计算宽格式 table 行数据的权重
Calculate weights of data table rows of wide-format
我有一个非常庞大的宽格式数据table，这里只给你一个简短的例子：
library(maditr)
library(reshape2)
library(data.table)
dt.test <- data.table(time = as.Date('2009-01-01') + 0:9,
s1 = rnorm(10, 5, 1),
s2 = rnorm(10, 10, 2),
s3 = rnorm(10, 20, 4))
dt.test <- dt.test %>%
reshape2::melt(id.vars = "time", variable.name = "stock", value.name = "price") %>%
maditr::dcast(stock ~ time, fun.aggregate = list, value.var = "price")
> dt.test
# stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09 2009-01-10
#1: s1 5.139208 3.879785 6.099094 5.304787 4.732387 4.77054 4.877472 4.830193 5.468288 3.121548
#2: s2 11.70447 7.360979 10.5366 7.525067 9.770211 14.13924 8.356996 10.45796 9.749032 5.034887
#3: s3 28.17668 10.22208 29.17786 24.41746 17.87546 24.67163 13.13525 14.9941 18.84145 22.07169
现在我需要计算每一行的权重，这意味着：每个行条目除以整行的平均值。
现在两种结束格式对我来说都是可能的。要么权重相同 table，要么我们创建一个新的权重 table dt.weights 并将权重保存在那里。
版本 1：将它们保存在同一个 table dt.test
在行 s1 之后保存 s1 的权重行，称为 s1W，在行 s2 之后保存权重行 s2W 和在我的数据 table.
中 s3 之后的权重行 s3W
版本 2：将它们保存在新的 table dt.weights
这看起来应该和以前一样（宽幅面），但用权重替换库存：
# dt.weights
# stock_w 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09 2009-01-10
#1: s1 5.139208 3.879785 6.099094 5.304787 4.732387 4.77054 4.877472 4.830193 5.468288 3.121548
#2: s2 11.70447 7.360979 10.5366 7.525067 9.770211 14.13924 8.356996 10.45796 9.749032 5.034887
#3: s3 28.17668 10.22208 29.17786 24.41746 17.87546 24.67163 13.13525 14.9941 18.84145 22.07169
这怎么行？
dt.test 和 dt.weights 的宽格式不应更改。
编辑： 我的数据的一小部分：
structure(list(GPNRPlan = c(630882L, 773297L, 830234L), `2018-01-01-00` = list(
101.59, 211.02, 27.91), `2018-01-01-01` = list(98.32, 209.58,
20.98), `2018-01-01-02` = list(95.51, 205.74, 14.62), `2018-01-01-03` = list(
93.28, 210.62, 13.96), `2018-01-01-04` = list(93.03, 219.44,
13.39), `2018-01-01-05` = list(101.62, 269.02, 11.08), `2018-01-01-06` = list(
114.8, 275.1, 10.94), `2018-01-01-07` = list(114.08, 285.74,
11.17), `2018-01-01-08` = list(105.71, 299.54, 10.6), `2018-01-01-09` = list(
107.51, 316.7, 10.84), `2018-01-01-10` = list(112.17, 298.06,
10.85), `2018-01-01-11` = list(120.32, 259.44, 13.08), `2018-01-01-12` = list(
119.75, 255.66, 29.68), `2018-01-01-13` = list(115.29, 284.08,
51.07), `2018-01-01-14` = list(117.77, 249.38, 50.02), `2018-01-01-15` = list(
121.11, 266.22, 51.51), `2018-01-01-16` = list(131.71, 258.14,
54.03), `2018-01-01-17` = list(147.68, 260.28, 60.09), `2018-01-01-18` = list(
157.38, 258.74, 60.94), `2018-01-01-19` = list(161.6, 227.96,
64.69), `2018-01-01-20` = list(154, 214.5, 52.98), `2018-01-01-21` = list(
139.59, 218.96, 46.26), `2018-01-01-22` = list(121.03, 214.46,
31.93), `2018-01-01-23` = list(104.83, 211.48, 21.57), `2018-01-02-00` = list(
104.33, 208.66, 18.05), `2018-01-02-01` = list(96.83, 209.98,
13.68), `2018-01-02-02` = list(94.7, 214.64, 13.49), `2018-01-02-03` = list(
93.8, 211.1, 10.61), `2018-01-02-04` = list(93.73, 223.18,
10.56), `2018-01-02-05` = list(103.18, 290.62, 10.33), `2018-01-02-06` = list(
132.27, 302.42, 11.02), `2018-01-02-07` = list(345.6, 409.74,
14.39), `2018-01-02-08` = list(564.61, 402.22, 15.35), `2018-01-02-09` = list(
619.72, 426.02, 36.77), `2018-01-02-10` = list(626.2, 393.6,
43.88), `2018-01-02-11` = list(619.47, 383.42, 41.75), `2018-01-02-12` = list(
577.24, 338.1, 47.78), `2018-01-02-13` = list(495.96, 367.32,
46.86), `2018-01-02-14` = list(494.98, 349.86, 50.1), `2018-01-02-15` = list(
538.48, 339.86, 50.87), `2018-01-02-16` = list(524.3, 315.06,
53.49), `2018-01-02-17` = list(475.18, 307, 53.45), `2018-01-02-18` = list(
366, 294.9, 55.2), `2018-01-02-19` = list(225.15, 243.6,
57.24), `2018-01-02-20` = list(182.21, 234.46, 51.21), `2018-01-02-21` = list(
160.3, 234, 33.39), `2018-01-02-22` = list(136.96, 234.78,
24.73), `2018-01-02-23` = list(117.38, 230.74, 21.19), `2018-01-03-00` = list(
105.54, 231.48, 14.04), `2018-01-03-01` = list(100.61, 237.72,
15.86), `2018-01-03-02` = list(97.85, 230.2, 13.49), `2018-01-03-03` = list(
96.75, 240.52, 10.18), `2018-01-03-04` = list(97, 235.82,
10.28), `2018-01-03-05` = list(105.85, 301.14, 9.6), `2018-01-03-06` = list(
131.66, 317.92, 11.66), `2018-01-03-07` = list(342.72, 401.08,
13.97), `2018-01-03-08` = list(563.71, 421.58, 19.96), `2018-01-03-09` = list(
617.2, 417.5, 39.21), `2018-01-03-10` = list(626.69, 388.46,
37.58), `2018-01-03-11` = list(624.09, 379.5, 41.75), `2018-01-03-12` = list(
581.89, 353.32, 46.09), `2018-01-03-13` = list(500.86, 253.34,
45.87), `2018-01-03-14` = list(498.61, 317.54, 42.89), `2018-01-03-15` = list(
539.27, 351.52, 50.02), `2018-01-03-16` = list(527.84, 320.2,
46.89), `2018-01-03-17` = list(477.11, 296.58, 47.37), `2018-01-03-18` = list(
360.48, 287.28, 49.22), `2018-01-03-19` = list(224.44, 247.82,
52.01), `2018-01-03-20` = list(183.9, 242.1, 56.8)), row.names = c(NA,
-3L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001ff1d5e1ef0>, sorted = "GPNRPlan")
我得到的解决方案（每行两次权重）：
它不是立竿见影的，但似乎有效：
第一步：构建权重 table。
#Compute row means
aux <- rowMeans(dt.test[, lapply(.SD, unlist), .SDcols = 2:ncol(dt.test)])
#Compute weights and add a column with "row names"
cbind(stock = paste0(dt.test$stock, "W"),
dt.test[, lapply(.SD, function(x) unlist(x) / aux), .SDcols = 2:ncol(dt.test)]
)
stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09
1: s1W 0.9517776 0.9545703 1.041051 1.191171 1.32749 0.884822 0.8491691 1.15274 0.7940578
2: s2W 1.126405 0.838604 0.9497316 1.033589 1.160884 0.8274315 1.071979 0.9675088 1.267201
3: s3W 1.235231 0.7507189 1.044507 1.244315 0.9601964 0.909451 1.101159 0.9570893 0.8158607
2009-01-10
1: 0.853152
2: 0.7566673
3: 0.9814708
然后，如果您愿意，将所有内容绑定在一起排序：
aux <- rowMeans(dt.test[, lapply(.SD, unlist), .SDcols = 2:ncol(dt.test)])
rbind(dt.test,
cbind(stock = paste0(dt.test$stock, "W"),
dt.test[, lapply(.SD, function(x) unlist(x) / aux), .SDcols = 2:ncol(dt.test)]
)
)[order(as.character(stock))]
stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09
1: s1 4.770632 4.78463 5.2181 5.970551 6.65383 4.435028 4.256324 5.777923 3.980087
2: s1W 0.9517776 0.9545703 1.041051 1.191171 1.32749 0.884822 0.8491691 1.15274 0.7940578
3: s2 11.36418 8.460586 9.58174 10.42776 11.71203 8.347867 10.81508 9.761092 12.78465
4: s2W 1.126405 0.838604 0.9497316 1.033589 1.160884 0.8274315 1.071979 0.9675088 1.267201
5: s3 25.03878 15.21747 21.17271 25.22293 19.46369 18.43505 22.32109 19.40071 16.53793
6: s3W 1.235231 0.7507189 1.044507 1.244315 0.9601964 0.909451 1.101159 0.9570893 0.8158607
2009-01-10
1: 4.276287
2: 0.853152
3: 7.633934
4: 0.7566673
5: 19.89493
6: 0.9814708
我有一个非常庞大的宽格式数据table，这里只给你一个简短的例子：
library(maditr)
library(reshape2)
library(data.table)
dt.test <- data.table(time = as.Date('2009-01-01') + 0:9,
s1 = rnorm(10, 5, 1),
s2 = rnorm(10, 10, 2),
s3 = rnorm(10, 20, 4))
dt.test <- dt.test %>%
reshape2::melt(id.vars = "time", variable.name = "stock", value.name = "price") %>%
maditr::dcast(stock ~ time, fun.aggregate = list, value.var = "price")
> dt.test
# stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09 2009-01-10
#1: s1 5.139208 3.879785 6.099094 5.304787 4.732387 4.77054 4.877472 4.830193 5.468288 3.121548
#2: s2 11.70447 7.360979 10.5366 7.525067 9.770211 14.13924 8.356996 10.45796 9.749032 5.034887
#3: s3 28.17668 10.22208 29.17786 24.41746 17.87546 24.67163 13.13525 14.9941 18.84145 22.07169
现在我需要计算每一行的权重，这意味着：每个行条目除以整行的平均值。
现在两种结束格式对我来说都是可能的。要么权重相同 table，要么我们创建一个新的权重 table dt.weights 并将权重保存在那里。
版本 1：将它们保存在同一个 table dt.test
在行 s1 之后保存 s1 的权重行，称为 s1W，在行 s2 之后保存权重行 s2W 和在我的数据 table.
s3 之后的权重行 s3W
版本 2：将它们保存在新的 table dt.weights
这看起来应该和以前一样（宽幅面），但用权重替换库存：
# dt.weights
# stock_w 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09 2009-01-10
#1: s1 5.139208 3.879785 6.099094 5.304787 4.732387 4.77054 4.877472 4.830193 5.468288 3.121548
#2: s2 11.70447 7.360979 10.5366 7.525067 9.770211 14.13924 8.356996 10.45796 9.749032 5.034887
#3: s3 28.17668 10.22208 29.17786 24.41746 17.87546 24.67163 13.13525 14.9941 18.84145 22.07169
这怎么行？
dt.test 和 dt.weights 的宽格式不应更改。
编辑： 我的数据的一小部分：
structure(list(GPNRPlan = c(630882L, 773297L, 830234L), `2018-01-01-00` = list(
101.59, 211.02, 27.91), `2018-01-01-01` = list(98.32, 209.58,
20.98), `2018-01-01-02` = list(95.51, 205.74, 14.62), `2018-01-01-03` = list(
93.28, 210.62, 13.96), `2018-01-01-04` = list(93.03, 219.44,
13.39), `2018-01-01-05` = list(101.62, 269.02, 11.08), `2018-01-01-06` = list(
114.8, 275.1, 10.94), `2018-01-01-07` = list(114.08, 285.74,
11.17), `2018-01-01-08` = list(105.71, 299.54, 10.6), `2018-01-01-09` = list(
107.51, 316.7, 10.84), `2018-01-01-10` = list(112.17, 298.06,
10.85), `2018-01-01-11` = list(120.32, 259.44, 13.08), `2018-01-01-12` = list(
119.75, 255.66, 29.68), `2018-01-01-13` = list(115.29, 284.08,
51.07), `2018-01-01-14` = list(117.77, 249.38, 50.02), `2018-01-01-15` = list(
121.11, 266.22, 51.51), `2018-01-01-16` = list(131.71, 258.14,
54.03), `2018-01-01-17` = list(147.68, 260.28, 60.09), `2018-01-01-18` = list(
157.38, 258.74, 60.94), `2018-01-01-19` = list(161.6, 227.96,
64.69), `2018-01-01-20` = list(154, 214.5, 52.98), `2018-01-01-21` = list(
139.59, 218.96, 46.26), `2018-01-01-22` = list(121.03, 214.46,
31.93), `2018-01-01-23` = list(104.83, 211.48, 21.57), `2018-01-02-00` = list(
104.33, 208.66, 18.05), `2018-01-02-01` = list(96.83, 209.98,
13.68), `2018-01-02-02` = list(94.7, 214.64, 13.49), `2018-01-02-03` = list(
93.8, 211.1, 10.61), `2018-01-02-04` = list(93.73, 223.18,
10.56), `2018-01-02-05` = list(103.18, 290.62, 10.33), `2018-01-02-06` = list(
132.27, 302.42, 11.02), `2018-01-02-07` = list(345.6, 409.74,
14.39), `2018-01-02-08` = list(564.61, 402.22, 15.35), `2018-01-02-09` = list(
619.72, 426.02, 36.77), `2018-01-02-10` = list(626.2, 393.6,
43.88), `2018-01-02-11` = list(619.47, 383.42, 41.75), `2018-01-02-12` = list(
577.24, 338.1, 47.78), `2018-01-02-13` = list(495.96, 367.32,
46.86), `2018-01-02-14` = list(494.98, 349.86, 50.1), `2018-01-02-15` = list(
538.48, 339.86, 50.87), `2018-01-02-16` = list(524.3, 315.06,
53.49), `2018-01-02-17` = list(475.18, 307, 53.45), `2018-01-02-18` = list(
366, 294.9, 55.2), `2018-01-02-19` = list(225.15, 243.6,
57.24), `2018-01-02-20` = list(182.21, 234.46, 51.21), `2018-01-02-21` = list(
160.3, 234, 33.39), `2018-01-02-22` = list(136.96, 234.78,
24.73), `2018-01-02-23` = list(117.38, 230.74, 21.19), `2018-01-03-00` = list(
105.54, 231.48, 14.04), `2018-01-03-01` = list(100.61, 237.72,
15.86), `2018-01-03-02` = list(97.85, 230.2, 13.49), `2018-01-03-03` = list(
96.75, 240.52, 10.18), `2018-01-03-04` = list(97, 235.82,
10.28), `2018-01-03-05` = list(105.85, 301.14, 9.6), `2018-01-03-06` = list(
131.66, 317.92, 11.66), `2018-01-03-07` = list(342.72, 401.08,
13.97), `2018-01-03-08` = list(563.71, 421.58, 19.96), `2018-01-03-09` = list(
617.2, 417.5, 39.21), `2018-01-03-10` = list(626.69, 388.46,
37.58), `2018-01-03-11` = list(624.09, 379.5, 41.75), `2018-01-03-12` = list(
581.89, 353.32, 46.09), `2018-01-03-13` = list(500.86, 253.34,
45.87), `2018-01-03-14` = list(498.61, 317.54, 42.89), `2018-01-03-15` = list(
539.27, 351.52, 50.02), `2018-01-03-16` = list(527.84, 320.2,
46.89), `2018-01-03-17` = list(477.11, 296.58, 47.37), `2018-01-03-18` = list(
360.48, 287.28, 49.22), `2018-01-03-19` = list(224.44, 247.82,
52.01), `2018-01-03-20` = list(183.9, 242.1, 56.8)), row.names = c(NA,
-3L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001ff1d5e1ef0>, sorted = "GPNRPlan")
我得到的解决方案（每行两次权重）：
它不是立竿见影的，但似乎有效：
第一步：构建权重 table。
#Compute row means
aux <- rowMeans(dt.test[, lapply(.SD, unlist), .SDcols = 2:ncol(dt.test)])
#Compute weights and add a column with "row names"
cbind(stock = paste0(dt.test$stock, "W"),
dt.test[, lapply(.SD, function(x) unlist(x) / aux), .SDcols = 2:ncol(dt.test)]
)
stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09
1: s1W 0.9517776 0.9545703 1.041051 1.191171 1.32749 0.884822 0.8491691 1.15274 0.7940578
2: s2W 1.126405 0.838604 0.9497316 1.033589 1.160884 0.8274315 1.071979 0.9675088 1.267201
3: s3W 1.235231 0.7507189 1.044507 1.244315 0.9601964 0.909451 1.101159 0.9570893 0.8158607
2009-01-10
1: 0.853152
2: 0.7566673
3: 0.9814708
然后，如果您愿意，将所有内容绑定在一起排序：
aux <- rowMeans(dt.test[, lapply(.SD, unlist), .SDcols = 2:ncol(dt.test)])
rbind(dt.test,
cbind(stock = paste0(dt.test$stock, "W"),
dt.test[, lapply(.SD, function(x) unlist(x) / aux), .SDcols = 2:ncol(dt.test)]
)
)[order(as.character(stock))]
stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 2009-01-07 2009-01-08 2009-01-09
1: s1 4.770632 4.78463 5.2181 5.970551 6.65383 4.435028 4.256324 5.777923 3.980087
2: s1W 0.9517776 0.9545703 1.041051 1.191171 1.32749 0.884822 0.8491691 1.15274 0.7940578
3: s2 11.36418 8.460586 9.58174 10.42776 11.71203 8.347867 10.81508 9.761092 12.78465
4: s2W 1.126405 0.838604 0.9497316 1.033589 1.160884 0.8274315 1.071979 0.9675088 1.267201
5: s3 25.03878 15.21747 21.17271 25.22293 19.46369 18.43505 22.32109 19.40071 16.53793
6: s3W 1.235231 0.7507189 1.044507 1.244315 0.9601964 0.909451 1.101159 0.9570893 0.8158607
2009-01-10
1: 4.276287
2: 0.853152
3: 7.633934
4: 0.7566673
5: 19.89493
6: 0.9814708