将多个值列重塑为宽格式 [英] Reshape multiple value columns to wide format
本文介绍了将多个值列重塑为宽格式的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我具有以下数据框，并且我想使用强制转换来创建数据透视表"，其中包含两个值(值和百分比)的列. 这是数据帧:
I have the following data frame and i want to use cast to create a "pivot table" with columns for two values (value and percent). Here is the data frame:
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"
)
这就是我要创建的(当然，具有不同的标头名称，例如:[month] _value，[month] _percent):
This is what i would like to create (of course, with different header names like: [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
在单值列上使用强制转换时，我还遇到以下错误:它没有考虑值"参数.因此，即使我指定value ="percent"，它仍会显示"value"列中的值.
I also encountered the following error while using cast on a single value column: it does not take into account the "value" parameter. So, even if i specify value = "percent" it still displays the values from "value" column.
cast(expensesByMonth, expense_type ~ month, fun.aggregate = sum, value = "percent")
推荐答案
您最好的选择是使用melt将数据重塑为长格式，然后再转换为dcast:
Your best option is to reshape your data to long format, using melt, and then to dcast:
library(reshape2)
meltExpensesByMonth <- melt(expensesByMonth, id.vars=1:2)
dcast(meltExpensesByMonth, expense_type ~ month + variable, fun.aggregate = sum)
输出的前几行:
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
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