将多个值列 reshape 为宽格式

Question

我有以下数据框，我想使用 cast 创建一个“数据透视表”，其中包含两个值(值和百分比)的列。
这是数据框:

expensesByMonth <- structure(list(month = c("2012-02-01", "2012-02-01", "2012-02-01", 
"2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", 
"2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-03-01", 
"2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", 
"2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", 
"2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-04-01", 
"2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", 
"2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", 
"2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", 
"2012-04-01", "2012-04-01", "2012-05-01", "2012-05-01", "2012-05-01", 
"2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", 
"2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", 
"2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", 
"2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", 
"2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", 
"2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", 
"2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", 
"2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", 
"2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", 
"2012-07-01", "2012-07-01", "2012-07-01"), 
expense_type = c("Adjustment", "Bank Service Charge", "Cable", "Clubbing", "Dining", "Education", 
"Gifts", "Groceries", "Lunch", "Personal Care", "Rent", "Transportation", 
"Adjustment", "Bank Service Charge", "Cable", "Clubbing", "Dining", 
"Gifts", "Groceries", "Lunch", "Medical Expenses", "Miscellaneous", 
"Personal Care", "Phone", "Recreation", "Rent", "Transportation", 
"Adjustment", "Bank Service Charge", "Clothes", "Clubbing", "Computer", 
"Dining", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", 
"Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", 
"Transportation", "Travel", "Bank Service Charge", "Cable", "Clothes", 
"Clubbing", "Computer", "Dining", "Electric", "Gifts", "Groceries", 
"Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", 
"Personal Care", "Phone", "Recreation", "Rent", "Transportation", 
"Adjustment", "Bank Service Charge", "Cable", "Charity", "Clothes", 
"Computer", "Dining", "Education", "Electric", "Gifts", "Groceries", 
"Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", 
"Personal Care", "Phone", "Recreation", "Rent", "Transportation", 
"Computer", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", 
"Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", 
"Repair and Maintenance", "Transportation"), 
value = c(442.37, 200, 21.33, 75, 22.5, 1800, 10, 233.33, 154.75, 30, 545, 32.5, 
2, 200, 36.33, 206.55, 74.5, 89, 372.68, 383.75, 144.19, 508.11, 
30, 38.4, 81.75, 1746.7, 35, 16.37, 200, 806.9, 324.81, 756, 
80.5, 100, 398.37, 326.25, 151, 29.95, 101, 90, 38.45, 61, 743.75, 
129, 228.53, 200, 39.05, 237, 40, 283.83, 141.32, 32.88, 30, 
424.4, 412, 142.75, 86.55, 1051.5, 30, 38.9, 51.5, 749.7, 35, 
10, 200, 16, 32.59, 149.81, 100, 80, 60, 31.91, 55, 397.25, 486.4, 
115.6, 47.08, 1000, 120, 41.11, 256, 761.6, 55, 10.54, 10, 342.11, 
291, 76.5, 66.8, 1008, 30, 41.11, 316, 765, 65, 62), 
percent = c(0.124025030980324, 0.0560729845967511, 0.00598018380724351, 0.0210273692237817, 
0.0063082107671345, 0.50465686137076, 0.00280364922983756, 0.0654175474797997, 
0.0433864718317362, 0.00841094768951267, 0.152798883026147, 0.00911185999697206, 
0.000506462461002391, 0.0506462461002391, 0.00919989060410842, 
0.0523049106600219, 0.018865726672339, 0.0225375795146064, 0.0943742149831854, 
0.0971774847048337, 0.0365134111259673, 0.128669320529962, 0.00759693691503586, 
0.0097240792512459, 0.0207016530934727, 0.442318990316438, 0.00886309306754183, 
0.00357276925628781, 0.0436502047194601, 0.176106750940662, 0.0708901149746392, 
0.164997773839559, 0.0175692073995827, 0.0218251023597301, 0.0869446602704567, 
0.0712043964486193, 0.0329559045631924, 0.00653661815673915, 
0.0220433533833274, 0.0196425921237571, 0.00839175185731621, 
0.0133133124394353, 0.162324198800492, 0.0281543820440518, 0.0498769064226911, 
0.0496724104530621, 0.00969853814096037, 0.0588618063868785, 
0.00993448209061241, 0.070492601294463, 0.0350985252261336, 0.0081661442784834, 
0.00745086156795931, 0.105404854981398, 0.102325165533308, 0.035453682960873, 
0.0214957356235626, 0.261152697956974, 0.00745086156795931, 0.00966128383312057, 
0.0127906456916635, 0.186197030583303, 0.00869267182928586, 0.00249044292527426, 
0.0498088585054852, 0.00398470868043882, 0.00811635349346881, 
0.0373093254635337, 0.0249044292527426, 0.0199235434021941, 0.0149426575516456, 
0.00794700337455016, 0.0136974360890084, 0.09893284520652, 0.12113514388534, 
0.0287895202161704, 0.0117250052921912, 0.249044292527426, 0.0298853151032911, 
0.0102382108658025, 0.0637553388870211, 0.189672133188888, 0.0136974360890084, 
0.00341757293956667, 0.0032424790697976, 0.110928451456846, 0.0943561409311103, 
0.0248049648839517, 0.021659760186248, 0.326841890235599, 0.00972743720939281, 
0.013329831455938, 0.102462338605604, 0.248049648839517, 0.0210761139536844, 
0.0201033702327451)), 
.Names = c("month", "expense_type", "value", "percent"), 
row.names = c(NA, -96L), 
class = "data.frame"
)

这就是我想要创建的(当然，使用不同的标题名称，例如:[month]_value、[month]_percent):

expenses   value     percent value.1   percent.1 value.2   percent.2 value.3   percent.3 value.4   percent.4 value.5   percent.5
1              Adjustment  442.37 0.124025031    2.00 0.000506462   16.37 0.003572769    0.00 0.000000000   10.00 0.002490443    0.00 0.000000000
2     Bank Service Charge  200.00 0.056072985  200.00 0.050646246  200.00 0.043650205  200.00 0.049672410  200.00 0.049808859    0.00 0.000000000
3                   Cable   21.33 0.005980184   36.33 0.009199891    0.00 0.000000000   39.05 0.009698538   16.00 0.003984709    0.00 0.000000000
4                 Charity    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000   32.59 0.008116353    0.00 0.000000000
5                 Clothes    0.00 0.000000000    0.00 0.000000000  806.90 0.176106751  237.00 0.058861806  149.81 0.037309325    0.00 0.000000000
6                Clubbing   75.00 0.021027369  206.55 0.052304911  324.81 0.070890115   40.00 0.009934482    0.00 0.000000000    0.00 0.000000000
7                Computer    0.00 0.000000000    0.00 0.000000000  756.00 0.164997774  283.83 0.070492601  100.00 0.024904429   10.54 0.003417573
8                  Dining   22.50 0.006308211   74.50 0.018865727   80.50 0.017569207  141.32 0.035098525   80.00 0.019923543    0.00 0.000000000
9               Education 1800.00 0.504656861    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000   60.00 0.014942658    0.00 0.000000000
10               Electric    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000   32.88 0.008166144   31.91 0.007947003    0.00 0.000000000
11                  Gifts   10.00 0.002803649   89.00 0.022537580  100.00 0.021825102   30.00 0.007450862   55.00 0.013697436   10.00 0.003242479
12              Groceries  233.33 0.065417547  372.68 0.094374215  398.37 0.086944660  424.40 0.105404855  397.25 0.098932845  342.11 0.110928451
13                  Lunch  154.75 0.043386472  383.75 0.097177485  326.25 0.071204396  412.00 0.102325166  486.40 0.121135144  291.00 0.094356141
14            Maintenance    0.00 0.000000000    0.00 0.000000000  151.00 0.032955905  142.75 0.035453683  115.60 0.028789520   76.50 0.024804965
15       Medical Expenses    0.00 0.000000000  144.19 0.036513411   29.95 0.006536618   86.55 0.021495736   47.08 0.011725005   66.80 0.021659760
16          Miscellaneous    0.00 0.000000000  508.11 0.128669321  101.00 0.022043353 1051.50 0.261152698 1000.00 0.249044293 1008.00 0.326841890
17          Personal Care   30.00 0.008410948   30.00 0.007596937   90.00 0.019642592   30.00 0.007450862  120.00 0.029885315   30.00 0.009727437
18                  Phone    0.00 0.000000000   38.40 0.009724079   38.45 0.008391752   38.90 0.009661284   41.11 0.010238211   41.11 0.013329831
19             Recreation    0.00 0.000000000   81.75 0.020701653   61.00 0.013313312   51.50 0.012790646  256.00 0.063755339  316.00 0.102462339
20                   Rent  545.00 0.152798883 1746.70 0.442318990  743.75 0.162324199  749.70 0.186197031  761.60 0.189672133  765.00 0.248049649
21 Repair and Maintenance    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000   65.00 0.021076114
22         Transportation   32.50 0.009111860   35.00 0.008863093  129.00 0.028154382   35.00 0.008692672   55.00 0.013697436   62.00 0.020103370
23                 Travel    0.00 0.000000000    0.00 0.000000000  228.53 0.049876906    0.00 0.000000000    0.00 0.000000000    0.00 0.000000000

我在单个值列上使用强制转换时也遇到了以下错误:它没有考虑“值”参数。所以，即使我指定 value = "percent"它仍然显示来自 "value"列的值。

cast(expensesByMonth, expense_type ~ month, fun.aggregate = sum, value = "percent")

Answer 1

data.table可以转换多个 value.var变量。这是非常直接的(和有效的)。

所以:

library(data.table) # v1.9.5+
dcast(setDT(expensesByMonth), expense_type ~ month, value.var = c("value", "percent"))