投射多个值列

我有以下数据框架，我想使用转换创build一个“数据透视表”与两个值（值和百分比）的列。 这里是数据框架：

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" ) 

这是我想创build（当然，不同的标题名称，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 [phone].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 

在单个值列上使用强制转换时，我也遇到以下错误：它不考虑“值”参数。 所以，即使我指定值=“百分比”，它仍然显示“值”列中的值。

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

Answer 1

您最好的select是将数据重塑为长格式，使用melt ，然后dcast ：

 library(reshape2) meltExpensesByMonth <- melt(expensesByMonth, id.vars=1:2) dcast(meltExpensesByMonth, expense_type ~ month + variable, fun.aggregate = sum) 

前几行输出：

  expense_type 2012-02-01_value 2012-02-01_percent 2012-03-01_value 2012-03-01_percent 1 Adjustment 442.37 0.124025031 2.00 0.0005064625 2 Bank Service Charge 200.00 0.056072985 200.00 0.0506462461 3 Cable 21.33 0.005980184 36.33 0.0091998906 4 Charity 0.00 0.000000000 0.00 0.0000000000 

Answer 2

data.table +可以在多个value.varvariables上value.var …因此这是相当直接的（而且是高效的）：

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

Answer 3

我更喜欢包tables的tabulatefunction。 它需要的因素，但无论如何这是一个好主意，你有数据的types。

 library(tables) expensesByMonth$month= as.factor(expensesByMonth$month) expensesByMonth$expense_type= as.factor(expensesByMonth$expense_type) tabular(expense_type~(month)*(value+percent)*(sum),data=expensesByMonth) # Optional formatting tabular(expense_type~month* ((Format(digits=1))*value+(Format(digits=3))*percent)*sum, data=expensesByMonth) 

部分产量：

  value percent value percent value percent expense_type sum sum sum sum sum sum Adjustment 442 0.124025 2 0.000506 16 0.003573 Bank Service Charge 200 0.056073 200 0.050646 200 0.043650 Cable 21 0.005980 36 0.009200 0 0.000000