("X0","X1","X2","X1","X0","X2","X0","X0", "X2","X1","X9","X3", "X2","X1","X9","X3")
)
I've been using this data.table code to flag each ID with an "END" in State_1 when it no longer generates values in its future periods:
setDT(data1)[, State1 := ifelse(rev(cumsum(rev(Values_1 + Values_2))), State, "END"), ID]
The above code gives these results:
> data1
ID Period Values_1 Values_2 State State1
1: 1 1 5 5 X0 X0
2: 1 2 0 2 X1 X1
3: 1 3 0 0 X2 X2
4: 1 4 0 12 X1 X1
5: 2 1 1 2 X0 X0
6: 2 2 -1 0 X2 END
7: 2 3 0 1 X0 X0
8: 2 4 0 0 X0 END
9: 3 1 0 0 X2 X2
10: 3 2 0 0 X1 X1
11: 3 3 0 0 X9 X9
12: 3 4 0 2 X3 X3
13: 4 1 1 4 X2 X2
14: 4 2 2 5 X1 X1
15: 4 3 3 6 X9 X9
16: 4 4 0 0 X3 END
When I would like to give these results for ID = 2 instead:
> data1
ID Period Values_1 Values_2 State State1
1: 1 1 5 5 X0 X0
2: 1 2 0 2 X1 X1
3: 1 3 0 0 X2 X2
4: 1 4 0 12 X1 X1
5: 2 1 1 2 X0 X0
6: 2 2 -1 0 X2 X2
7: 2 3 0 1 X0 X0
8: 2 4 0 0 X0 END
9: 3 1 0 0 X2 X2
10: 3 2 0 0 X1 X1
11: 3 3 0 0 X9 X9
12: 3 4 0 2 X3 X3
13: 4 1 1 4 X2 X2
14: 4 2 2 5 X1 X1
15: 4 3 3 6 X9 X9
16: 4 4 0 0 X3 END
In order to do this, I need to change the data.table code to in effect something like the below (it doesn't work), where if BOTH the future period values for an ID of Values_1 AND Values_2 (separately calculated) = 0, then the State_1 for that ID is flagged END for all of its future periods. How can this be done in data.table?
setDT(data1)[, State1 := ifelse(rev(cumsum(rev(Values_1))) & rev(cumsum(rev(Values_2))), State, "END"), ID]
This is linked with related post How to use dplyr or data.table to perform look-ahead calculations by groups of data subsets?
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也许这样的东西：
输出：
Perhaps something like this:
Output:
链接帖子中的答案似乎是假设
values_1和values_2的非负值。如果有负，请将abs插入data.table表达式：The answer in the linked post appears to be assuming non-negative values for
Values_1andValues_2. If there are negatives, insert anabsinto thedata.tableexpression: