Ändern Sie mehrere Wertspalten in ein Wide-Format um

Ich habe den folgenden Datenrahmen und ich möchte Cast verwenden, um eine “Pivot-Tabelle” mit Spalten für zwei Werte (Wert und Prozent) zu erstellen. Hier ist der Datenrahmen:

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" ) 

Das möchte ich erstellen (natürlich mit verschiedenen Header-Namen wie: [Monat] _value, [Monat] _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 

Ich habe auch den folgenden Fehler bei der Verwendung von Cast für eine einzelne Wertspalte festgestellt: Der Parameter “value” wird nicht berücksichtigt. Also, auch wenn ich value = “percent” angabe, zeigt es immer noch die Werte aus der Spalte “value” an.

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

Solutions Collecting From Web of "Ändern Sie mehrere Wertspalten in ein Wide-Format um"

Ihre beste Option ist es, Ihre Daten in ein langes Format dcast , indem Sie ” melt und dann zu ” dcast :

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

Die ersten Zeilen der Ausgabe:

  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 

data.table kann mehrere value.var Variablen value.var . Dies ist ziemlich direkt (und effizient).

Deshalb:

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

Ich bevorzuge die tabulate function in den tables für dieses. Es erfordert Faktoren, aber das ist sowieso eine gute Idee mit der Art der Daten, die Sie haben.

 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) 

Teilleistung:

  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 

Da diese Frage oft besucht wird, verdient sie meines Erachtens auch eine vollständige Antwort auf die Antwort. Die Reshape-function von Base R ist recht vielseitig und kann problemlos auch auf dieses Problem angewendet werden:

 expenses < - reshape(expensesByMonth, idvar = 'expense_type', direction = 'wide', timevar = 'month', sep = '_') 

Die Zellen mit NA Werten können durch 0 mit:

 expenses[is.na(expenses)] < - 0 

was gibt (geordnet nach expense_type , um es einfacher zu machen, mit der gewünschten Ausgabe zu vergleichen):

 > expenses[order(expenses$expense_type),] expense_type value_2012-02-01 percent_2012-02-01 value_2012-03-01 percent_2012-03-01 value_2012-04-01 percent_2012-04-01 value_2012-05-01 percent_2012-05-01 value_2012-06-01 percent_2012-06-01 value_2012-07-01 percent_2012-07-01 1 Adjustment 442.37 0.124025031 2.00 0.0005064625 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 0.0506462461 200.00 0.043650205 200.00 0.049672410 200.00 0.049808859 0.00 0.000000000 3 Cable 21.33 0.005980184 36.33 0.0091998906 0.00 0.000000000 39.05 0.009698538 16.00 0.003984709 0.00 0.000000000 67 Charity 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 32.59 0.008116353 0.00 0.000000000 30 Clothes 0.00 0.000000000 0.00 0.0000000000 806.90 0.176106751 237.00 0.058861806 149.81 0.037309325 0.00 0.000000000 4 Clubbing 75.00 0.021027369 206.55 0.0523049107 324.81 0.070890115 40.00 0.009934482 0.00 0.000000000 0.00 0.000000000 32 Computer 0.00 0.000000000 0.00 0.0000000000 756.00 0.164997774 283.83 0.070492601 100.00 0.024904429 10.54 0.003417573 5 Dining 22.50 0.006308211 74.50 0.0188657267 80.50 0.017569207 141.32 0.035098525 80.00 0.019923543 0.00 0.000000000 6 Education 1800.00 0.504656861 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 60.00 0.014942658 0.00 0.000000000 52 Electric 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 32.88 0.008166144 31.91 0.007947003 0.00 0.000000000 7 Gifts 10.00 0.002803649 89.00 0.0225375795 100.00 0.021825102 30.00 0.007450862 55.00 0.013697436 10.00 0.003242479 8 Groceries 233.33 0.065417547 372.68 0.0943742150 398.37 0.086944660 424.40 0.105404855 397.25 0.098932845 342.11 0.110928451 9 Lunch 154.75 0.043386472 383.75 0.0971774847 326.25 0.071204396 412.00 0.102325166 486.40 0.121135144 291.00 0.094356141 37 Maintenance 0.00 0.000000000 0.00 0.0000000000 151.00 0.032955905 142.75 0.035453683 115.60 0.028789520 76.50 0.024804965 21 Medical Expenses 0.00 0.000000000 144.19 0.0365134111 29.95 0.006536618 86.55 0.021495736 47.08 0.011725005 66.80 0.021659760 22 Miscellaneous 0.00 0.000000000 508.11 0.1286693205 101.00 0.022043353 1051.50 0.261152698 1000.00 0.249044293 1008.00 0.326841890 10 Personal Care 30.00 0.008410948 30.00 0.0075969369 90.00 0.019642592 30.00 0.007450862 120.00 0.029885315 30.00 0.009727437 24 Phone 0.00 0.000000000 38.40 0.0097240793 38.45 0.008391752 38.90 0.009661284 41.11 0.010238211 41.11 0.013329831 25 Recreation 0.00 0.000000000 81.75 0.0207016531 61.00 0.013313312 51.50 0.012790646 256.00 0.063755339 316.00 0.102462339 11 Rent 545.00 0.152798883 1746.70 0.4423189903 743.75 0.162324199 749.70 0.186197031 761.60 0.189672133 765.00 0.248049649 95 Repair and Maintenance 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 65.00 0.021076114 12 Transportation 32.50 0.009111860 35.00 0.0088630931 129.00 0.028154382 35.00 0.008692672 55.00 0.013697436 62.00 0.020103370 45 Travel 0.00 0.000000000 0.00 0.0000000000 228.53 0.049876906 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 

Das könnte man auch mit dem tidyverse :

 library(dplyr) library(tidyr) expensesByMonth %>% gather(k, v, 3:4) %>% unite(km, k, month) %>% spread(km, v, fill = 0)