Losses
[[1]]
[[1]]$`1`
YearID Category Loss
1 2 US HU 826.79527
2 4 US HU 357.37926
3 5 US HU 148.71542
4 5 US HU 654.44473
5 8 US HU 81.74428
6 8 US HU 60.27192
7 8 US HU 63.59582
8 10 US HU 778.97650
9 10 US HU 1498.90801
10 11 US HU 66.72708
11 11 US HU 123.25430
12 11 US HU 70.87750
13 11 US HU 272.57985
14 12 US HU 77.14205
15 13 US HU 305.00484
16 16 US HU 577.17381
17 16 US HU 137.12441
18 20 US HU 53.29270
19 20 US HU 123.86174
[[1]]$`2`
YearID Category Loss
1 2 US EQ 2.6895238
2 9 US EQ 42.7690970
3 9 US EQ 10.7816904
4 11 US EQ 2.3136544
5 12 US EQ 0.4897316
6 13 US EQ 1.0560520
7 15 US EQ 111.0338641
8 17 US EQ 123.7145577
9 19 US EQ 8.1518931
10 19 US EQ 0.3099158
[[1]]$`3`
YearID Category Loss
1 2 JP EQ 0.2920082
2 5 JP EQ 64.6424343
3 7 JP EQ 0.6097201
4 7 JP EQ 6.7342614
5 10 JP EQ 2.9948139
6 10 JP EQ 12.8309552
7 15 JP EQ 0.2373154
8 16 JP EQ 1.6869774
9 20 JP EQ 1.3283421
[[2]]
[[2]]$`1`
YearID Category Loss
1 15 US HU 107.63285
2 16 US HU 192.89825
3 19 US HU 215.81496
4 2 US HU 826.79527
5 4 US HU 357.37926
6 5 US HU 148.71542
7 5 US HU 654.44473
8 8 US HU 81.74428
9 8 US HU 60.27192
10 8 US HU 63.59582
11 10 US HU 778.97650
12 10 US HU 1498.90801
13 11 US HU 66.72708
14 11 US HU 123.25430
15 11 US HU 70.87750
16 11 US HU 272.57985
17 12 US HU 77.14205
18 13 US HU 305.00484
19 16 US HU 577.17381
20 16 US HU 137.12441
21 20 US HU 53.29270
22 20 US HU 123.86174
[[2]]$`2`
YearID Category Loss
1 5 US EQ 0.6082396
2 10 US EQ 159.8625057
3 2 US EQ 2.6895238
4 9 US EQ 42.7690970
5 9 US EQ 10.7816904
6 11 US EQ 2.3136544
7 12 US EQ 0.4897316
8 13 US EQ 1.0560520
9 15 US EQ 111.0338641
10 17 US EQ 123.7145577
11 19 US EQ 8.1518931
12 19 US EQ 0.3099158
[[2]]$`3`
YearID Category Loss
1 7 JP EQ 0.3618008
2 12 JP EQ 11.0977981
3 2 JP EQ 0.2920082
4 5 JP EQ 64.6424343
5 7 JP EQ 0.6097201
6 7 JP EQ 6.7342614
7 10 JP EQ 2.9948139
8 10 JP EQ 12.8309552
9 15 JP EQ 0.2373154
10 16 JP EQ 1.6869774
11 20 JP EQ 1.3283421
[[3]]
[[3]]$`1`
YearID Category Loss
1 4 US HU 72.52669
2 8 US HU 270.71636
3 10 US HU 132.25629
4 10 US HU 49.67546
5 11 US HU 134.83450
6 12 US HU 116.10639
7 18 US HU 409.75564
8 19 US HU 268.86671
9 20 US HU 576.44159
10 15 US HU 107.63285
11 16 US HU 192.89825
12 19 US HU 215.81496
13 2 US HU 826.79527
14 4 US HU 357.37926
15 5 US HU 148.71542
16 5 US HU 654.44473
17 8 US HU 81.74428
18 8 US HU 60.27192
19 8 US HU 63.59582
20 10 US HU 778.97650
21 10 US HU 1498.90801
22 11 US HU 66.72708
23 11 US HU 123.25430
24 11 US HU 70.87750
25 11 US HU 272.57985
26 12 US HU 77.14205
27 13 US HU 305.00484
28 16 US HU 577.17381
29 16 US HU 137.12441
30 20 US HU 53.29270
31 20 US HU 123.86174
[[3]]$`2`
YearID Category Loss
1 6 US EQ 3.6683023
2 10 US EQ 4.6943200
3 11 US EQ 2.6239743
4 13 US EQ 1.3989827
5 13 US EQ 0.2399834
6 19 US EQ 5.8900987
7 5 US EQ 0.6082396
8 10 US EQ 159.8625057
9 2 US EQ 2.6895238
10 9 US EQ 42.7690970
11 9 US EQ 10.7816904
12 11 US EQ 2.3136544
13 12 US EQ 0.4897316
14 13 US EQ 1.0560520
15 15 US EQ 111.0338641
16 17 US EQ 123.7145577
17 19 US EQ 8.1518931
18 19 US EQ 0.3099158
[[3]]$`3`
YearID Category Loss
1 1 JP EQ 0.9199341
2 2 JP EQ 2.7294618
3 7 JP EQ 224.4266519
4 11 JP EQ 0.3948219
5 14 JP EQ 0.7592534
6 15 JP EQ 1.2838320
7 7 JP EQ 0.3618008
8 12 JP EQ 11.0977981
9 2 JP EQ 0.2920082
10 5 JP EQ 64.6424343
11 7 JP EQ 0.6097201
12 7 JP EQ 6.7342614
13 10 JP EQ 2.9948139
14 10 JP EQ 12.8309552
15 15 JP EQ 0.2373154
16 16 JP EQ 1.6869774
17 20 JP EQ 1.3283421
-
list(structure(list(`1` = structure(list(YearID = c(2, 4, 5,
5, 8, 8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16, 16, 20, 20),
Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "US HU"),
Loss = c(826.795273886473, 357.379257145211, 148.715422723442,
654.444727907641, 81.7442792092622, 60.2719188870034, 63.5958204662617,
778.976495232166, 1498.90801288032, 66.7270779301846, 123.254304652346,
70.8774963289464, 272.579854338023, 77.1420493089197, 305.004836528708,
577.173809957773, 137.124412010961, 53.2926961871035, 123.861739643341
)), .Names = c("YearID", "Category", "Loss"), row.names = c(NA,
-19L), class = "data.frame"), `2` = structure(list(YearID = c(2,
9, 9, 11, 12, 13, 15, 17, 19, 19), Category = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "US EQ"),
Loss = c(2.68952379748490, 42.7690970302631, 10.7816904147645,
2.31365444951509, 0.489731609222403, 1.05605200325909, 111.033864050878,
123.714557672790, 8.15189312989583, 0.309915764784634)), .Names = c("YearID",
"Category", "Loss"), row.names = c(NA, -10L), class = "data.frame"),
`3` = structure(list(YearID = c(2, 5, 7, 7, 10, 10, 15, 16,
20), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), class = "factor", .Label = "JP EQ"), Loss = c(0.292008160090971,
64.6424342762608, 0.609720084708476, 6.73426138743874, 2.99481387294425,
12.8309551549701, 0.237315428236897, 1.68697744368254, 1.32834211322205
)), .Names = c("YearID", "Category", "Loss"), row.names = c(NA,
-9L), class = "data.frame")), .Names = c("1", "2", "3")),
structure(list(`1` = structure(list(YearID = c(15, 16, 19,
2, 4, 5, 5, 8, 8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16,
16, 20, 20), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = "US HU", class = "factor"), Loss = c(107.632845241511,
192.898254968436, 215.814957575895, 826.795273886473, 357.379257145211,
148.715422723442, 654.444727907641, 81.7442792092622, 60.2719188870034,
63.5958204662617, 778.976495232166, 1498.90801288032, 66.7270779301846,
123.254304652346, 70.8774963289464, 272.579854338023, 77.1420493089197,
305.004836528708, 577.173809957773, 137.124412010961, 53.2926961871035,
123.861739643341)), .Names = c("YearID", "Category", "Loss"
), row.names = c(NA, 22L), class = "data.frame"), `2` = structure(list(
YearID = c(5, 10, 2, 9, 9, 11, 12, 13, 15, 17, 19, 19
), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = "US EQ", class = "factor"),
Loss = c(0.60823959378077, 159.862505662270, 2.68952379748490,
42.7690970302631, 10.7816904147645, 2.31365444951509,
0.489731609222403, 1.05605200325909, 111.033864050878,
123.714557672790, 8.15189312989583, 0.309915764784634
)), .Names = c("YearID", "Category", "Loss"), row.names = c(NA,
12L), class = "data.frame"), `3` = structure(list(YearID = c(7,
12, 2, 5, 7, 7, 10, 10, 15, 16, 20), Category = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "JP EQ", class = "factor"),
Loss = c(0.36180083325003, 11.0977980698104, 0.292008160090971,
64.6424342762608, 0.609720084708476, 6.73426138743874,
2.99481387294425, 12.8309551549701, 0.237315428236897,
1.68697744368254, 1.32834211322205)), .Names = c("YearID",
"Category", "Loss"), row.names = c(NA, 11L), class = "data.frame")), .Names = c("1",
"2", "3")), structure(list(`1` = structure(list(YearID = c(4,
8, 10, 10, 11, 12, 18, 19, 20, 15, 16, 19, 2, 4, 5, 5, 8,
8, 8, 10, 10, 11, 11, 11, 11, 12, 13, 16, 16, 20, 20), Category = structure(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, 1L, 1L
), .Label = "US HU", class = "factor"), Loss = c(72.5266949028921,
270.716360695826, 132.256285323478, 49.6754599765934, 134.834496124065,
116.106394912601, 409.755636985602, 268.866707090942, 576.441585784339,
107.632845241511, 192.898254968436, 215.814957575895, 826.795273886473,
357.379257145211, 148.715422723442, 654.444727907641, 81.7442792092622,
60.2719188870034, 63.5958204662617, 778.976495232166, 1498.90801288032,
66.7270779301846, 123.254304652346, 70.8774963289464, 272.579854338023,
77.1420493089197, 305.004836528708, 577.173809957773, 137.124412010961,
53.2926961871035, 123.861739643341)), .Names = c("YearID",
"Category", "Loss"), row.names = c(NA, 31L), class = "data.frame"),
`2` = structure(list(YearID = c(6, 10, 11, 13, 13, 19,
5, 10, 2, 9, 9, 11, 12, 13, 15, 17, 19, 19), Category = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "US EQ", class = "factor"), Loss = c(3.66830233362805,
4.69432002882382, 2.62397430771946, 1.39898268940725,
0.239983432670597, 5.89009873171407, 0.60823959378077,
159.862505662270, 2.68952379748490, 42.7690970302631,
10.7816904147645, 2.31365444951509, 0.489731609222403,
1.05605200325909, 111.033864050878, 123.714557672790,
8.15189312989583, 0.309915764784634)), .Names = c("YearID",
"Category", "Loss"), row.names = c(NA, 18L), class = "data.frame"),
`3` = structure(list(YearID = c(1, 2, 7, 11, 14, 15,
7, 12, 2, 5, 7, 7, 10, 10, 15, 16, 20), Category = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = "JP EQ", class = "factor"), Loss = c(0.91993405093065,
2.72946175093358, 224.426651937984, 0.394821850501270,
0.75925338029943, 1.28383204511935, 0.36180083325003,
11.0977980698104, 0.292008160090971, 64.6424342762608,
0.609720084708476, 6.73426138743874, 2.99481387294425,
12.8309551549701, 0.237315428236897, 1.68697744368254,
1.32834211322205)), .Names = c("YearID", "Category",
"Loss"), row.names = c(NA, 17L), class = "data.frame")), .Names = c("1",
"2", "3")))
-
InputList = list(c(1.5, 1.2), c(0.9, 0.8), c(1, 1))
InputList的长度始终与Losses的长度相同。
即会有相同的没有。 InputList中的向量作为Losses的长度。
我必须首先根据向量的第一个元素按递增顺序排列InputList
即。
InputList = list(c(0.9, 0.8), c(1, 1),c(1.5, 1.2))
现在，我必须将InputList的第一个向量的第二个元素相乘，即这里它与第一个损失列表中所有数据帧的损失列相加，即损失[[1]]
并且类似地将1与损失[[2]]
中所有数据帧的Loss列相乘我希望将结果作为类似于原始列表的列表丢失，只是每个数据框的损失列都相应地膨胀或收缩。
答案 0 :(得分：2)
Q1：“我必须首先根据向量的第一个元素按递增顺序排列InputList，即
newInput <- InputList[order(sapply(InputList, "[[", 1))]
newInput
[[1]]
[1] 0.9 0.8
[[2]]
[1] 1 1
[[3]]
[1] 1.5 1.2
Q2：“现在，我必须将InputList的第一个向量的第二个元素相乘，即在这里它与第一个损失列表中的所有数据帧的损失列为0.8，即损失[[1]]，并且类似将1与损失[[2]]“
中所有数据帧的Loss列相乘（我认为这意味着损失[[1]]中的所有三个数据帧都需要将其损失列乘以0.9。）
这将返回一个3 x 3的数据帧矩阵，其损耗列乘以所需的因子：
ddapply <- function(dfrmlist,fac){
lapply(dfrmlist,
function(dfrm){ dfrm[, "Loss"] <- dfrm[ ,"Loss"]*fac; return(dfrm)})}
mapply( ddapply, Losses, sapply(newInput, "[[", 2) )
第一组数据帧值位于第一列，而不是第一行，因为R矩阵中元素的列主要排序。所以这是损失的第一个元素中的第二个data.frame：
> mapply( ddapply, Losses, sapply(newInput, "[[", 2) )[2,1]
$`2`
YearID Category Loss
1 2 US EQ 2.1516190
2 9 US EQ 34.2152776
3 9 US EQ 8.6253523
4 11 US EQ 1.8509236
5 12 US EQ 0.3917853
6 13 US EQ 0.8448416
7 15 US EQ 88.8270912
8 17 US EQ 98.9716461
9 19 US EQ 6.5215145
10 19 US EQ 0.2479326